**Design, Implementation and Modeling of Flooding Disaster-Oriented USV**

Junfeng Xiong, Feng Gu, Decai Li, Yuqing He and Jianda Han

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64305

### **Abstract**

[23] Itseez. OpenCV [Internet]. Available from: http://www.opencv.org [Accessed:

[24] Burger, W. and Burge, M. Principles of Digital Image Processing—Fundamental

[26] Smereka M.. Duleba, I. Circular object detection using a modified hough transform. International Journal for Applied Mathematics and Computer Science. 2008; 18(1):85–

[27] Bradski G., Kaehler A. Learning OpenCV. 1st ed. Sebastopol, CA: O'Reilly Media; 2008.

[29] Gageik N., Benz P., Montenegro S. Obstacle detection and collision avoidance for a UAV with complementary low‐cost sensors. IEEE Access 3: 2015; 599–609.

[32] Rodos Framework, DLR Webpage, www.dlr.de/irs/desktopdefault.aspx/tabid‐

Techniques. 1st ed. London: Springer; 2009. 260 pp.

[25] Rhody H. Lecture 10: Hough Circle Transform [Internet], 2005.

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26 Recent Advances in Robotic Systems

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[30] Logitech, http://www.logitech.com.

5976/9736\_read‐19576/.

Although there exist some unmanned surface platforms, and parts of them have been applied in flooding disaster relief, the autonomy of these platforms is still so weak that most of them can only work under the control of operators. The primary reason is the difficulty of obtaining a dynamical model that is sufficient rich for model-based control and sufficient simple for model parameters identification. This makes them difficult to be used to achieve some high-performance autonomous control, such as robust control with respect to disturbances and unknown dynamics and trajectory tracking control in complicated and dynamical surroundings. In this chapter, a flooding disaster-orient‐ ed unmanned surface vehicle (USV) designed and implemented by Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) is introduced first, including the hardware and software structures. Then, we propose a quasi-linear parameter varying (qLPV) model to approach the dynamics of the USV system. We first apply this to solve a structured modeling problem and then introduce model error to solve an unstructured modeling problem. Subsequently, the qLPV model identification results are analyzed and the superiority compared to two linear models is demonstrated. At last, extensive application experiments, including rescuing rope throwing using an automatic pneumatic and water sampling in a 2.5 m radius circle, are described in detail to show the performance of course keeping control and GPS point tracking control based on the proposed model.

**Keywords:** unmanned surface vehicles, quasi-linear parameter varying system, active modeling, unscented Kalman filter

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Open source intelligence is an intelligence-gathering discipline that involves the collection, analysis, and interpretation of information from publicly available sources to produce "usable" intelligence. It can be distinguished from research since the former's intent is to create tailored or customized knowledge to support a particular decision or satisfy a specified information need by an individual or group. The sources of this information are now quite vast, and include media (newspapers, magazines, radio, TV, Internet), social networks (Facebook, Twitter, YouTube), public data (government reports, speeches), observation and reporting (plane spotters, satellite imagery), professional and academic (conferences, papers), and geospatial dimensions. The latter are often glossed over, but must be considered since not all open source data is text-based. These data come from various sources, including maps, spatial databases, commercial imagery, and the like. As information has become more available by virtue of the Internet and other digital media, the physical collection of information from open sources has become much easier.

### **5. Application of available technology**

Technology is now employed extensively by intelligence analysts to extract meaning from available information, to support the performance of a variety of analyses, and to aid in the communication of analytical results to the users of intelligence. The design of systems to support the intelligence process, and specifically intelligence analysis, can benefit from what we now know about the nature and role of critical thinking in this process. This knowledge of the specific skills required also supports the application of cognitive ergonomics to the development of training systems and methods that best meet analyst performance requirements. To be meaningful and realistic, training content and exercises must be developed and implemented within the context of available technology. Below, we summarize some of the technology that might be employed for extracting and analyzing information, the design of which can benefit from cognitive ergonomics that addresses specific critical thinking skills.

### **5.1 Extraction of entities, concepts, relationships and events**

Software applications are required to analyze, from any source of text data, and automatically extract many different entity types, such as people, dates, location, modes of transportation, facilities, measurements, currency figures, weapons, email addresses, and organizations. The extraction capability is extended to the detection and extraction of

increased globalization, communications flow, opportunities for terrorism) will continue to blur the line between personal security and national security, which in turn, will blur the line between law enforcement and military operations, and between activities involving

There is increasing awareness of the importance of intelligence, particularly that from open sources. A senior advisor to the Secretary of Defense recently stated that most information (perhaps as much as 90%) that matters now is available to anyone with an internet connection, that understanding and influencing foreign populations was very important, and that future enemies are unlikely to confront the world's overwhelming military power

Open source intelligence is an intelligence-gathering discipline that involves the collection, analysis, and interpretation of information from publicly available sources to produce "usable" intelligence. It can be distinguished from research since the former's intent is to create tailored or customized knowledge to support a particular decision or satisfy a specified information need by an individual or group. The sources of this information are now quite vast, and include media (newspapers, magazines, radio, TV, Internet), social networks (Facebook, Twitter, YouTube), public data (government reports, speeches), observation and reporting (plane spotters, satellite imagery), professional and academic (conferences, papers), and geospatial dimensions. The latter are often glossed over, but must be considered since not all open source data is text-based. These data come from various sources, including maps, spatial databases, commercial imagery, and the like. As information has become more available by virtue of the Internet and other digital media, the

Technology is now employed extensively by intelligence analysts to extract meaning from available information, to support the performance of a variety of analyses, and to aid in the communication of analytical results to the users of intelligence. The design of systems to support the intelligence process, and specifically intelligence analysis, can benefit from what we now know about the nature and role of critical thinking in this process. This knowledge of the specific skills required also supports the application of cognitive ergonomics to the development of training systems and methods that best meet analyst performance requirements. To be meaningful and realistic, training content and exercises must be developed and implemented within the context of available technology. Below, we summarize some of the technology that might be employed for extracting and analyzing information, the design of which can benefit from cognitive ergonomics that addresses

Software applications are required to analyze, from any source of text data, and automatically extract many different entity types, such as people, dates, location, modes of transportation, facilities, measurements, currency figures, weapons, email addresses, and organizations. The extraction capability is extended to the detection and extraction of

with conventional warfare, but with a technology-assisted insurgency (Packer, 2006).

physical collection of information from open sources has become much easier.

**5.1 Extraction of entities, concepts, relationships and events**

people and those involving territory (Fingar, 2006).

**5. Application of available technology**

specific critical thinking skills.

activities, events and relationships among these types of entities. Automatically extracting this information means that analysts do not have to read extensive amounts of text to pull out these types of information manually; they can focus sooner on the relevant information. Automated event and relationship extraction helps analysts more quickly discover associations, transactions, and action sequences that can be employed in the development of link, event and activity analyses. Therefore, assuming that this is done effectively, analysis can begin with information that has been automatically extracted and organized from much more voluminous amounts of information available to the analyst.

Information relevant to global operations might be in various languages other than English, such as Arabic, Chinese, Farsi and many others. Technology is available to support and augment the efforts of the limited number of translators typically available to exploit foreign language documents. Language processing software can help translators analyze documents in their native language and help them select the most relevant documents or sections of documents for translation. Available software might contain a suite of natural-language processing components that enable language and character encoding identification, paragraph and sentence analysis, stemming and decompounding, part-of-speech tagging, and noun phrase extraction. With such a system, analyst training can assume that the capabilities exist to provide the analyst with information that has been extracted and translated relatively effectively, by means of automated and human processing, from numerous different languages.

Software can also provide user-guided text extraction from unstructured data sources, supporting the transformation of user-identified text-based information into structured graphic formats for further analysis. The user can highlight important information contained in text documents—entities and associations among entities, for example—and easily put it in chart form to enhance visualization of the information without having to retype information. This type of conversion can be employed with a variety of text formats and applications.

### **5.2 Database development and query capabilities**

Technology can also help store, organize and query data extracted from multiple sources. Multi-user databases can now be built relatively quickly without the need for advanced, specialized technical expertise through the use of built-in forms and automated importation of information from data extraction tools and systems. Complex database query languages that previously had to be learned by analysts can now be replaced by simple, more intuitive, ways to query data, such as using graphics to "draw" questions. Some of the tasks that can be facilitated by currently available technology include the following:

Critical Thinking Skills for Intelligence Analysis 221

One valuable capability that can be provided by analytical software applications is data filtering. An important critical thinking strategy to counter the effects of complexity is that of determining specific analytic objectives and filtering out information in the database that is not relevant to meeting that objective. Examples of specific analytical objectives include the following: defining the flow of money into a specific organization; clarifying the span of control of a specific individual; including only information above a specified level of validity; tracking events that occurred only during a specified time period; and examining

The results from pursuing these specific objectives might provide support to a set of premises that lead to the development of an inference about the who, what, when, where, why and how of the activity of interest. Other capabilities provided by analytical software

 Switch between network and timeline views to identify patterns in both time and space. Automatically compare labels, types, attributes, names and aliases when combining

Augment charts by including visuals such as maps and photographs.

financial transactions above a specified amount during a specified time period.

Fig. 3. Sample data integration diagram

data from different sources.

include the following:

Geographic information system technology and services are available to augment database development and query capabilities. For example, required geographical information can be obtained through a web-based map interface (e.g., Google Maps, Google Earth, Ushahidi), providing access to geo-referenced infrastructure data. One existing system provides more than 1,300 layers of infrastructure data encompassing the physical, economic, socio-demographic, religious, health, educational, energy, military, transportation, political, governmental, geographical and chemical infrastructures of the United States. For example, some systems can provide the name, address, administrator contact information, number of beds and personnel for each hospital in the United States. Similar information can be provided for schools, fire stations, airports, and related facilities.

For the part-task training exercises and scenarios required to develop critical thinking skills, database development and query capabilities are not likely to be required of the trainee. However, the development of training exercises and scenarios, to be realistic, must be compatible with current and future database configurations, formats and capabilities. For this reason, the training developer must be knowledgeable about these and future systems and how they are likely to be employed in the intelligence process.

### **5.3 Data integration support**

Analytical software applications now serve to support the analysis function by providing tools that permit the analyst to convert information into a variety of formats, from multiple sources, into graphic products that lead to greater understanding of the information by both the analyst and the ultimate user of analytical products. This is the part of the intelligence process that is typically referred to as data integration. Significant advances have been made in the development and improvement of these systems; further enhancements can be made through the application of cognitive ergonomics, specifically through the application of our knowledge about the critical thinking skills that must be supported.

Analysts can uncover and interpret relationships and patterns hidden in data through the generation of intuitive charts. Moreover, information about each entity and link portrayed on a chart can be accessed through embedded data cards connected to the displayed icons or through links from icons back to the database. A sample chart is shown below in Figure 3. The mechanics for obtaining the additional information is typically just a matter of clicking on the icon of interest.

Maintain the quality of the database by searching a set, a query result or the entire

Create reports that can be printed, posted to a web page, or saved in a word-processing

Enable location-based database queries by interfacing with available geographical

 Interface with analytical software to provide the means for allowing the manual analysis of data and/or the automatic generation of charts, such as link diagrams, event

Geographic information system technology and services are available to augment database development and query capabilities. For example, required geographical information can be obtained through a web-based map interface (e.g., Google Maps, Google Earth, Ushahidi), providing access to geo-referenced infrastructure data. One existing system provides more than 1,300 layers of infrastructure data encompassing the physical, economic, socio-demographic, religious, health, educational, energy, military, transportation, political, governmental, geographical and chemical infrastructures of the United States. For example, some systems can provide the name, address, administrator contact information, number of beds and personnel for each hospital in the United States. Similar information can be provided for schools, fire stations, airports, and related

For the part-task training exercises and scenarios required to develop critical thinking skills, database development and query capabilities are not likely to be required of the trainee. However, the development of training exercises and scenarios, to be realistic, must be compatible with current and future database configurations, formats and capabilities. For this reason, the training developer must be knowledgeable about these and future systems

Analytical software applications now serve to support the analysis function by providing tools that permit the analyst to convert information into a variety of formats, from multiple sources, into graphic products that lead to greater understanding of the information by both the analyst and the ultimate user of analytical products. This is the part of the intelligence process that is typically referred to as data integration. Significant advances have been made in the development and improvement of these systems; further enhancements can be made through the application of cognitive ergonomics, specifically through the application of our knowledge about the critical thinking skills that must be

Analysts can uncover and interpret relationships and patterns hidden in data through the generation of intuitive charts. Moreover, information about each entity and link portrayed on a chart can be accessed through embedded data cards connected to the displayed icons or through links from icons back to the database. A sample chart is shown below in Figure 3. The mechanics for obtaining the additional information is typically just a matter of clicking

application to facilitate the communication of query results.

and how they are likely to be employed in the intelligence process.

timelines and financial transaction flow charts.

database for duplicate information.

mapping software.

**5.3 Data integration support**

facilities.

supported.

on the icon of interest.

One valuable capability that can be provided by analytical software applications is data filtering. An important critical thinking strategy to counter the effects of complexity is that of determining specific analytic objectives and filtering out information in the database that is not relevant to meeting that objective. Examples of specific analytical objectives include the following: defining the flow of money into a specific organization; clarifying the span of control of a specific individual; including only information above a specified level of validity; tracking events that occurred only during a specified time period; and examining financial transactions above a specified amount during a specified time period.

Fig. 3. Sample data integration diagram

The results from pursuing these specific objectives might provide support to a set of premises that lead to the development of an inference about the who, what, when, where, why and how of the activity of interest. Other capabilities provided by analytical software include the following:

Critical Thinking Skills for Intelligence Analysis 223

analyst must expend effort to envision the goal of the analysis and maintain that vision

Fig. 4. Critical thinking skills grouped into the intelligence analysis functions they support

Critical thinking is required to distinguish between relevant and irrelevant information, and valid and invalid information, relative to the desired end state, purpose or goal of the analysis. This skill is obviously related to envisioning the goal, because the analyst needs a well-defined goal before being able to determine what information is likely to be relevant in meeting that goal. The principal skill involved here is the assessment of information for its

**6.1.2 Assess and filter for relevance and validity**

during the analytical process.

### **6. Key critical thinking skills for intelligence analysis**

Harris (2011) reviewed the literature and identified 120 elements considered by researchers and educators as important for critical thinking. Like elements were grouped together. Two survey instruments were then developed based on the listing of 18 critical thinking skills and designed to identify those skills that would provide the highest training payoff. The first instrument was designed to collect data from a sample of 73 intelligence analysts at a software user's national conference in Washington DC following a 60-minute presentation on critical thinking. The second instrument employed a similar, expanded approach to collect data from six instructors who conduct intelligence analysis training and 14 students who had just completed a two-week course on intelligence analysis. Analyses of these data identified 11 critical thinking skills that appeared to have the highest payoff for intelligence analysis and mapped these skills to four specific intelligence analysis functions:

He then developed specifications for the development of web-based training on these skills, and developed and installed on-line prototype demonstrations of a critical thinking strategies overview module and a module for one of the 11 specific skills—consider valuecost-risk tradeoffs in seeking additional information. The 11 critical thinking skills are listed and mapped to intelligence analysis functions in Figure 4. A description of each skill is provided below, related to the intelligence analysis function it serves.

### **6.1 Assess and integrate information**

The three skills associated with this first function are: envision the goal (end state) of the analysis, assess and filter for relevance and validity, and extract the essential message. These skills are described in the paragraphs that follow.

### **6.1.1 Envision the goal (end state) of the analysis**

This skill is the ability to envision the desired goal (the desired end state of the analysis in terms of providing a useful inference that can be acted on with confidence in a timely manner) and to use that vision to guide and limit the analysis to tasks that will achieve the desired goal. This critical thinking skill constitutes an overall check on the process and products of thinking to ensure that it is moving the analysis forward along the right path.

There are many circumstances and reasons why an analyst might head down the wrong path, particularly early in an analysis. The directions given at the outset for conducting the analysis might be vague and confusing; the volume of information might be so great as to provide many opportunities to head in the wrong direction; and some types of information might be more compelling than others, even if not as helpful in meeting the analytical objectives. Consequently, particularly early in the data collection and integration efforts, the

Harris (2011) reviewed the literature and identified 120 elements considered by researchers and educators as important for critical thinking. Like elements were grouped together. Two survey instruments were then developed based on the listing of 18 critical thinking skills and designed to identify those skills that would provide the highest training payoff. The first instrument was designed to collect data from a sample of 73 intelligence analysts at a software user's national conference in Washington DC following a 60-minute presentation on critical thinking. The second instrument employed a similar, expanded approach to collect data from six instructors who conduct intelligence analysis training and 14 students who had just completed a two-week course on intelligence analysis. Analyses of these data identified 11 critical thinking skills that appeared to have the highest payoff for intelligence analysis and mapped these skills to four specific

He then developed specifications for the development of web-based training on these skills, and developed and installed on-line prototype demonstrations of a critical thinking strategies overview module and a module for one of the 11 specific skills—consider valuecost-risk tradeoffs in seeking additional information. The 11 critical thinking skills are listed and mapped to intelligence analysis functions in Figure 4. A description of each skill is

The three skills associated with this first function are: envision the goal (end state) of the analysis, assess and filter for relevance and validity, and extract the essential message. These

This skill is the ability to envision the desired goal (the desired end state of the analysis in terms of providing a useful inference that can be acted on with confidence in a timely manner) and to use that vision to guide and limit the analysis to tasks that will achieve the desired goal. This critical thinking skill constitutes an overall check on the process and products of thinking to ensure that it is moving the analysis forward along the right path.

There are many circumstances and reasons why an analyst might head down the wrong path, particularly early in an analysis. The directions given at the outset for conducting the analysis might be vague and confusing; the volume of information might be so great as to provide many opportunities to head in the wrong direction; and some types of information might be more compelling than others, even if not as helpful in meeting the analytical objectives. Consequently, particularly early in the data collection and integration efforts, the

provided below, related to the intelligence analysis function it serves.

**6. Key critical thinking skills for intelligence analysis**

intelligence analysis functions:

develop hypotheses, and

test hypotheses.

 assess and integrate information, organize information into premises,

**6.1 Assess and integrate information**

skills are described in the paragraphs that follow.

**6.1.1 Envision the goal (end state) of the analysis**

analyst must expend effort to envision the goal of the analysis and maintain that vision during the analytical process.

Fig. 4. Critical thinking skills grouped into the intelligence analysis functions they support

### **6.1.2 Assess and filter for relevance and validity**

Critical thinking is required to distinguish between relevant and irrelevant information, and valid and invalid information, relative to the desired end state, purpose or goal of the analysis. This skill is obviously related to envisioning the goal, because the analyst needs a well-defined goal before being able to determine what information is likely to be relevant in meeting that goal. The principal skill involved here is the assessment of information for its

Critical Thinking Skills for Intelligence Analysis 225

causes and effects that may be vital to understanding a situation, threat, process or set of events—who is sending suicide bombers into the crowded market places of the city, for example. This particular skill is one of recognizing patterns and relationships in the process

A critical task in the intelligence analysis process is the organization of information into premises—summarizing related items of information, results of data integration efforts, and/or information that answers a question into a summary statement that encompasses the central idea (premise) contained in the information. To complete this task successfully, the analyst must be able to recognize the patterns and relationships that serve as a logical basis

Information obtained for analysis may contain or be based on assumptions (ideas treated as facts but that are not yet supported by available evidence) that are not immediately obvious. On the other hand, the analyst might introduce, in the process of the analysis, assumptions that are mistakenly treated as evidence. Consequently, the analyst must have the capability to identify and challenge any and all assumptions, because they are very likely to be invalid

The tendency to overlook or accept assumptions in an analysis might be related to biases introduced into the process, such as certain mind sets and expectations, but they can also be a function of simply not being attentive to their possible existence. The need to challenge assumptions arises mainly while organizing information into premises. Premises should be based on the evidence at hand, an effort that can be defeated by the inclusion of ideas and beliefs based on conjecture. Therefore, as a part of the premise formulation process, there should be a conscious effort to identify, challenge, and remove information that cannot be supported by the evidence at hand. This is an important analytical effort because the

The skills associated with this function are: establish logical relationships; consider alternative perspectives; and counter biases, expectations, mind sets and oversimplification.

The application of inductive logic to a set of premises to develop one or more hypotheses is at the heart of the intelligence analysis process. The hypothesis is a tentative explanation, subject to further testing, of a situation, process, threat, or activity of interest. Developing useful hypotheses requires skill in applying logical reasoning to a set of premises that have

The critical aspect of this skill is that of organizing a set of premises into an argument that leads to an explanation that is based on the facts summarized in the premises, but that projects the explanation beyond these facts alone. That is, the analyst develops a

premises, once developed, provide the primary basis for hypothesis development.

These skills are described in the paragraphs that follow.

been developed from data organized and integrated for this purpose.

of building premises that will lead, ultimately, to the development of hypotheses.

for premise development.

**6.3 Develop hypotheses**

**6.3.1 Establish logical relationships**

or misleading.

**6.2.2 Challenge assumptions**

potential relevancy to the objectives of the analysis; once relevancy has been determined one must then assess validity to provide assurance that it will contribute positively to the analysis.

Assessing and filtering information contributes to intelligence analysis during the assessment and integration stage. If one of the objectives of the analysis is to determine the relationships among entities of various types (for example: individuals, organizations, places, and vehicles) the information most relevant to the analysis would be linkages among entities. For this objective, information that does not provide linkages would be considered not relevant. Thus, in addition to critical thinking skills, the analyst needs to understand and be proficient in the application of specific analytical techniques such as link analysis or financial profiling.

### **6.1.3 Extract the essential message**

Extracting the essential message is the ability to sort through the details of information and distinguish the essential from the non-essential. It also encompasses the ability to generate clear, concise statements that summarize the main point (the gist) of the information. The process is often automatic, because most people have extensive experience in attempting to get the main idea from what they read, see and hear. The automatic process usually works well if the amount of information is limited and the main points are stated clearly and unambiguously. However, critical thinking is needed when the information is extensive, is created in different formats and styles for different audiences, and the content has a high degree of complexity. The problem is further intensified when information is poorly presented with the main points not clearly discernable from the details.

The intelligence analyst typically deals with extensive amounts of information that is likely to be complex, is often ambiguous, may be prepared by someone from a different culture, and is not always presented clearly and simply. As a consequence, skill is required to extract the essential message from information and to summarize this message for future use in the analytical process. It is extremely useful to summarize a large amount of complex information with a simple statement so that the entire body of information need only be consulted subsequently to seek or verify specific details. Also, the gist serves as convenient shorthand to help communicate, is more easily remembered, and helps the analyst focus on the most important issues.

### **6.2 Organize information into premises**

The skills associated with this function are: recognize patterns and relationships, and challenge assumptions. These skills are described in the paragraphs that follow.

### **6.2.1 Recognize patterns and relationships**

An important function of intelligence analysis has been referred to in recent years as "connecting the dots" (Lahneman, 2006). While this expression is not very definitive, it does provide a general feeling for a skill that is important to the work of the analyst—recognizing and confirming patterns and relationships. A special aspect of this skill is establishing

potential relevancy to the objectives of the analysis; once relevancy has been determined one must then assess validity to provide assurance that it will contribute positively to the

Assessing and filtering information contributes to intelligence analysis during the assessment and integration stage. If one of the objectives of the analysis is to determine the relationships among entities of various types (for example: individuals, organizations, places, and vehicles) the information most relevant to the analysis would be linkages among entities. For this objective, information that does not provide linkages would be considered not relevant. Thus, in addition to critical thinking skills, the analyst needs to understand and be proficient in the application of specific analytical techniques such as link analysis or

Extracting the essential message is the ability to sort through the details of information and distinguish the essential from the non-essential. It also encompasses the ability to generate clear, concise statements that summarize the main point (the gist) of the information. The process is often automatic, because most people have extensive experience in attempting to get the main idea from what they read, see and hear. The automatic process usually works well if the amount of information is limited and the main points are stated clearly and unambiguously. However, critical thinking is needed when the information is extensive, is created in different formats and styles for different audiences, and the content has a high degree of complexity. The problem is further intensified when information is poorly

The intelligence analyst typically deals with extensive amounts of information that is likely to be complex, is often ambiguous, may be prepared by someone from a different culture, and is not always presented clearly and simply. As a consequence, skill is required to extract the essential message from information and to summarize this message for future use in the analytical process. It is extremely useful to summarize a large amount of complex information with a simple statement so that the entire body of information need only be consulted subsequently to seek or verify specific details. Also, the gist serves as convenient shorthand to help communicate, is more easily remembered, and helps the analyst focus on

The skills associated with this function are: recognize patterns and relationships, and

An important function of intelligence analysis has been referred to in recent years as "connecting the dots" (Lahneman, 2006). While this expression is not very definitive, it does provide a general feeling for a skill that is important to the work of the analyst—recognizing and confirming patterns and relationships. A special aspect of this skill is establishing

challenge assumptions. These skills are described in the paragraphs that follow.

presented with the main points not clearly discernable from the details.

analysis.

financial profiling.

the most important issues.

**6.2 Organize information into premises**

**6.2.1 Recognize patterns and relationships**

**6.1.3 Extract the essential message**

causes and effects that may be vital to understanding a situation, threat, process or set of events—who is sending suicide bombers into the crowded market places of the city, for example. This particular skill is one of recognizing patterns and relationships in the process of building premises that will lead, ultimately, to the development of hypotheses.

A critical task in the intelligence analysis process is the organization of information into premises—summarizing related items of information, results of data integration efforts, and/or information that answers a question into a summary statement that encompasses the central idea (premise) contained in the information. To complete this task successfully, the analyst must be able to recognize the patterns and relationships that serve as a logical basis for premise development.

### **6.2.2 Challenge assumptions**

Information obtained for analysis may contain or be based on assumptions (ideas treated as facts but that are not yet supported by available evidence) that are not immediately obvious. On the other hand, the analyst might introduce, in the process of the analysis, assumptions that are mistakenly treated as evidence. Consequently, the analyst must have the capability to identify and challenge any and all assumptions, because they are very likely to be invalid or misleading.

The tendency to overlook or accept assumptions in an analysis might be related to biases introduced into the process, such as certain mind sets and expectations, but they can also be a function of simply not being attentive to their possible existence. The need to challenge assumptions arises mainly while organizing information into premises. Premises should be based on the evidence at hand, an effort that can be defeated by the inclusion of ideas and beliefs based on conjecture. Therefore, as a part of the premise formulation process, there should be a conscious effort to identify, challenge, and remove information that cannot be supported by the evidence at hand. This is an important analytical effort because the premises, once developed, provide the primary basis for hypothesis development.

### **6.3 Develop hypotheses**

The skills associated with this function are: establish logical relationships; consider alternative perspectives; and counter biases, expectations, mind sets and oversimplification. These skills are described in the paragraphs that follow.

### **6.3.1 Establish logical relationships**

The application of inductive logic to a set of premises to develop one or more hypotheses is at the heart of the intelligence analysis process. The hypothesis is a tentative explanation, subject to further testing, of a situation, process, threat, or activity of interest. Developing useful hypotheses requires skill in applying logical reasoning to a set of premises that have been developed from data organized and integrated for this purpose.

The critical aspect of this skill is that of organizing a set of premises into an argument that leads to an explanation that is based on the facts summarized in the premises, but that projects the explanation beyond these facts alone. That is, the analyst develops a

Critical Thinking Skills for Intelligence Analysis 227

Testing hypotheses requires: considering value-cost-risk tradeoffs in seeking additional information, seeking disconfirming evidence, and assessing the strength of logical

A dilemma faced by intelligence analysts is whether to stop and report an inference based on available information, or to collect additional information. More information might produce an inference with greater usefulness at a higher level of confidence, but seeking additional information adds to intelligence costs and also risks a result that is not timely enough to be of value. This dilemma might be encountered early in the intelligence process or, more critically, later during the testing of hypotheses. This skill, then, is the ability to evaluate the need for

The analyst faces value-cost-risk tradeoffs principally during the stage of analysis in which hypotheses are being tested; this is a critical part of the process of developing a useful inference. Typically, one or more hypotheses would have been developed at this stage of the analysis and additional information might be required to help confirm or refute them. With limited time and resources available for collecting additional information, the analyst must employ these resources in the manner that will produce the greatest value for the resources expended. The analyst must also be sensitive to producing an inference in sufficient time

This skill is closely related to two skills addressed earlier—consider alternative perspectives and counter biases, expectations, mind sets, and oversimplification. Seeking disconfirming evidence is an important component of efforts taken to develop and test alternative competing hypotheses and is done in the face of biases that work to impede such efforts. A particularly important influence, confirmation bias, affects the development of alternative hypotheses by tending to prevent the analyst from seeking information other than what is

The skill, then, is the ability to seek disconfirming evidence, particularly in the testing of hypotheses, when the more natural inclination is to seek confirming evidence. This skill is applied to intelligence analysis mainly during the testing of hypotheses. Assuming that the analysis has been performed effectively to this point, the analyst has two or more alternative explanations for the information at hand; testing these alternatives requires the collection of additional information that will ultimately result in selecting the most valid or producing some composite that is the most valid. To overcome our built-in human tendency to seek confirming evidence, the analyst needs to learn the techniques and discipline of seeking

The development of a hypothesis from a set of premises is based on the logical relationship that exists between premises and hypothesis. The relationship is necessarily

relationships. These skills are described in the paragraphs that follow.

and at a high enough level of confidence for it to be of use.

disconfirming evidence during the hypothesis testing process.

**6.4.3 Assess the strength of logical relationships**

**6.4.2 Seek disconfirming evidence**

likely to confirm a favored explanation.

**6.4.1 Consider value-cost-risk tradeoffs in seeking additional information**

new information by considering the value, cost and risk tradeoffs that are involved.

**6.4 Test hypotheses**

hypothesis that fills in missing gaps to provide a more complete and more useful explanation. The set of hypotheses thus developed serve as the basis for guiding the collection of additional information to fill in the gaps with facts rather than conjecture. The establishment of logical relationships enables the intelligence analyst to link information to premises, premises to hypotheses, and hypotheses to inferences that can be acted on with confidence. The logical relationships are necessarily inductive in nature going from the specifics to the general, permitting discovery of what was previously unknown. It is the tightness of this logic that provides the necessary discipline for the ultimate development of useful, valid inferences.

### **6.3.2 Consider alternative perspectives**

This is the ability to develop explanations from different perspectives for the same information. An important component of this ability is to set aside one's own inclinations, values, beliefs, expectations, and preferences so as to develop explanations that cover the full range of possibilities. Some aspects of this skill have been called divergent thinking generating different ideas about a topic from available information or knowledge. But while divergent thinking is characterized by spontaneous, free-flowing, unorganized idea generation, this skill requires the development of explanations from the deliberate consideration of a set of premises that have been systematically derived from available information.

Intelligence analysis relies on the development of alternative competing hypotheses. After a set of premises has been derived from information determined to be relevant and valid, alternative hypotheses are developed that define the full range of possible explanations for the information. This process requires the critical thinking skill of considering alternative perspectives. The resulting alternative hypotheses, then, serve to guide collection of the additional information needed to formulate a useful inference.

### **6.3.3 Counter biases, expectations, mind sets and oversimplification**

Analysts are subject to the same biases, expectations, mindsets and oversimplifications that affect the thinking of all humans. While these negative influences might have limited impact on the lives that most of us live, they can be devastating to the work of the intelligence analyst. Consequently, analysts must develop the ability to understand and recognize the possible effects of these influences and to develop skills to keep them from distorting the products of analysis.

This skill involves the ability to continuously reevaluate one's view of the situation for these types of negative influences and to take the appropriate steps to eliminate them from the analysis. Although the types of influences addressed in this skill can enter the intelligence analysis process anywhere along the line, the primary concern is their role in hypothesis development and testing. Prior to this point, the tests for relevancy and validity should help assure the analyst that cognitive biases have had only a limited opportunity to enter the process. Now, as the analyst moves from strictly factual information to using conjecture in developing the most encompassing and useful hypotheses possible, these opportunities for distortion can operate most freely.

### **6.4 Test hypotheses**

226 Ergonomics – A Systems Approach

hypothesis that fills in missing gaps to provide a more complete and more useful explanation. The set of hypotheses thus developed serve as the basis for guiding the collection of additional information to fill in the gaps with facts rather than conjecture. The establishment of logical relationships enables the intelligence analyst to link information to premises, premises to hypotheses, and hypotheses to inferences that can be acted on with confidence. The logical relationships are necessarily inductive in nature going from the specifics to the general, permitting discovery of what was previously unknown. It is the tightness of this logic that provides the necessary discipline for the

This is the ability to develop explanations from different perspectives for the same information. An important component of this ability is to set aside one's own inclinations, values, beliefs, expectations, and preferences so as to develop explanations that cover the full range of possibilities. Some aspects of this skill have been called divergent thinking generating different ideas about a topic from available information or knowledge. But while divergent thinking is characterized by spontaneous, free-flowing, unorganized idea generation, this skill requires the development of explanations from the deliberate consideration of a set of premises that have been systematically derived from available

Intelligence analysis relies on the development of alternative competing hypotheses. After a set of premises has been derived from information determined to be relevant and valid, alternative hypotheses are developed that define the full range of possible explanations for the information. This process requires the critical thinking skill of considering alternative perspectives. The resulting alternative hypotheses, then, serve to guide collection of the

Analysts are subject to the same biases, expectations, mindsets and oversimplifications that affect the thinking of all humans. While these negative influences might have limited impact on the lives that most of us live, they can be devastating to the work of the intelligence analyst. Consequently, analysts must develop the ability to understand and recognize the possible effects of these influences and to develop skills to keep them from distorting the

This skill involves the ability to continuously reevaluate one's view of the situation for these types of negative influences and to take the appropriate steps to eliminate them from the analysis. Although the types of influences addressed in this skill can enter the intelligence analysis process anywhere along the line, the primary concern is their role in hypothesis development and testing. Prior to this point, the tests for relevancy and validity should help assure the analyst that cognitive biases have had only a limited opportunity to enter the process. Now, as the analyst moves from strictly factual information to using conjecture in developing the most encompassing and useful hypotheses possible, these opportunities for

ultimate development of useful, valid inferences.

additional information needed to formulate a useful inference.

**6.3.3 Counter biases, expectations, mind sets and oversimplification**

**6.3.2 Consider alternative perspectives**

information.

products of analysis.

distortion can operate most freely.

Testing hypotheses requires: considering value-cost-risk tradeoffs in seeking additional information, seeking disconfirming evidence, and assessing the strength of logical relationships. These skills are described in the paragraphs that follow.

### **6.4.1 Consider value-cost-risk tradeoffs in seeking additional information**

A dilemma faced by intelligence analysts is whether to stop and report an inference based on available information, or to collect additional information. More information might produce an inference with greater usefulness at a higher level of confidence, but seeking additional information adds to intelligence costs and also risks a result that is not timely enough to be of value. This dilemma might be encountered early in the intelligence process or, more critically, later during the testing of hypotheses. This skill, then, is the ability to evaluate the need for new information by considering the value, cost and risk tradeoffs that are involved.

The analyst faces value-cost-risk tradeoffs principally during the stage of analysis in which hypotheses are being tested; this is a critical part of the process of developing a useful inference. Typically, one or more hypotheses would have been developed at this stage of the analysis and additional information might be required to help confirm or refute them. With limited time and resources available for collecting additional information, the analyst must employ these resources in the manner that will produce the greatest value for the resources expended. The analyst must also be sensitive to producing an inference in sufficient time and at a high enough level of confidence for it to be of use.

### **6.4.2 Seek disconfirming evidence**

This skill is closely related to two skills addressed earlier—consider alternative perspectives and counter biases, expectations, mind sets, and oversimplification. Seeking disconfirming evidence is an important component of efforts taken to develop and test alternative competing hypotheses and is done in the face of biases that work to impede such efforts. A particularly important influence, confirmation bias, affects the development of alternative hypotheses by tending to prevent the analyst from seeking information other than what is likely to confirm a favored explanation.

The skill, then, is the ability to seek disconfirming evidence, particularly in the testing of hypotheses, when the more natural inclination is to seek confirming evidence. This skill is applied to intelligence analysis mainly during the testing of hypotheses. Assuming that the analysis has been performed effectively to this point, the analyst has two or more alternative explanations for the information at hand; testing these alternatives requires the collection of additional information that will ultimately result in selecting the most valid or producing some composite that is the most valid. To overcome our built-in human tendency to seek confirming evidence, the analyst needs to learn the techniques and discipline of seeking disconfirming evidence during the hypothesis testing process.

### **6.4.3 Assess the strength of logical relationships**

The development of a hypothesis from a set of premises is based on the logical relationship that exists between premises and hypothesis. The relationship is necessarily

Critical Thinking Skills for Intelligence Analysis 229

cally studied since the 1950s and are better understood. It incorporates many ideas offered by leading thinkers in philosophy and education. It also embodies many of the variables discussed in the relevant literature (e.g., predisposing attitudes, experience, knowledge, and skills) and specifies the relationships among them. The model can, and has been, used to make testable predictions about the factors that influence critical thinking and about the associated psychological consequences. It also offers practical guidance to the

The model is based on the most recent versions of heuristic theory, the foundation of which is that two cognitive systems are used to make judgments. System 1, based on intuition, is a quick, automatic, implicit process that employs associational strengths to arrive at solutions automatically. System 2 is effortful, conscious, and deliberately controlled. The two systems run in parallel and work together, capitalizing on each other's strengths and compensating for their weaknesses. For example, one function of System 2, the controlled deliberate process, is to monitor the products of the automatic process, making adjustments to correct or block the judgment of System 1. If no intuitive response is accessible, System 2 will be the primary processing system used to arrive at a

Technology can now be employed extensively by intelligence analysts to extract meaning from available information, to support the performance of a variety of analyses, and to aid in the communication of analytical results to the users of intelligence. The design of future systems to support the intelligence process can benefit from cognitive ergonomics, specifically from what we now know about the nature and role of critical thinking. Moreover, findings about specific critical thinking skills can support the development of

Research and experience to date in training and applying intelligence analysis skills suggest that the principal challenges that affect critical thinking are human limitations. Humans are limited in their capabilities to address complexity, by the biases they bring to the process, by their difficulties in handling uncertainty and, often, by the lack of relevant domain expertise. These limitations must be overcome by appropriately designed training systems and

Recent research has identified the 11 critical thinking skills that are most important for successful intelligence analysis. They are presented below as they relate to the principal

 *Envision the end state of the analysis* and use that vision to guide and limit the analysis to those tasks most likely to attain the desired goal, checking on the process and products

*Assess and filter for relevance and validity*, examining information for its potential

 *Extract the essential message* by sorting through the details of information to distinguish the essential from the non-essential, and by generating clear, concise statements

training systems and methods that best meet analyst performance requirements.

development of training for critical thinking skills.

judgment.

methods.

intelligence function they serve. Assess and Integrate Information

to ensure movement along the right path.

summarizing the main points.

contribution to the objectives of the analysis.

one of inductive logic, in which the argument proceeds from the specifics (the premises) to the general (the hypothesis). The strength of the relationship depends on the extent of conjecture involved in making the jump from the facts as summarized in the premises and the hypothesis that goes beyond the premises to provide a more useful explanation. More conjecture leads to weaker relationships; less conjecture leads to stronger relationships. The most meaningful way to assess and convey the strength of this logical relationship is to provide a numerical probability estimate of the confidence one can have that the hypothesis or inference is true.

The critical thinking skill is that of assessing the strength of these relationships in a manner that provides a numerical probability of the validity of hypotheses and inferences. Critical thinking is required because the process is a subjective one—subjective conditional probability—calling for a careful and deliberate assessment. The process is necessarily subjective (and consequently requires critical thinking) because the analyst will hardly ever have the type of statistical evidence needed to provide a simple objective calculation of probability (one that does not require critical thinking). In applying subjective conditional probability, the analyst must answer the following question: Given this specific set of premises (the conditions), what is the probability that the hypothesis (or inference) is true?

As stated earlier in this paper, the objective of intelligence analysis is to develop inferences that can be acted on with confidence. For the product of intelligence analysis to be complete, therefore, it must produce an inference that provides the needed explanation and, also, an estimate of the level of confidence that the user can have in that inference. The goal is to provide the greatest level of detail at the highest level of confidence. However, this usually results in a tradeoff—greater detail typically comes at a lower level of confidence. Conversely, the analyst can provide a higher level of confidence but with less detail. Providing confidence assessments enables the analyst to best meet the needs of the user more detail at lower confidence or less detail at higher confidence. To provide such estimates, the analyst must be capable of generating and communicating subjective conditional probability estimates.

### **7. Conclusions**

In the last couple of decades a number of useful tools have been developed to support the intelligence process, encompassing the functions of data collection, evaluation, collation and integration. However, intelligence analysis remains highly dependent on the cognitive capabilities, specifically the critical thinking skills, of the human analyst. For this reason, it is important for the success of the process to understand the inherent capabilities and limitations of the analyst and, in particular, the challenges that must be overcome through the application of cognitive ergonomics to the design of analysis systems and in the training of critical thinking skills.

To better understand critical thinking and the efforts required to maximize its effectiveness, a model was developed that is sufficiently specific to enhance understanding and to permit empirical testing. The model identifies the role of critical thinking within the related fields of reasoning and judgment, which have been empiri-

one of inductive logic, in which the argument proceeds from the specifics (the premises) to the general (the hypothesis). The strength of the relationship depends on the extent of conjecture involved in making the jump from the facts as summarized in the premises and the hypothesis that goes beyond the premises to provide a more useful explanation. More conjecture leads to weaker relationships; less conjecture leads to stronger relationships. The most meaningful way to assess and convey the strength of this logical relationship is to provide a numerical probability estimate of the confidence one can have that the

The critical thinking skill is that of assessing the strength of these relationships in a manner that provides a numerical probability of the validity of hypotheses and inferences. Critical thinking is required because the process is a subjective one—subjective conditional probability—calling for a careful and deliberate assessment. The process is necessarily subjective (and consequently requires critical thinking) because the analyst will hardly ever have the type of statistical evidence needed to provide a simple objective calculation of probability (one that does not require critical thinking). In applying subjective conditional probability, the analyst must answer the following question: Given this specific set of premises (the conditions), what is the probability that the hypothesis

As stated earlier in this paper, the objective of intelligence analysis is to develop inferences that can be acted on with confidence. For the product of intelligence analysis to be complete, therefore, it must produce an inference that provides the needed explanation and, also, an estimate of the level of confidence that the user can have in that inference. The goal is to provide the greatest level of detail at the highest level of confidence. However, this usually results in a tradeoff—greater detail typically comes at a lower level of confidence. Conversely, the analyst can provide a higher level of confidence but with less detail. Providing confidence assessments enables the analyst to best meet the needs of the user more detail at lower confidence or less detail at higher confidence. To provide such estimates, the analyst must be capable of generating and communicating subjective

In the last couple of decades a number of useful tools have been developed to support the intelligence process, encompassing the functions of data collection, evaluation, collation and integration. However, intelligence analysis remains highly dependent on the cognitive capabilities, specifically the critical thinking skills, of the human analyst. For this reason, it is important for the success of the process to understand the inherent capabilities and limitations of the analyst and, in particular, the challenges that must be overcome through the application of cognitive ergonomics to the design of analysis systems and in the training

To better understand critical thinking and the efforts required to maximize its effectiveness, a model was developed that is sufficiently specific to enhance understanding and to permit empirical testing. The model identifies the role of critical thinking within the related fields of reasoning and judgment, which have been empiri-

hypothesis or inference is true.

(or inference) is true?

conditional probability estimates.

**7. Conclusions**

of critical thinking skills.

cally studied since the 1950s and are better understood. It incorporates many ideas offered by leading thinkers in philosophy and education. It also embodies many of the variables discussed in the relevant literature (e.g., predisposing attitudes, experience, knowledge, and skills) and specifies the relationships among them. The model can, and has been, used to make testable predictions about the factors that influence critical thinking and about the associated psychological consequences. It also offers practical guidance to the development of training for critical thinking skills.

The model is based on the most recent versions of heuristic theory, the foundation of which is that two cognitive systems are used to make judgments. System 1, based on intuition, is a quick, automatic, implicit process that employs associational strengths to arrive at solutions automatically. System 2 is effortful, conscious, and deliberately controlled. The two systems run in parallel and work together, capitalizing on each other's strengths and compensating for their weaknesses. For example, one function of System 2, the controlled deliberate process, is to monitor the products of the automatic process, making adjustments to correct or block the judgment of System 1. If no intuitive response is accessible, System 2 will be the primary processing system used to arrive at a judgment.

Technology can now be employed extensively by intelligence analysts to extract meaning from available information, to support the performance of a variety of analyses, and to aid in the communication of analytical results to the users of intelligence. The design of future systems to support the intelligence process can benefit from cognitive ergonomics, specifically from what we now know about the nature and role of critical thinking. Moreover, findings about specific critical thinking skills can support the development of training systems and methods that best meet analyst performance requirements.

Research and experience to date in training and applying intelligence analysis skills suggest that the principal challenges that affect critical thinking are human limitations. Humans are limited in their capabilities to address complexity, by the biases they bring to the process, by their difficulties in handling uncertainty and, often, by the lack of relevant domain expertise. These limitations must be overcome by appropriately designed training systems and methods.

Recent research has identified the 11 critical thinking skills that are most important for successful intelligence analysis. They are presented below as they relate to the principal intelligence function they serve.

Assess and Integrate Information

Critical Thinking Skills for Intelligence Analysis 231

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### Develop Hypotheses

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### **8. References**

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### *Edited by Isabel L. Nunes*

This book covers multiple topics of Ergonomics following a systems approach, analysing the relationships between workers and their work environment from different but complementary standpoints. The chapters focused on Physical Ergonomics address the topics upper and lower limbs as well as low back musculoskeletal disorders and some methodologies and tools that can be used to tackle them. The organizational aspects of work are the subject of a chapter that discusses how dynamic, flexible and reconfigurable assembly systems can adequately respond to changes in the market. The chapters focused on Human-Computer Interaction discuss the topics of Usability, User-Centred Design and User Experience Design presenting framework concepts for the usability engineering life cycle aiming to improve the usersystem interaction, for instance of automated control systems. Cognitive Ergonomics is addressed in the book discussing the critical thinking skills and how people engage in cognitive work.

Ergonomics - A Systems Approach

Ergonomics

A Systems Approach

*Edited by Isabel L. Nunes*

Photo by bluebeat76 / iStock


**3. Conclusions**

The ionic lamellar coordination polymer based on a flexible triphosphonic acid linker, [Gd(H4nmp)(H2O)2]Cl2 H2O (H6nmp stands for fornitrilo(trimethylphosphonic) acid) presented a high catalytic efficiency to oxidize the most refractory sulfur and nitrogen compounds present in real diesel (mainly dibenzothiophene derivative, indole, and quinolone). The different methods followed for its preparation (microwave, one-pot, hydrothermal) originated some morphological differences, as the size and shape of obtained particles; however, this did not influence its catalytic performance. Using the model diesel/H2O2/[BMIM]PF6 system, complete desulfurization and denitrogenation were achieved after 2 h of reaction using sulfur or nitrogen model diesel, respectively. The ionic liquid [BMIM]PF6 was the extraction solvent selected since its efficiency was higher than MeCN and other more hydrophilic ionic liquids. When the single model diesel was replaced by the multicomponent S/N model diesel, the desulfurization efficiency decreases from 100% to 88% after 2 h, while the denitrogenation effectivity was maintained. The initial extraction (before oxidant addition) for sulfur and nitrogen was maintained when single model diesels were replaced by the multicomponent diesel. However, the extraction of nitrogen compounds is higher (86%) than the sulfur compounds (36%), what contribute largely for the higher efficiency of denitrogenation process. The recycle capacity of the lamellar catalyst was studied for consecutive desulfurization processes and the catalytic efficiency was maintained between cycles. This result indicates that [Gd(H4nmp)(H2O)2]Cl2 H2O is a stable catalyst, although some structural adjustment occurred to form the active heterogeneous catalyst. An improvement in the reuse capacity of the diesel/H2O2/[BMIM]PF6 system need to be performed in the near future, since the desulfurization process catalyzed by the lamellar material loss efficiency in consecutive ECODS cycles, probably caused by the saturation of the extraction [BMIM]PF6 phase with sulfur and nitrogen compounds.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4344/10/7/731/s1, Figure S1: The representative sulfur and nitrogen compounds used in this work to prepare model diesels. 1-benzothiophene (1-BT), dibenzothiophene (DBT), 4-methyldibenzothiophene (4-MDBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT), Figure S2: Powder X-ray diffraction and SEM images of [Gd(H4nmp)(H2O)2]Cl·2H2O (1) obtained using different experimental methods (op—one-pot; mw—Microwave Assisted; ht—Hydrothermal), Figure S3: Desulfurization of a multicomponent model diesel (2350 ppm S) catalyzed by layered MOF [Gd(H4nmp)(H2O)2]Cl·2H2O (20 mg), using 0.43 mmol of H2O2 oxidant and different volume of [BMIM]PF6 extraction solvent (1:1 and 1:0.5 model diesel/[BMIM]PF6), at 70 ◦C. The vertical dashed line indicates the instant that oxidative catalytic reaction was started by addition of oxidant, Figure S4: Denitrogenation and desulfurization profile of a model diesel containing approximately 400 ppm N and 2200 ppm of S, catalyzed by layered MOF [Gd(H4nmp)(H2O)2]Cl·2H2O (20 mg), 0.43 mmol of H2O2 oxidant and 1:0.5 model diesel/[BMIM]PF6, at 70 ◦C. The vertical dashed line indicates the instant that oxidative catalytic reaction was started by addition of oxidant, Figure S5: SEM, mapping and EDS spectra of compound [Gd(H4nmp)(H2O)2]Cl·2H2O (1) after catalytic use for one cycle of ECODS, with a P:Gd ratio of 3:1, Figure S6: FT-IR spectra of layered [Gd(H4nmp)(H2O)2]Cl·2H2O before and after catalytic use for one ECODS cycle.

**Author Contributions:** Conceptualization, F.A.A.P and S.S.B.; Data curation, F.M. and R.F.M.; Formal analysis, F.M. and R.F.M.; Funding acquisition, S.S.B.; Investigation, F.M.; Project administration, S.S.B.; Supervision, S.S.B. and F.A.A.P.; Validation, F.A.A.P. and R.F.M.; Visualization, F.M.; Writing—original draft, S.S.B.; Writing—review & editing, F.A.A.P and S.S.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was partly funded through the project REQUIMTE-LAQV (Ref. UID/QUI/50006/2019) and the project GlyGold, PTDC/CTM-CTM/31983/2017, and partially developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, LAQV-REQUIMTE (UIDB/50006/2020) and CQE (UIDB/00100/2020) research units, financed by national funds through the FCT/MCTES (Fundação para a Ciência e a Tecnologia / Ministério da Ciência, Tecnologia e Ensino Superior) and when appropriate co-financed by FEDER (Fundo Europeu de Desenvolvimento Regional) under the PT2020 Partnership Agreement. FCT is also gratefully acknowledged for the Junior Research Position CEECIND/00553/2017 (to RFM).

**Conflicts of Interest:** The authors declare no conflict of interest.


**High Temperature Hyperthermia in Breast Cancer Treatment**

Mario Francisco Jesús Cepeda Rubio, Arturo Vera Hernández and Lorenzo Leija Salas

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53277


**Abstract**

The Dendritic Cell Algorithm (DCA) is a bioinspired, population-based, supervised binary classifier, designed for anomaly detection in communication networks. The proposed model is inspired by the behavior of Dendritic Cells and Danger Theory. The main contribution of this research addresses two contemporary challenges of Network-based Intrusion Detection Systems, namely feature selection and generalization capabilities to improve classification performance. Feature selection improvement is achieved by using information gain and mutual information. A Decision Tree model is incorporated as a classification mechanism in order to improve accuracy, as a substitute to the classification threshold of the DCA. The proposed model is assessed using two publicly available datasets, namely UNSW-NB15 and NSL-KDD. Experimental results are compared against state of the art bioinspired and machine learning approaches for binary classification. The proposed approach provides competitive results when compared to other state of the art approaches, such as Support Vector Machines, and Artificial Neural Networks, achieving a 97.25 and 93.28% accuracy for the UNSW-NB15 and NSL-KDD datasets, respectively. Future challenges include multi-class classification, further performance improvements, and online detection.

**Keywords:** Anomaly detection, Dendritic Cell Algorithm, Decision Tree, binary classifier, Danger Theory, Artificial Immune System