In this paper we examine the effect of the use of a Computer Algebra System ( CAS ) to solve interdisciplinary problems . We have been working for some years in the application of mathematics to different engineering problems , the main reason was to improve the motivation of our students and to make possible for them to connect and apply what they learn in Linear Algebra class to other engineering subjects . We describe a case study with students of Electromechanical and Mechanical Engineering from the Coimbra Institute of Engineering and from Geological and Industrial Engineering students from the University of Salamanca in a Linear Algebra course . Abstract . The Lagrangian Meshless Smoothed Particle Hydrodynamics ( SPH ) method is na alternative to the traditional mesh Eulerian methods for solving engineering problems . Although it began to be used a few decades ago , SPH method has advantages compared to methods with meshes , in the numerical solution of classical problems studied in engineering courses . The project includes an engineering education research component , which is focused on evaluating the effectiveness of the curriculum in enhancing students ' knowledge , skills , and motivation in cybersecurity concepts . The first year of the project has been completed by introducing cybersecurity modules into four courses ( CS : Introduction to Software Design , and Software Design and Data Structures , and CPE : Engineering Problem Solving with C++ , and Data Structures and Algorithms ) impacting ~1600 students . The details of curriculum development , implementation and , preliminary findings of the research will be presented . We focus on representing expert knowledge , and examine how control knowledge provides the structure for using domain knowledge . To demonstrate our architecture for engineering problem solving , we present a Thermodynamics Problem Solver ( TPS ) that uses the IPSA architecture . TPS solves over 150 thermodynamics problems taken from the first four chapters of a common thermodynamics textbook and produces expert-like solutions . Data shows Problem Solving Skills is the second most important non technical skill sought by employers ( To et al. , 2019 ) ; The studies show that the problems can not be solved by using the same kind of thinking approach applied at the moment it was created . Therefore , a systematic analytical skill is required to handle the engineering related problems happening at manufacturing environment or engineering workplace . The purpose of this paper is to analyze the existing literature about Problem Solving skills for graduate engineers through a systematic literature review . It presents a unified approach to moment methods by employing the concepts of linear spaces and functional analysis . Written especially for those who have a minimal amount of experience in electromagnetic theory , this book illustrates theoretical and mathematical concepts to prepare all readers with the skills they need to apply the method of moments to new , engineering-related problems . Written especially for those who have a minimal amount of experience in electromagnetic theory , theoretical and mathematical concepts are illustrated by examples that prepare all readers with the skills they need to apply the method of moments to new , engineering-related problems . " Comparisons of responses preand post-semester , as well as comparisons across courses , will help determine impacts of the workshop innovation . We expect that students ’ experiences in the workshops will improve their general abilities to apply the mathematics they have learned to engineering-related problems , and will have positive effects on their self-efficacy perceptions related to succeeding in the engineering curriculum . Our findings will help determine whether positive impacts on student skill and self-efficacy are indeed occurring in ways we are able to detect with the use of these instruments . Background and Theoretical Framework First established at Purdue University in 1995 , the EPICS program aimed to integrate engineering undergraduate student teams into local community service multidisciplinary service learning projects . Within EPICS program , teams of undergraduates design , build , and deploy real systems to solve engineering-based problems for local community service and education organizations 1 . It is now operating at 15 universities nationwide with over 1350 students participated 1 . By using the macros and functions it helps to portray to the students the difference between using two or more different numerical techniques and the types and magnitudes of errors that may result . The second main reason for using macros and m-file functions in the numerical methods classroom is that it reinforces the need for engineers to develop well written and structured computer programs that are used as tools in solving engineering-based problems . This paper details the different types of numerical methods that were taught in the class . The implication of multi-fractal spectrum is more profound with profusion of information than the simply fractal dimension . In settling the engineering-oriented problems , the multi-fractal spectrum is mainly to describe the random probability distribution of physical phenomenon . Before the damage of structures occurrence , the multi-fractal spectrum of space distribution of interior strain changes from centralized to loose , which indicates that complexity of damage fractal structure and the inhomogeneity of space distribution are both increasing . There are a limited number of spacecrafts that are controlled by advanced control methods , among which H2/H∞ robust control is the most popular method to deal with flexible structures , adaptive control is commonly used to deal with model/parameter uncertainty , and the linear quadratic regulator ( LQR ) is the most frequently used method in case of optimal control . It is hoped that this review paper will enlighten aerospace engineers who hold an open mind about advanced control methods , as well as scholars who are enthusiastic about engineering-oriented problems . It shows the connection between the proposed skills in different Electronic Engineering program courses and the consolidation through solving problems . The elements of the CDIO initiative were identified in the different development stages to find engineering problem solutions : Conceiving , Designing , Implementing and Operating . The analysis of the whole process can go beyond to simple exercise routine and it evidence the way in which engineering students have an asynchronous learning as they progress in achieving to the required solution . CAD brought it out of the past and into the future with a renewed respect , appreciation , precision , and applicability to a wide variety of engineering problems . Thus , graphical analysis has become a champion of faculty seeking to provide solutions to problems and offer an entirely new perspective to engineering problem solutions . “ Graphical thinking ” provides a tremendous tool to engineers seeking to develop an extensive conceptual knowledge base via greater visualization capabilities . For modularity , the tool is divided into three blocks : 1 a collection of numerical methods , which constitutes the core of the package ; 2 a series of examples , problems , and figures , which contributes to the understanding of the applicability of the numerical techniques implemented in the package ; and 3 a graphics user interface , which integrates the methods and examples into MATLAB 's graphical environment . The numerical methods range in complexity from elementary applications to more complex examples representative of engineering problems of interest . A help facility assists the user with the program features . The question of suitable numerical schemes for application in turbulence simulations is addressed in the next section of the chapter . And at the end , a survey is given of typical DNS and LES applications for fundamental studies and more engineering-type problems in order to illustrate the historical development and the state-of-the-art . It is based on a generalization of modus ponens ( MP ) known as generalized modus ponens ( GMP ) . We are interested in approximate reasoning within symbolic multi-valued logic framework . In a previous work , we have proposed a new GMP based on linguistic modifiers in the multi-valued logic framework . In this paper , we introduce an axiomatic definition of an interval-valued fuzzy sets ' inclusion measure which is different from Bustince 's [ H. Bustince , Indicator of inclusion grade for interval-valued fuzzy sets , Applications to approximate reasoning based on interval-valued fuzzy sets , International Journal of Approximate Reasoning , 23 ( 2000 ) 137 - 209 ] . The relationship among the normalized distance , the similarity measure , the inclusion measure , and the entropy of interval-valued fuzzy sets is investigated in detail . The set-theoretic compatibility measures are the predominant ones found in the historical survey of compatibility measure in Chapter 3 and continue to provide the foundation for similarity assessment in the majority of approximate reasoning applications . Table 12.1 gives the compatibility measure and its subclass for measures considered in this set of experiments . The fuzzy numbers arise in decision making , control theory , fuzzy systems and approximate reasoning problems . The operations on them are becoming more and more popular . This method used Bayesian formula combining with the conditional independence assumption to do causal reasoning existing in a variety of graphs according to the high efficiency of expressing uncertainty reasoning by Bayesian network , and transformed a higher-order conditional event to normal event solution via Condition Event Algebra , so the uncertainty reasoning solution of domain knowledge can be realized . Through analyzing an uncertain reasoning case solving based on the tourism domain ontology , the results show that the proposed method is effective , and can well solve approximate reasoning problems of domain knowledge uncertainty reasoning . It is possible to identify the maximum ozone concentration episodes from these diagrams and then to set up the valid rules in the form of IF-THEN logical statements . These rules are obtained from available daily ozone , NO"x and meteorological data as a first approximate reasoning step . In this manner , without mathematical formulations , expert maximum ozone concentration systems are identified . This expert system has four steps , Pre-processing , Segmentation , Feature extraction and Approximate reasoning . The focus of this paper is to improve the last step , Approximate reasoning step , by using fuzzy function strategy instead of fuzzy rule-base approach . The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results . The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework . The algorithms have adjustable levels of accuracy and efficiency , and they can be applied uniformly across many areas and problem tasks . In general , a calculus of approximation spaces is a system for combining , describing , measuring , reasoning about , and performing operations on approximation spaces . An approach to achieving a calculus of approximation spaces that provides a basis for approximating reasoning in distributed systems of cooperating agents is considered in this paper . Examples of basic concepts are given throughout this paper to illustrate how approximation spaces can be beneficially used in many settings , in particular for complex concept approximation . In particular we defined approximation techniques for : propositional logic , fragments of first-order logic ( concept description languages ) and modal logic . In our research we also addressed the issue of representing the knowledge of a reasoner with limited resources and how to use such a knowledge for approximate reasoning purposes . We introduce a method to automatically abstract ASP programs that preserves their structure by reducing the vocabulary while ensuring an over-approximation ( i.e. , each original answer set maps to some abstract answer set ) . This allows for generating partial answer set candidates that can help with approximation of reasoning . Computing the abstract answer sets is intuitively easier due to a smaller search space , at the cost of encountering spurious answer sets . Furthermore , they prove that the Cholesky factors of the transformed covariance matrices also consist of subblocks that are essentially banded . They combine these two observations to construct a fast O(N/sup 2/ ) algorithm for solving the N/spl times/N linear positive definite systems of equations that arise in statistical signal processing . Finally , they provide an error analysis of the proposed linear positive definite system solver . The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications . Students , practitioners and researchers interested in statistical signal processing , computational intelligence , and machine learning will find in this book the theory to understand the basics , the algorithms to implement applications , and exciting but still unexplored leads that will provide fertile ground for future research . This solar energy IoT system is currently programmable and can : a ) provide mobile analytics , b ) enable solar farm control , c ) detect and remedy faults , d ) optimize power under different shading conditions , and e ) reduce inverter transients . A series of federal and industry grants sponsored research on statistical signal analysis , communications , and optimization of this system . A Cyber-Physical project , whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods , was launched recently . This paper investigates the classical statistical signal processing problem of detecting a signal in the presence of colored noise with an unknown covariance matrix . In particular , we consider a scenario where 𝓂-dimensional $ p$ possible signal-plus-noise samples and 𝓂-dimensional $ n$ noise-only samples are available at the detector . This paper considers a statistical signal processing problem involving agent based models of financial markets which at a micro-level are driven by socially aware and risk- averse trading agents . These agents trade ( buy or sell ) stocks by exploiting information about the decisions of previous agents ( social learning ) via an order book in addition to a private ( noisy ) signal they receive on the value of the stock . In particular , such topics as hypothesis test-based detection , fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated , from theoretical foundations to practical implementations . With a large number of illustrations and practical examples , this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing , as well as a reference for researchers in related fields . In this work , we show that the same is true for sharp functions that are only weakly convex , provided that the subgradient methods are initialized within a fixed tube around the solution set . A variety of statistical and signal processing tasks come equipped with good initialization and provably lead to formulations that are both weakly convex and sharp . Therefore , in such settings , subgradient methods can serve as inexpensive local search procedures . The application of two methods of statistical processing of signals is considered . The correlation method with pre-filtering using the orthogonal Laguerre-based filters is proposed for broadband signals . We introduce a reduced-detail paradigm for nonstationary statistical signal processing with enhanced performance . Time-frequency localized subspace signal components ( called robustons ) are used as atomic entities for statistical signal modeling and processing . Robuston signal processing employs special time-varying filters that allow an efficient on-line implementation , and statistical signal descriptors that can be estimated in a stable manner by means of intra-subspace averaging . Acknowledgements It is a great pleasure to thank my supervisor , Dr. Steven Blostein , for his excellent supervision and nancial support throughout this research . Without Dr. Blostein 's constructive suggestions and knowledgeable guidance in statistical signal processing area , this work would not have been successfully completed . I would like to thank my defence committee members , Dr. S. G. Akl ( Computer Science ) , Dr. N. C. Beaulieu , Dr J. C. Cartledge and Dr. D. Falconer ( Carleton University ) for taking time to review this thesis . Two dimensional chirp signal has been used for modeling gray scale symmetric images in the statistical signal processing literature . In this paper we propose a computationally efficient algorithm for estimating different parameters of a two dimensional chirp signal model in presence of stationary noise . Abstract Bayesian inference has become a standard tool in modern statistical signal processing theory and machine learning . It is based on a probabilistically consistent representation of available knowledge about variables of interest and the amount of uncertainty contained in this knowledge . This paper describes the use of a multi-objective evolutionary algorithm to solve an engineering design problem - determining the geometry and operating settings for a crusher in a comminution circuit for ore processing . The outcome is a tool for consulting engineers that can be used to create and explore candidate designs for various scenarios . Finding optimum conditions for process factors in an engineering optimization problem with response surface functions requires structured data collection using experimental design . When the experimental design space is constrained owing to external factors , its design space may form an asymmetrical and irregular shape and thus standard experimental design methods become ineffective . Optimization is one the most important functions in Data mining . In an engineering optimization problem , it is not easy to find an exact solution . In this regard , evolutionary techniques have been applied as a part of procedure of achieving the exact solution . In this paper , conceptual design frame and its flow model of innovating products is put forward through combining the methods of conceptual design and TRIZ theory . Process system model of innovating design that includes requirement analysis , total function analysis and decomposing , engineering problem analysis , finding solution of engineering problem and primarily design is constructed and this establishes the base for innovating design of existing product . In this paper , we put forward a so-called Game-Inspired Engineering Design ( GIED ) process which is a systematic design methodology using analogies of games or game elements to figure out solutions for resolving existing engineering problems . The whole design process consists of five steps , which are 1 ) engineering problem definition , 2 ) engineering problem reformulation , 3 ) game solution search , 4 ) principle extraction from the game-based solution , and 5 ) principle application & implementation . The individual steps are discussed in details and a case study of a cleaning robot capable of reconfiguration has been presented which was developed through following the GIED process . Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided . This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications . The author suggests that , in engineering problem formulation for mathematics courses , the prime focus should be good engineering problems from which one can extract the opportunity to learn relevant mathematics and useful engineering skills . A series of guidelines on how to formulate engineering problems suitable for mathematics courses is formulated.<<ETX > > In this paper , we extend this algorithm to solve multi-objective optimisation problems . The proposed multi-objective bat algorithm ( MOBA ) is first validated against a subset of test functions , and then applied to solve multi-objective design problems such as welded beam design . Simulation results suggest that the proposed algorithm works efficiently . Different problems with these bounds were solved using genetic , accelerated particle swarm , and cuckoo search algorithms . Three different problems ( multi-pass turning , welded beam design , and tension spring ) were used as case studies . The results of the studies were compared with the earlier studies . Then the method COBRA-m is modified for solving constrained multiobjective optimization problems . The proposed algorithm is first validated against a subset of test functions , and then applied to known multiobjective design problems such as welded beam design and disc brake design . Simulation results suggest that the proposed algorithm works effectively Also , various examples are discussed . Third , a real-time case study “ Welded Beam Design Problem ” is solved with 10 different metaheuristics and the experimental results are statistically analyzed with non-parametric Friedman test in order to estimate the different performance of metaheuristics . Finally , limitation and new trends of metaheuristics are discussed . The results revealed that the MOSFS performs significantly better than other algorithms in both convergence and coverage and it is able to approximate true Pareto front precisely . In the end , MOSFS is implemented to solve a real-world engineering design problem called welded beam design problem and efficiency of the algorithm is compared to recently developed algorithms . The results of simulations and the Wilcoxon rank-sum test showed that the MOSFS is able to provide the most promising Pareto front for the problem considering various performance metrics at a 95 % confidence level . This paper is the first research study of both the whale algorithm and HWOANM for the optimization of processing parameters in manufacturing processes . The HWOANM is evaluated using the well-known benchmark problems such as cantilever beam problem , welded beam problem , and three-bar truss problem . Finally , a grinding manufacturing optimization problem is solved to investigate the performance of the HWOANM . Experimental results showed that PSOSCANMS is more successful than PSO and outperforms the other state-of-the-art compared algorithms over the tested optimization problems . Moreover , an engineering design problem such as spring compression , welded beam is also considered . The result of the problems in engineering design and application problems shows that the algorithm proposed is relevant in difficult cases involving unknown search areas . Hence , in this paper , a novel developed meta-heuristic algorithm based on the electrons ’ search approach is presented and the algorithm is benchmarked on 20 mathematical functions with four well-known methods for validation and verification tests . Moreover , the algorithm is implemented in two engineering design problems ( tension/expression spring and welded beam design optimization ) and the results demonstrate that the ERSA performs more efficiently for solving unknown search spaces and the algorithm found best solution in approximately 95 % of the reviewed benchmark functions . In this paper , the underlying control network was built up in view of the actual operation of heating network through applying the intelligent nodes based on CAN bus , and the data acquisition and control functions were completed to realize the local intelligent control , then adapt the change of outdoor temperature and the operating condition of heating network , and achieve the unattended purpose ultimately . The seamless connection between the underlying control network ( CAN network ) and the upper management network ( Internet ) was implemented by using embedded network gateway and reliable VPN technology , the operating parameters of the heat exchange stations can be remote monitored in real-time . ABSTRACT A microcomputer based data acquisition and control system ( DACS ) has been developed for a solar energy research laboratory at the University of Waterloo , The DACS is based on an IBM-PC personal computer and Hewlett-Packard data acquisition and control unit interfaced to standard test system and weather sensors . A software package has been developed to run the data acquisition and control functions . Data analysis is performed off-line using the Lotus 1 - 2 - 3 electronic spreadsheet program . The Individual Perspective Design ( IPD ) for local ( load level ) control is extended to piecewise deterministic and diffusion models for thermostatically controlled load models . The IPD design is formulated as an infinite horizon average reward optimal control problem , in which the reward function contains a term that uses relative entropy rate to model deviation from nominal dynamics . In the piecewise deterministic model , the optimal solution is obtained via the solution to an eigenfunction problem , similar to what is obtained in prior work . Speech technology is firmly rooted in daily life , most notably in command-and-control ( C&C ) applications . C&C usability downgrades quickly , however , when used by people with non-standard speech . Benning , and a Limited Use Assessment conducted by the Marine Corps Forces Pacific Command Experimentation Center ( MEC ) at Camp Pendleton evaluating the effectiveness of using speech commands to control an Unmanned Ground Vehicle . SPEAR , developed by Think-A-Move , Ltd. , provides speech control of UGVs . SPEAR detects user speech in the ear canal with an earpiece containing an in-ear microphone . The problem of constructing a guaranteeing anticipating control for a shock isolator that protects an object on a movable base from impacts acting on this base is considered . It is assumed that the shape of the impact is not known but its duration is given and the base acceleration is described by a sign definite function of time the integral of which over time is also given . It is significantly more efficient than those that using expensive cryptographic primitives . I also show how these schemes can be used for multigrained access control with various options . In addition , I describe how much these schemes are resilient to infer the types of connecting edges . The library provides a portable layer and user friendly abstraction over the underlying parallel environment . CAPLib contains optimised message passing routines for data exchange between parallel processes and other utility routines for parallel execution control , initialisation and debugging . By compiling and linking with different implementations of the library , the user is able to run on many different parallel environments . Wafer level reliability ( WLR ) was envisioned as an upstream tool to be applied within the wafer fab process to avoid or detect reliability problems before they can reach the fmished product . To demonstrate a practical application of this approach , the development , execution and results of a WLR qualification and production control plan for an advanced , sub-micron , triple-level metal CMOS process is provided . The WLR qualification evaluated the following reliability concerns : oxygen precipitation , contact integrity , hot carrier reliability , mobile ion contamination , dielectric integrity , electromigration , stress voiding , via integrity , and corrosion susceptibility . We present an experimental robotic interaction test bed to support our investigation . We use the test bed to explore two HRI-related task-sets : robotic navigation control and robotic posture control . We discuss the implementation of these two task-sets using an AIBO " robot dog . In this paper , a robust distributed power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning , which does not require the interference channel and power strategy information among CR users ( and from CR users to PUs ) . To the best of our knowledge , this research provides the solution for the first time for the incomplete-information power control game in CR networks . During the repeated game , CR users can control their power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step . Non-ideal state equations are needed to compute a growing number of engineering-relevant problems . The additional computational overhead from the complex thermodynamics accounts for a significant portion of the total computation , especially the near-critical or transcritical thermodynamic regimes . Artificial Immune Systems ( AIS ) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural immune systems for use in tackling complex problem domains . The Clonal Selection Algorithm ( CLONALG ) is one such system inspired by the clonal selection theory of acquired immunity , which has shown success on broad range of engineering problem domains . The focus of this work is the CLONALG algorithm , specifically the techniques history , previous research and algorithm function .
In this paper we examine the effect of the use of a Computer Algebra System ( CAS ) to solve interdisciplinary problems . We have been working for some years in the application of mathematics to different engineering problems , the main reason was to improve the motivation of our students and to make possible for them to connect and apply what they learn in Linear Algebra class to other engineering subjects . We describe a case study with students of Electromechanical and Mechanical Engineering from the Coimbra Institute of Engineering and from Geological and Industrial Engineering students from the University of Salamanca in a Linear Algebra course .
Abstract . The Lagrangian Meshless Smoothed Particle Hydrodynamics ( SPH ) method is na alternative to the traditional mesh Eulerian methods for solving engineering problems . Although it began to be used a few decades ago , SPH method has advantages compared to methods with meshes , in the numerical solution of classical problems studied in engineering courses .
The project includes an engineering education research component , which is focused on evaluating the effectiveness of the curriculum in enhancing students ' knowledge , skills , and motivation in cybersecurity concepts . The first year of the project has been completed by introducing cybersecurity modules into four courses ( CS : Introduction to Software Design , and Software Design and Data Structures , and CPE : Engineering Problem Solving with C++ , and Data Structures and Algorithms ) impacting ~1600 students . The details of curriculum development , implementation and , preliminary findings of the research will be presented .
We focus on representing expert knowledge , and examine how control knowledge provides the structure for using domain knowledge . To demonstrate our architecture for engineering problem solving , we present a Thermodynamics Problem Solver ( TPS ) that uses the IPSA architecture . TPS solves over 150 thermodynamics problems taken from the first four chapters of a common thermodynamics textbook and produces expert-like solutions .
Data shows Problem Solving Skills is the second most important non technical skill sought by employers ( To et al. , 2019 ) ; The studies show that the problems can not be solved by using the same kind of thinking approach applied at the moment it was created . Therefore , a systematic analytical skill is required to handle the engineering related problems happening at manufacturing environment or engineering workplace . The purpose of this paper is to analyze the existing literature about Problem Solving skills for graduate engineers through a systematic literature review .
It presents a unified approach to moment methods by employing the concepts of linear spaces and functional analysis . Written especially for those who have a minimal amount of experience in electromagnetic theory , this book illustrates theoretical and mathematical concepts to prepare all readers with the skills they need to apply the method of moments to new , engineering-related problems . Written especially for those who have a minimal amount of experience in electromagnetic theory , theoretical and mathematical concepts are illustrated by examples that prepare all readers with the skills they need to apply the method of moments to new , engineering-related problems . "
Comparisons of responses preand post-semester , as well as comparisons across courses , will help determine impacts of the workshop innovation . We expect that students ’ experiences in the workshops will improve their general abilities to apply the mathematics they have learned to engineering-related problems , and will have positive effects on their self-efficacy perceptions related to succeeding in the engineering curriculum . Our findings will help determine whether positive impacts on student skill and self-efficacy are indeed occurring in ways we are able to detect with the use of these instruments .
Background and Theoretical Framework First established at Purdue University in 1995 , the EPICS program aimed to integrate engineering undergraduate student teams into local community service multidisciplinary service learning projects . Within EPICS program , teams of undergraduates design , build , and deploy real systems to solve engineering-based problems for local community service and education organizations 1 . It is now operating at 15 universities nationwide with over 1350 students participated 1 .
By using the macros and functions it helps to portray to the students the difference between using two or more different numerical techniques and the types and magnitudes of errors that may result . The second main reason for using macros and m-file functions in the numerical methods classroom is that it reinforces the need for engineers to develop well written and structured computer programs that are used as tools in solving engineering-based problems . This paper details the different types of numerical methods that were taught in the class .
The implication of multi-fractal spectrum is more profound with profusion of information than the simply fractal dimension . In settling the engineering-oriented problems , the multi-fractal spectrum is mainly to describe the random probability distribution of physical phenomenon . Before the damage of structures occurrence , the multi-fractal spectrum of space distribution of interior strain changes from centralized to loose , which indicates that complexity of damage fractal structure and the inhomogeneity of space distribution are both increasing .
There are a limited number of spacecrafts that are controlled by advanced control methods , among which H2/H∞ robust control is the most popular method to deal with flexible structures , adaptive control is commonly used to deal with model/parameter uncertainty , and the linear quadratic regulator ( LQR ) is the most frequently used method in case of optimal control . It is hoped that this review paper will enlighten aerospace engineers who hold an open mind about advanced control methods , as well as scholars who are enthusiastic about engineering-oriented problems .
It shows the connection between the proposed skills in different Electronic Engineering program courses and the consolidation through solving problems . The elements of the CDIO initiative were identified in the different development stages to find engineering problem solutions : Conceiving , Designing , Implementing and Operating . The analysis of the whole process can go beyond to simple exercise routine and it evidence the way in which engineering students have an asynchronous learning as they progress in achieving to the required solution .
CAD brought it out of the past and into the future with a renewed respect , appreciation , precision , and applicability to a wide variety of engineering problems . Thus , graphical analysis has become a champion of faculty seeking to provide solutions to problems and offer an entirely new perspective to engineering problem solutions . “ Graphical thinking ” provides a tremendous tool to engineers seeking to develop an extensive conceptual knowledge base via greater visualization capabilities .
For modularity , the tool is divided into three blocks : 1 a collection of numerical methods , which constitutes the core of the package ; 2 a series of examples , problems , and figures , which contributes to the understanding of the applicability of the numerical techniques implemented in the package ; and 3 a graphics user interface , which integrates the methods and examples into MATLAB 's graphical environment . The numerical methods range in complexity from elementary applications to more complex examples representative of engineering problems of interest . A help facility assists the user with the program features .
The question of suitable numerical schemes for application in turbulence simulations is addressed in the next section of the chapter . And at the end , a survey is given of typical DNS and LES applications for fundamental studies and more engineering-type problems in order to illustrate the historical development and the state-of-the-art .
It is based on a generalization of modus ponens ( MP ) known as generalized modus ponens ( GMP ) . We are interested in approximate reasoning within symbolic multi-valued logic framework . In a previous work , we have proposed a new GMP based on linguistic modifiers in the multi-valued logic framework .
In this paper , we introduce an axiomatic definition of an interval-valued fuzzy sets ' inclusion measure which is different from Bustince 's [ H. Bustince , Indicator of inclusion grade for interval-valued fuzzy sets , Applications to approximate reasoning based on interval-valued fuzzy sets , International Journal of Approximate Reasoning , 23 ( 2000 ) 137 - 209 ] . The relationship among the normalized distance , the similarity measure , the inclusion measure , and the entropy of interval-valued fuzzy sets is investigated in detail .
The set-theoretic compatibility measures are the predominant ones found in the historical survey of compatibility measure in Chapter 3 and continue to provide the foundation for similarity assessment in the majority of approximate reasoning applications . Table 12.1 gives the compatibility measure and its subclass for measures considered in this set of experiments .
The fuzzy numbers arise in decision making , control theory , fuzzy systems and approximate reasoning problems . The operations on them are becoming more and more popular .
This method used Bayesian formula combining with the conditional independence assumption to do causal reasoning existing in a variety of graphs according to the high efficiency of expressing uncertainty reasoning by Bayesian network , and transformed a higher-order conditional event to normal event solution via Condition Event Algebra , so the uncertainty reasoning solution of domain knowledge can be realized . Through analyzing an uncertain reasoning case solving based on the tourism domain ontology , the results show that the proposed method is effective , and can well solve approximate reasoning problems of domain knowledge uncertainty reasoning .
It is possible to identify the maximum ozone concentration episodes from these diagrams and then to set up the valid rules in the form of IF-THEN logical statements . These rules are obtained from available daily ozone , NO"x and meteorological data as a first approximate reasoning step . In this manner , without mathematical formulations , expert maximum ozone concentration systems are identified .
This expert system has four steps , Pre-processing , Segmentation , Feature extraction and Approximate reasoning . The focus of this paper is to improve the last step , Approximate reasoning step , by using fuzzy function strategy instead of fuzzy rule-base approach . The results show that Type-2 fuzzy function approach requires less computation steps with less computational complexity and could provide better results .
The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework . The algorithms have adjustable levels of accuracy and efficiency , and they can be applied uniformly across many areas and problem tasks .
In general , a calculus of approximation spaces is a system for combining , describing , measuring , reasoning about , and performing operations on approximation spaces . An approach to achieving a calculus of approximation spaces that provides a basis for approximating reasoning in distributed systems of cooperating agents is considered in this paper . Examples of basic concepts are given throughout this paper to illustrate how approximation spaces can be beneficially used in many settings , in particular for complex concept approximation .
In particular we defined approximation techniques for : propositional logic , fragments of first-order logic ( concept description languages ) and modal logic . In our research we also addressed the issue of representing the knowledge of a reasoner with limited resources and how to use such a knowledge for approximate reasoning purposes .
We introduce a method to automatically abstract ASP programs that preserves their structure by reducing the vocabulary while ensuring an over-approximation ( i.e. , each original answer set maps to some abstract answer set ) . This allows for generating partial answer set candidates that can help with approximation of reasoning . Computing the abstract answer sets is intuitively easier due to a smaller search space , at the cost of encountering spurious answer sets .
Furthermore , they prove that the Cholesky factors of the transformed covariance matrices also consist of subblocks that are essentially banded . They combine these two observations to construct a fast O(N/sup 2/ ) algorithm for solving the N/spl times/N linear positive definite systems of equations that arise in statistical signal processing . Finally , they provide an error analysis of the proposed linear positive definite system solver .
The book compares the performance of ITL algorithms with the second order counterparts in many engineering and machine learning applications . Students , practitioners and researchers interested in statistical signal processing , computational intelligence , and machine learning will find in this book the theory to understand the basics , the algorithms to implement applications , and exciting but still unexplored leads that will provide fertile ground for future research .
This solar energy IoT system is currently programmable and can : a ) provide mobile analytics , b ) enable solar farm control , c ) detect and remedy faults , d ) optimize power under different shading conditions , and e ) reduce inverter transients . A series of federal and industry grants sponsored research on statistical signal analysis , communications , and optimization of this system . A Cyber-Physical project , whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods , was launched recently .
This paper investigates the classical statistical signal processing problem of detecting a signal in the presence of colored noise with an unknown covariance matrix . In particular , we consider a scenario where 𝓂-dimensional $ p$ possible signal-plus-noise samples and 𝓂-dimensional $ n$ noise-only samples are available at the detector .
This paper considers a statistical signal processing problem involving agent based models of financial markets which at a micro-level are driven by socially aware and risk- averse trading agents . These agents trade ( buy or sell ) stocks by exploiting information about the decisions of previous agents ( social learning ) via an order book in addition to a private ( noisy ) signal they receive on the value of the stock .
In particular , such topics as hypothesis test-based detection , fast active contour segmentation and algorithm design for non-conventional imaging systems are comprehensively treated , from theoretical foundations to practical implementations . With a large number of illustrations and practical examples , this book serves as an excellent textbook or reference book for senior or graduate level courses on statistical signal/image processing , as well as a reference for researchers in related fields .
In this work , we show that the same is true for sharp functions that are only weakly convex , provided that the subgradient methods are initialized within a fixed tube around the solution set . A variety of statistical and signal processing tasks come equipped with good initialization and provably lead to formulations that are both weakly convex and sharp . Therefore , in such settings , subgradient methods can serve as inexpensive local search procedures .
The application of two methods of statistical processing of signals is considered . The correlation method with pre-filtering using the orthogonal Laguerre-based filters is proposed for broadband signals .
We introduce a reduced-detail paradigm for nonstationary statistical signal processing with enhanced performance . Time-frequency localized subspace signal components ( called robustons ) are used as atomic entities for statistical signal modeling and processing . Robuston signal processing employs special time-varying filters that allow an efficient on-line implementation , and statistical signal descriptors that can be estimated in a stable manner by means of intra-subspace averaging .
Acknowledgements It is a great pleasure to thank my supervisor , Dr. Steven Blostein , for his excellent supervision and nancial support throughout this research . Without Dr. Blostein 's constructive suggestions and knowledgeable guidance in statistical signal processing area , this work would not have been successfully completed . I would like to thank my defence committee members , Dr. S. G. Akl ( Computer Science ) , Dr. N. C. Beaulieu , Dr J. C. Cartledge and Dr. D. Falconer ( Carleton University ) for taking time to review this thesis .
Two dimensional chirp signal has been used for modeling gray scale symmetric images in the statistical signal processing literature . In this paper we propose a computationally efficient algorithm for estimating different parameters of a two dimensional chirp signal model in presence of stationary noise .
Abstract Bayesian inference has become a standard tool in modern statistical signal processing theory and machine learning . It is based on a probabilistically consistent representation of available knowledge about variables of interest and the amount of uncertainty contained in this knowledge .
This paper describes the use of a multi-objective evolutionary algorithm to solve an engineering design problem - determining the geometry and operating settings for a crusher in a comminution circuit for ore processing . The outcome is a tool for consulting engineers that can be used to create and explore candidate designs for various scenarios .
Finding optimum conditions for process factors in an engineering optimization problem with response surface functions requires structured data collection using experimental design . When the experimental design space is constrained owing to external factors , its design space may form an asymmetrical and irregular shape and thus standard experimental design methods become ineffective .
Optimization is one the most important functions in Data mining . In an engineering optimization problem , it is not easy to find an exact solution . In this regard , evolutionary techniques have been applied as a part of procedure of achieving the exact solution .
In this paper , conceptual design frame and its flow model of innovating products is put forward through combining the methods of conceptual design and TRIZ theory . Process system model of innovating design that includes requirement analysis , total function analysis and decomposing , engineering problem analysis , finding solution of engineering problem and primarily design is constructed and this establishes the base for innovating design of existing product .
In this paper , we put forward a so-called Game-Inspired Engineering Design ( GIED ) process which is a systematic design methodology using analogies of games or game elements to figure out solutions for resolving existing engineering problems . The whole design process consists of five steps , which are 1 ) engineering problem definition , 2 ) engineering problem reformulation , 3 ) game solution search , 4 ) principle extraction from the game-based solution , and 5 ) principle application & implementation . The individual steps are discussed in details and a case study of a cleaning robot capable of reconfiguration has been presented which was developed through following the GIED process .
Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided . This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications .
The author suggests that , in engineering problem formulation for mathematics courses , the prime focus should be good engineering problems from which one can extract the opportunity to learn relevant mathematics and useful engineering skills . A series of guidelines on how to formulate engineering problems suitable for mathematics courses is formulated.<<ETX > >
In this paper , we extend this algorithm to solve multi-objective optimisation problems . The proposed multi-objective bat algorithm ( MOBA ) is first validated against a subset of test functions , and then applied to solve multi-objective design problems such as welded beam design . Simulation results suggest that the proposed algorithm works efficiently .
Different problems with these bounds were solved using genetic , accelerated particle swarm , and cuckoo search algorithms . Three different problems ( multi-pass turning , welded beam design , and tension spring ) were used as case studies . The results of the studies were compared with the earlier studies .
Then the method COBRA-m is modified for solving constrained multiobjective optimization problems . The proposed algorithm is first validated against a subset of test functions , and then applied to known multiobjective design problems such as welded beam design and disc brake design . Simulation results suggest that the proposed algorithm works effectively
Also , various examples are discussed . Third , a real-time case study “ Welded Beam Design Problem ” is solved with 10 different metaheuristics and the experimental results are statistically analyzed with non-parametric Friedman test in order to estimate the different performance of metaheuristics . Finally , limitation and new trends of metaheuristics are discussed .
The results revealed that the MOSFS performs significantly better than other algorithms in both convergence and coverage and it is able to approximate true Pareto front precisely . In the end , MOSFS is implemented to solve a real-world engineering design problem called welded beam design problem and efficiency of the algorithm is compared to recently developed algorithms . The results of simulations and the Wilcoxon rank-sum test showed that the MOSFS is able to provide the most promising Pareto front for the problem considering various performance metrics at a 95 % confidence level .
This paper is the first research study of both the whale algorithm and HWOANM for the optimization of processing parameters in manufacturing processes . The HWOANM is evaluated using the well-known benchmark problems such as cantilever beam problem , welded beam problem , and three-bar truss problem . Finally , a grinding manufacturing optimization problem is solved to investigate the performance of the HWOANM .
Experimental results showed that PSOSCANMS is more successful than PSO and outperforms the other state-of-the-art compared algorithms over the tested optimization problems . Moreover , an engineering design problem such as spring compression , welded beam is also considered . The result of the problems in engineering design and application problems shows that the algorithm proposed is relevant in difficult cases involving unknown search areas .
Hence , in this paper , a novel developed meta-heuristic algorithm based on the electrons ’ search approach is presented and the algorithm is benchmarked on 20 mathematical functions with four well-known methods for validation and verification tests . Moreover , the algorithm is implemented in two engineering design problems ( tension/expression spring and welded beam design optimization ) and the results demonstrate that the ERSA performs more efficiently for solving unknown search spaces and the algorithm found best solution in approximately 95 % of the reviewed benchmark functions .
In this paper , the underlying control network was built up in view of the actual operation of heating network through applying the intelligent nodes based on CAN bus , and the data acquisition and control functions were completed to realize the local intelligent control , then adapt the change of outdoor temperature and the operating condition of heating network , and achieve the unattended purpose ultimately . The seamless connection between the underlying control network ( CAN network ) and the upper management network ( Internet ) was implemented by using embedded network gateway and reliable VPN technology , the operating parameters of the heat exchange stations can be remote monitored in real-time .
ABSTRACT A microcomputer based data acquisition and control system ( DACS ) has been developed for a solar energy research laboratory at the University of Waterloo , The DACS is based on an IBM-PC personal computer and Hewlett-Packard data acquisition and control unit interfaced to standard test system and weather sensors . A software package has been developed to run the data acquisition and control functions . Data analysis is performed off-line using the Lotus 1 - 2 - 3 electronic spreadsheet program .
The Individual Perspective Design ( IPD ) for local ( load level ) control is extended to piecewise deterministic and diffusion models for thermostatically controlled load models . The IPD design is formulated as an infinite horizon average reward optimal control problem , in which the reward function contains a term that uses relative entropy rate to model deviation from nominal dynamics . In the piecewise deterministic model , the optimal solution is obtained via the solution to an eigenfunction problem , similar to what is obtained in prior work .
Speech technology is firmly rooted in daily life , most notably in command-and-control ( C&C ) applications . C&C usability downgrades quickly , however , when used by people with non-standard speech .
Benning , and a Limited Use Assessment conducted by the Marine Corps Forces Pacific Command Experimentation Center ( MEC ) at Camp Pendleton evaluating the effectiveness of using speech commands to control an Unmanned Ground Vehicle . SPEAR , developed by Think-A-Move , Ltd. , provides speech control of UGVs . SPEAR detects user speech in the ear canal with an earpiece containing an in-ear microphone .
The problem of constructing a guaranteeing anticipating control for a shock isolator that protects an object on a movable base from impacts acting on this base is considered . It is assumed that the shape of the impact is not known but its duration is given and the base acceleration is described by a sign definite function of time the integral of which over time is also given .
It is significantly more efficient than those that using expensive cryptographic primitives . I also show how these schemes can be used for multigrained access control with various options . In addition , I describe how much these schemes are resilient to infer the types of connecting edges .
The library provides a portable layer and user friendly abstraction over the underlying parallel environment . CAPLib contains optimised message passing routines for data exchange between parallel processes and other utility routines for parallel execution control , initialisation and debugging . By compiling and linking with different implementations of the library , the user is able to run on many different parallel environments .
Wafer level reliability ( WLR ) was envisioned as an upstream tool to be applied within the wafer fab process to avoid or detect reliability problems before they can reach the fmished product . To demonstrate a practical application of this approach , the development , execution and results of a WLR qualification and production control plan for an advanced , sub-micron , triple-level metal CMOS process is provided . The WLR qualification evaluated the following reliability concerns : oxygen precipitation , contact integrity , hot carrier reliability , mobile ion contamination , dielectric integrity , electromigration , stress voiding , via integrity , and corrosion susceptibility .
We present an experimental robotic interaction test bed to support our investigation . We use the test bed to explore two HRI-related task-sets : robotic navigation control and robotic posture control . We discuss the implementation of these two task-sets using an AIBO " robot dog .
In this paper , a robust distributed power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning , which does not require the interference channel and power strategy information among CR users ( and from CR users to PUs ) . To the best of our knowledge , this research provides the solution for the first time for the incomplete-information power control game in CR networks . During the repeated game , CR users can control their power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step .
Non-ideal state equations are needed to compute a growing number of engineering-relevant problems . The additional computational overhead from the complex thermodynamics accounts for a significant portion of the total computation , especially the near-critical or transcritical thermodynamic regimes .
Artificial Immune Systems ( AIS ) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural immune systems for use in tackling complex problem domains . The Clonal Selection Algorithm ( CLONALG ) is one such system inspired by the clonal selection theory of acquired immunity , which has shown success on broad range of engineering problem domains . The focus of this work is the CLONALG algorithm , specifically the techniques history , previous research and algorithm function .
CAS and real life problems to learn basic concepts in Linear Algebra course DEVELOPMENT OF A COMPUTER CODE USING THE LAGRANGIAN SMOOTHED PARTICLE HYDRODYNAMICS (SPH) METHOD FOR SOLUTION OF PROBLEMS IN FLUID DYNAMICS AND HEAT TRANSFER Development and Analysis of a Spiral Theory-based Cybersecurity Curriculum: (Abstract Only) An integrated architecture for engineering problem-solving A Structured 8 Disciplines Methodology To Develop Problem Solving Skills Among Engineering Students During Internship :A Systematic Literature Review Field Computation by Moment Methods Impact of Collaborative Problem-solving Workshops in Engineering Calculus Course on Applied Mathematical Construct Validity Of The Epics Scales Across Groups: A Mimic Modeling Investigation Using Macros And Functions To Enhance Teaching Numerical Methods To Engineering Students Application of multi-fractal on diagnosis of smart materials and structures Applications of advanced control methods in spacecrafts: progress, challenges, and future prospects Development of an automatic control system employing CDIO standards and competence-based learning The “Back To The Future” Experience Of Graphical Analysis Applied numerical methods and graphical visualization Turbulent flows: Direct numerical simulation and large-eddy simulation Extended symbolic approximate reasoning based on linguistic modifiers Normalized distance, similarity measure, inclusion measure and entropy of interval-valued fuzzy sets and their relationship Set-Theoretic Comparative Study The application of horizontal membership functions to fuzzy arithmetic operations A domain knowledge uncertainty reasoning method combining Bayesian network with conditional event algebra Contour diagram fuzzy model for maximum surface ozone prediction Using Type-2 fuzzy function for diagnosing brain tumors based on image processing approach Mini-Buckets: A General Scheme for Generating Approximations in Automated Reasoning Calculi of Approximation Spaces Tractable Reasoning via Approximation Omission-Based Abstraction for Answer Set Programs Fast positive definite linear system solvers Information Theoretic Learning - Renyi's Entropy and Kernel Perspectives Solar energy management as an Internet of Things (IoT) application Detection of a Signal in Colored Noise: A Random Matrix Theory Based Analysis Sequential Detection of Market shocks using Risk-averse Agent Based Models Statistical Image Processing Techniques for Noisy Images: An Application-Oriented Approach Subgradient Methods for Sharp Weakly Convex Functions Methods of processing broadband and narrowband radar signals Robuston methods for stable statistical signal processing: principles and application to nonstationary signal estimation Spatial-temporal signal processing Efficient algorithm for estimating the parameters of two dimensional chirp signal Bayesian Approach to Collaborative Inference in Networks of Agents Designing Crushers With A Multi-objective Evolutionary Algorithm Response surface optimization for a nonlinearly constrained irregular experimental design space Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring Innovating Method of Existing Mechanical Product Based on TRIZ Theory Game-Inspired Engineering Design: Processes and Case Studies Optimal batch scheduling of a multiproduct dairy process using a combined optimization/constraint programming approach Engineering problem formulation for mathematics courses Bat algorithm for multi-objective optimisation An efficient approach by adjusting bounds for heuristic optimization algorithms Co-Operation of Biology-Related Algorithms for Constrained Multiobjective Optimization Metaheuristic Algorithms: A Comprehensive Review Multi-Objective Stochastic Fractal Search: a powerful algorithm for solving complex multi-objective optimization problems A novel hybrid whale–Nelder–Mead algorithm for optimization of design and manufacturing problems Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems Electron radar search algorithm: a novel developed meta-heuristic algorithm City Heating Network Dispatching and Management Information System A MICROCOMPUTER BASED DATA ACQUISITION AND CONTROL SYSTEM FOR SOLAR ENERGY RESEARCH Distributed Control of Thermostatically Controlled Loads: Kullback-Leibler Optimal Control in Continuous Time The self-taught vocal interface Soldier experiments and assessments using SPEAR speech control system for UGVs Protection of an Object on a Movable Base Using Anticipating Control under the Worst Disturbances Privacy-Preserving Social Network with Multigrained and Multilevel Access Control CAPLib—a ’thin layer‘ message passing library to support computational mechanics codes on distributed memory parallel system Practical application of a wafer-level reliability control program Exploring the use of tangible user interfaces for human-robot interaction: a comparative study Reinforcement Learning for Repeated Power Control Game in Cognitive Radio Networks Uncertainty quantification of tabulated supercritical thermodynamics for compressible Navier-Stokes solvers Clonal selection theory and Clonalg: the clonal selection classification algorithm (CSCA)
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