**3. Structure of application**

For filling of application, the ideal requirements of the patentability and also the application must be filed by the competent authority for a certain country or a group of countries. A quick focus must be given when filing the application and try to cover the application with the following parts.

a.Claims

b.Detailed description (or specification)

#### e.Abstract

#### f. Summary

Who that file the application; the title must be itself descriptive and creative. The title properly indicates the subject matter of the application. The patent application itself should also include all priority information, such as the identification of related applications. The audience for the application basically is the judges and patent examiner. Also, apart from these the patent agent's client and the inventor are also audiences.

### **3.1 Claims**

In the patent filing, the claims for the inventor prepared by the agent that is first it must be easy in language and plan should be in at least three. The patent agent outlines a diagram in the first disclosure meeting and discusses with the inventor. The language or the terminology used for the filing the might be difficult to understand by the inventor. So, the agent explains it in pictures or another diagram by which it is easy to understand.

In practice, the agent prepares the several drafts for the communication and select the best one for further proceeding. The clam is the legal part of the application. For the preparation of quality contents draft, the agent must give the time and during writing focus on that, it is concise and explanatory. In any case, it is seen that due to short of time the ideal paper not prepare and such situation the technical paper of the inventor consider a claim. For convenient the claim may present in a picture that is easy to understand. By pictures language the novelty of the paper is high.

As the claims completed the patent agent require to check the specification to verify and confirm that the claim terms appropriately explain in the paper.

The detailed description section, sometimes known as the "preferred embodiment of invention" section or the "disclosed embodiment of the invention" section breathes life into the claims and provides a sufficient explanation of the invention for an ordinary person skilled in the art to make and understand the invention. In some jurisdictions, the term "specification" is also used to refer to the description in addition to the summary and background sections of the application; suffice to say that "detailed description" and "specification" are generally the same for purposes of patent drafting.

The detailed description section must be closely tied to the drawings. This section cannot be substantively amended once the application has been filed. Consequently, the patent agent must make sure that the detailed description section provides an appropriate degree of technical disclosure on the day that the application is filed as he won't have a second chance to alter this part of the application. The patent agent cannot amend his application to include new technical disclosure during prosecution.

If the patent agent uses a highly abstract term in the claims he should consider using the term in the detailed description section, but in a manner that ties the abstract term to a specific embodiment of the invention. For example, if the claims use the term "warning device" for automobile horn, the specification could either say: "One example of warning device 10212 is an automobile horn. Other warning devices may be used, consistent with the spirit of the invention," or, "Automobile horn 102 constitutes a warning device. Many other such warning devices may be used consistent with the spirit of the invention."

As mentioned above, the detailed description section cannot be substantively amended once the application is filed. Thus, a patent agent should take care that

**57**

**3.3 Drawings**

**3.4 Background**

*Patent Application Preparation and Filing DOI: http://dx.doi.org/10.5772/intechopen.88377*

the patent application (1) reflects the disclosure material provided by the inventors, (2) provides sufficient information to enable an ordinary artisan to reproduce the invention and (3) provides sufficient depth so that the claims can be narrowed during patent prosecution to avoid close prior art. Further considerations about the scope and importance of the detailed description section will be discussed below

For the preparation of filing, the specification should be full filling the basic requirements. The condition as per the country rule regulation. For example, Canadian practices are more or less different from those of American practice. But worldwide the American practice is more considering that the Canadian practice

I.Describing the Invention: The first object is to give a hint of what to be claimed. Explain the patent by the inventor through the electronic circuit compromising a combination of a logic gate. In such a way if the inventor wants to file another one application need to only change the logic gate where the microprocessor change. Therefore, all claimed elements must be shown in the specification. In specification there is no limit, it must be broad and require covering most of the points in a general way, where it full

II.Professional Person enables to explain Invention: It is in the concern to update software have full knowledge to a patent agent. For convenient, the agent explains all the content of the application in drawing with the help of updated version of the software. By block diagrams, is easy to understand

III.The Best Mode by the Inventor: The inventor discloses his best knowledge at the time of filing of the application. Two ways by which it explains. First, the invention carries out as claimed, not disclose invention for commercial in the market. The second is subjective here; the inventors may be not disclosing the proper knowledge in such case some aspects of the invention in dilemma.

In this part, the presentation of the invention is the best way of drawing which makes easy to understand the facts of the invention. The patent agent describes the innovative idea with good visual supporting materials. It is found that the drawings are the most important of the patent after the claims. The preparation of the picture itself an innovative idea, the patent agent first read all the contents of the application and thick on the designing of the picture. Some time it is very difficult to explain the principle or innovative idea by drawing a picture. During the explanation of innovation by drawing the agent focus on the using of minimum words and that itself explanatory, use in some way a reference in the picture.

This part is very less important for the filing of the application, the patent agent focus on that very least al last. If the background of the application prepares first the

and are also illustrated by the following example [2, 5, 6].

**3.2 Detailed description (or specification)**

The specification discusses in three parts:

the whole contents of the patent.

fill all terms that cover the patent application.

regarding the specification.

#### *Patent Application Preparation and Filing DOI: http://dx.doi.org/10.5772/intechopen.88377*

*Intellectual Property Rights - Patent*

Who that file the application; the title must be itself descriptive and creative. The title properly indicates the subject matter of the application. The patent application itself should also include all priority information, such as the identification of related applications. The audience for the application basically is the judges and patent examiner. Also, apart from these the patent agent's client and the inventor

In the patent filing, the claims for the inventor prepared by the agent that is first it must be easy in language and plan should be in at least three. The patent agent outlines a diagram in the first disclosure meeting and discusses with the inventor. The language or the terminology used for the filing the might be difficult to understand by the inventor. So, the agent explains it in pictures or another diagram by

In practice, the agent prepares the several drafts for the communication and select the best one for further proceeding. The clam is the legal part of the application. For the preparation of quality contents draft, the agent must give the time and during writing focus on that, it is concise and explanatory. In any case, it is seen that due to short of time the ideal paper not prepare and such situation the technical paper of the inventor consider a claim. For convenient the claim may present in a picture that is

As the claims completed the patent agent require to check the specification to

The detailed description section, sometimes known as the "preferred embodiment of invention" section or the "disclosed embodiment of the invention" section breathes life into the claims and provides a sufficient explanation of the invention for an ordinary person skilled in the art to make and understand the invention. In some jurisdictions, the term "specification" is also used to refer to the description in addition to the summary and background sections of the application; suffice to say that "detailed description" and "specification" are generally the same for purposes

The detailed description section must be closely tied to the drawings. This section cannot be substantively amended once the application has been filed. Consequently, the patent agent must make sure that the detailed description section provides an appropriate degree of technical disclosure on the day that the application is filed as he won't have a second chance to alter this part of the application. The patent agent cannot amend his application to include new technical disclosure

If the patent agent uses a highly abstract term in the claims he should consider using the term in the detailed description section, but in a manner that ties the abstract term to a specific embodiment of the invention. For example, if the claims use the term "warning device" for automobile horn, the specification could either say: "One example of warning device 10212 is an automobile horn. Other warning devices may be used, consistent with the spirit of the invention," or, "Automobile horn 102 constitutes a warning device. Many other such warning devices may be

As mentioned above, the detailed description section cannot be substantively amended once the application is filed. Thus, a patent agent should take care that

easy to understand. By pictures language the novelty of the paper is high.

verify and confirm that the claim terms appropriately explain in the paper.

e.Abstract

f. Summary

are also audiences.

of patent drafting.

during prosecution.

used consistent with the spirit of the invention."

which it is easy to understand.

**3.1 Claims**

**56**

the patent application (1) reflects the disclosure material provided by the inventors, (2) provides sufficient information to enable an ordinary artisan to reproduce the invention and (3) provides sufficient depth so that the claims can be narrowed during patent prosecution to avoid close prior art. Further considerations about the scope and importance of the detailed description section will be discussed below and are also illustrated by the following example [2, 5, 6].


**Author details**

Abdullahi Hassan Gorondutse1,2\* and Haim Hilman1

\*Address all correspondence to<EMAIL_ADDRESS>
1 School of Business Management, University Utara Malaysia, Sintok, Kedah, Malaysia

2 Department of Economics and Management Sciences, Nigerian Police Academy Wudil, Kano, Nigeria


A feed-forward, multilayer ANN can be used to learn and distinguish from aggregated, extracted NIALM feature data for load disaggregation. In this paper, a comparative study where a feed-forward, multilayer ANN against the GA described in the previous subsection is used to address the same NIALM/load disaggregation problem, Equation (1), is conducted. As seen in Figure 5, a feed-forward, multilayer ANN contains an input layer, a number of intermediate hidden layers, and an output layer. The size of the input layer depends on the number of independent variables (extracted representative features) of considered feature data to be learned. The number of intermediate hidden layers with their size (the number of hidden neurons) specified in each hidden layer, can be determined experimentally through hyperparameter tuning where hyperparameters including the learning algorithm and the number of epochs for iterative rounds of learning affect how well the connectionism can represent the considered feature data (the hyperparameters are a set of parameters whose value is specified and used to control the learning process). The size of the output layer depends on the number of dependent variables (mutually exclusive classes) of considered feature data to be fitted.

**Figure 5.** Representative ANNs, connectionisms, mimicking biologic NNs: (**a**) A connectionism is fully connected; (**b**) A connectionism considers dropout—one of the hyperparameters whose specified value is used to control the learning process of a connectionism—to prevent overfitting [28].

In this paper, *F*1 score described in detail in the following subsection is conducted and used as the performance metric to indicate the effectiveness of the two parallel computing-accelerated AI approaches in load recognition for load disaggregation.

### *2.3. Performance Evaluation of Load Recognition by F1 Score*

In this paper, as shown in Equation (3), *F*1 score [29] is used to evaluate the performance of the two parallel computing-accelerated AI approaches in load recognition for load disaggregation.

$$F\_1 \text{score} = 2 \cdot \frac{Precision \cdot Recall}{Precision + Recall} \tag{3}$$

In Equation (3), precision is a ratio of the total number of correctly recognized positives to the total number of predicted positives. Recall (i.e., sensitivity or hit rate) is a ratio of the total number of correctly recognized positives to the total number of actual positives. See ref. [29] for more details about precision and recall. To summarize, *F*1 score is the harmonic mean of precision and recall. A recognizer that produces no false positives has a precision of 1.0. A recognizer that produces no false negatives has a recall of 1.0. In Equation (3), an *F*1 score reaches its best value at 1.0 (perfect precision

and recall). The higher the score, the better the recognition performance. Besides *F*1 score, we also use receiver operating characteristic (ROC) curves [29] to evaluate the performance of the two parallel computing-accelerated AI approaches in load recognition for load disaggregation. An ROC curve considering false positive rate (FPR) and true positive rate (TPR) is a graph showing the recognition performance of a recognizer examined at all recognition thresholds (or with different configuration settings) [29]. In an ROC curve, an error to a trained and validated recognizer can be computed by the Euclidean distance, from the perfect identification (FPR = 0, TPR = 1) til a resulting (FPR, TPR) [30,31] where FPR and TPR are defined as Equations (4) and (5), respectively. TPR is referred to as recall. As seen in Equations (4) and (5), ROC curves [29] are also used to evaluate the performance of the investigated methodology in load recognition, where TPR (a synonym for recall) defined in Equation (4) and FPR defined in Equation (5) are considered. See ref. [29] for more details about precision and recall.

$$TPR = \frac{TP}{TP + FN} \tag{4}$$

$$FPR = \frac{FP}{FP + TN} \tag{5}$$

In Equations (4) and (5), *TP* (true positives) means the data instances are recognized, and fit with reality. *TN* (true negatives) means the data instances are recognized, and the nonexistence fits with reality. *FP* (false positives) means the data instances are erroneously recognized as positives. Finally, *FN* (false negatives) means the data instances are incorrectly recognized as negatives.

### **3. Experimentation and Results**

In this section, experiments are carried out to verify the validity of the investigated NIALM, by a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset [32]. The UK-DALE dataset contains records of power consumption measured and collected from five different households in the UK. In each house, the authors in [32] recorded both whole-house power consumption (power demand) from the mains every 6 s and power consumption by concerned individual electrical appliances every 6 s. Figure 6 shows the considered historical power demand on two typical days (Sunday 7 December 2014 (Figure 6a,b) and Thursday 4 December 2014 (Figure 6c,d)) in House 1 from the UK-DALE dataset, which is considered, parsed, and used to experimentally validate the performance of the investigated methodology in load recognition. The summarization of the UK-DALE dataset can be found in [32]. In Figure 6a,c, we show the total power demand in the mains. Also, we show the power demand by the concerned individual electrical appliances and all other submeters [32] considered together and treated as a single individual in power absorptions for load disaggregation. As shown in Figure 6b,d, the thin white gap between the power demand in the mains and the summed-up power demand illustrates the amount of power demand, base load, which is not concerned/metered. The concerned electrical appliances, including the considered submeters as a single individual in power absorptions to *Pi* in Equation (2), are listed in Table 1; their power demand is shown in Figure 6 and the base load, *Pbase* in Equation (2), is assumed to have a constant value of 150.0 watts for simplicity's sake. Figure 7 shows the power demand of the several individual electrical appliances targeted in this paper and listed in Table 1. The behavior of the power demand by the electrical appliances can be found in [32]. For example, Figure 7a shows the power demand by the fridge running and doing its respective job of compressor on (state 1: its mean power consumption is ~90.0 watts with a standard deviation of ~44.0) or defrost (state 2: its mean power consumption is ~245.0 watts with a standard deviation of ~16.0). In this paper, the load classes considered from the targeted electrical appliances in Table 1 and recognized are shown in Table 2.

**Figure 6.** *Cont*. 179

**Figure 6.** Considered historical power demand on two typical days in House 1 from the UK-DALE dataset parsed in this paper. The thin blue line in (**a**,**c**) shows the total (whole-house) power demand in the mains; the stacked, filled, and colored blocks show the power demand by the concerned individual electrical appliances and all other submeters [32] considered together and treated as a single individual in the power absorptions also concerned in load disaggregation. Base load in (**b**,**d**) exists in the house.

**Table 1.** Electrical appliances concerned and considered for load disaggregation in this paper.

1 *Pi* (= mean(*Pi*(*t*)): statistically computed, for the mean values, from the historical power demand data and stored in the database in Figure 1, where an eventless NIALM approach, the presented methodology, is shown; 2 τ*i*: *ci*·*std*(*Pi*(*t*)); 3 In the GA, the multistate transitions from the same types of the concerned electrical appliances are mutually exclusive; illegal offspring are assigned an objective value of 1000 to Equation (2) (an illegal chromosome cannot be decoded to/as a solution; that is, such an illegal chromosome cannot be evaluated).

**Figure 7.** *Cont*.

**Figure 7.** *Cont*.

**Figure 7.** *Cont*.

**Figure 7.** Shown power demand of most of the electrical appliances: (**a**) fridge; (**b**) htpc; (**c**) washer dryer; (**d**) other submeters.

**Table 2.** Twelve load classes considered from the targeted electrical appliances in Table 1 and recognized.

We used the UK-DALE dataset [32] as our reference dataset to experimentally validate the performance of the investigated methodology in load recognition; note that, in the dataset, data that were recorded from House 1 in the UK were considered in this experiment. In this experiment, a total of 4096 (=2*N*(=12)) load combinations need to be recognized by the parallel computing accelerated GA in this paper (the total number of meters installed and used as ground truth in the house environment is 54 [32]), where a total of 208 composite power consumption (NIALM) data instances are disaggregated according to Equation (2). For each data instance acquired at time *t* and disaggregated, the parallel computing accelerated GA indicates the electrical appliances whose operation is active or inactive. In this experiment, the parallel computing accelerated GA is implemented in MATLAB® and run on an Acer Predator G3-710 Intel® CoreTM i7-6700 CPU (3.40 GHz) (RAM: 16 GB) personal computer (PC), where for parallel computing the total number of available workers, *n*, on the machine is four. Note that running the parallel computing accelerated GA requires Global Optimization Toolbox™ [33] required with Parallel Computing Toolbox™ [34] for parallel computing. In this experiment, for simplicity, *ci* in Equation (2) is set to 1.5, which can be determined through an exhaustive search for the house environment. Also, *Pbase* assumed and estimated according to Figure 6b,d is 150.0 watts. Parameters for the parallel computing accelerated GA are specified below. The population size, *pop\_size*, is 250. The initial population is created randomly in bit strings, where the total length of each chromosome is 12. The roulette selection strategy is used, and for this proportional selection procedure raw fitness values based on the rank of evaluated chromosomes, rather than their raw fitness value, are scaled. The single-point crossover operator is conducted; the crossover fraction of the population to be evolved is 0.55. The bit-wise mutation operator is used; the mutation rate is 0.01. An elitist strategy that guarantees a total of top (0.05 × *pop\_size*) chromosomes to survive from their current population to the next population is also used. The maximum number of generations is 50. Finally, the fitness function is clarified as —*E*, where *E* is the declared objective function shown in Equation (2).

Figure 8 shows the evolutionary trajectory of the parallel computing accelerated GA to a disaggregated NIALM data instance. The resulting objective value obtained is 0.7. To the total 208 NIALM data instances where they are run over for load disaggregation, the parallel computing accelerated GA compared to a standard GA achieves, in terms of computation time, an acceleration of up to 3.49 × (=19.57 s/5.60 s).

**Figure 8.** Obtained evolutionary trajectory by the parallel computing accelerated GA in this experiment.

In order to evaluate the performance of the parallel computing accelerated GA in load recognition, we examined the ROC curves, TPR and FPR in Equations (3) and (4), where their Area under ROC (AUC) is shown. Table 3 tabulates the load recognition results obtained by the parallel computing accelerated GA applied on the class-imbalanced data, where the *F*1 score is shown to each load class. Table 4 also tabulates the load recognition results obtained by the parallel computing accelerated GA, where TPR vs. FPR is also shown to each load class. As shown in Table 4, the presented methodology got good load recognition results (against random guesses with AUCs of 0.5) for the fridge, kettle, and dishwasher, where activities of daily living (ADLs) [35] can be inferred from them for occupants in the house. The experimental results reported in this paper have shown the validity of the parallel computing accelerated GA-based NIALM for load disaggregation. In addition, the NIALM's achieved acceleration of up to 3.49× has been shown. The parallel computing accelerated GA can exploit parallel computing and, thus, reduce computation time.  Parallel computing will be exploited massively and the computation time will, thus, be reduced drastically. As shown in Tables 3 and 4, the performance of the presented methodology in load recognition needs a significant improvement, where the presented methodology suffers from similar *P* where the concerned electrical appliances or the load combinations of the concerned electrical appliances are identical. It will be improved. The improvement is shown below.

**Table 3.** Load recognition results obtained by the parallel computing accelerated GA.

1 *F*1 score is the harmonic mean of precision and recall, which is commonly used to evaluate a class-imbalanced problem addressed by a recognizer with a class-imbalanced dataset to be learned.

**Table 4.** Obtained TPRs vs. FPRs, for ROC curves, by the parallel computing accelerated GA, where for the 12 classes the AUCs obtained are also shown.

The performance of the parallel computing accelerated GA in load recognition needs a significant improvement, although the algorithm is capable of recognizing the fridge/freezer, kettle, and dishwasher in the house environment for ADLs. A comparative study is conducted below, where a feed-forward, multilayer ANN as neurocomputing against evolutionary computing is used for the addressed NIALM problem. A network configuration of 1-15-12 of a feed-forward, multilayer ANN in Figure 5a is constructed, specified, and used, in this experiment, to address the same 208 NIALM data instances used before. The feed-forward, multilayer ANN was trained on 135 randomly sampled training data instances (~65% of the whole dataset) and tested on the remaining 73 test data instances. The training trajectory of the constructed, specified and used feed-forward, multilayer ANN is shown in Figure 9. The resulting mean squared error (MSE) is 0.055 (its initial MSE is 0.891). Table 5 tabulates the load recognition results obtained by the feed-forward, multilayer ANN, which has been well trained and validated on the class-imbalanced training data. In Table 5, the *F*1 score is shown to each load class. Table 6 also tabulates the load recognition results obtained by the well-trained and -tested feed-forward, multilayer ANN applied on the class-imbalanced test data. In Table 6, TPR vs. FPR is also shown to each load class. The authors of [24] used the benchmark implementations, from NILMTK in [36], of the combinatorial optimization (CO) approach, which was developed in [1], as a load recognizer to perform load disaggregation for the UK-DALE dataset. A comparison among the CO, the parallel computing accelerated GA, and the feed-forward, multilayer ANN for load recognition is shown in Table 7. As shown in Tables 3–7, the feed-forward, multilayer ANN outperforms, in terms of load

recognition, the parallel computing accelerated GA that is slightly superior in load recognition to the CO.

**Figure 9.** Training trajectory of the feed-forward, multilayer (1-15-12) ANN.

**Table 5.** Load recognition results obtained by the feed-forward ANN where it has been well trained and validated on the class-imbalanced training data.

**Table 6.** Obtained TPRs vs. FPRs, for ROC curves, from the well trained and tested feed-forward, multilayer ANN applied on the class-imbalanced test data; for the 12 classes the AUCs obtained are also shown.

**Table 7.** Comparison among the CO, the parallel computing accelerated GA and the feed-forward, multilayer ANN for load recognition.

1 Combinatorial optimization-based NIALM, which was implemented as a widget in the NILMTK developed for the purpose of performing preliminary work for NIALM data preparation and providing a few load recognition approaches for load disaggregation. 2 Across its all states.

In this experiment, more NIALM data instances, a total of 989 NIALM data instances, are sampled from the house environment and used to experimentally verify the performance of a feed-forward, multilayer ANN (Figure 5a) in terms of load recognition. A network configuration of 1-23-12 of a feed-forward, multilayer ANN was constructed, specified, and used. Also, the feed-forward, multilayer ANN was trained on 643 randomly sampled training data instances (~65% of the whole dataset) and tested on the remaining 346 test data instances. The training trajectory of the feed-forward, multilayer ANN is shown in Figure 10. The resulting MSE was 0.044 (the initial MSE was 1.110). The feed-forward, multilayer ANN can learn from the training data instances across all available CPU workers on the PC used. Table 8 tabulates the load recognition results obtained by the feed-forward, multilayer ANN where it has been well-trained and -validated. In Table 8, the *F*1 score is shown to each load class. Table 9 also tabulates the load recognition results obtained by the well-trained and -validated feed-forward, multilayer ANN applied on class-imbalanced test data. In Table 9, TPR vs. FPR is also shown for each load class.

**Figure 10.** Training trajectory of the feed-forward, multilayer (1-23-12) ANN.

**Table 8.** Load recognition results obtained by the feed-forward ANN where it has been well trained and validated on the class-imbalanced training data.

**Table 9.** Obtained TPRs vs. FPRs, for ROC curves, from the well trained and tested feed-forward, multilayer ANN applied on the class-imbalanced test data; for the 12 classes the AUCs obtained are also shown.

As seen in Table 8, the 989 NIALM data instances make up a class-imbalanced dataset. Accuracy alone is not sufficient for evaluating a recognizer trained from class-imbalanced data, where in each class there may exist a significant disparity between positives (status: On) and negatives (status: Off). As a result, *F*1 score, the harmonic mean of precision and recall, is conducted and used to address the class-imbalanced problem (in order to fully evaluate the performance of the feed-forward ANN in terms of load recognition). Various metrics in addition to *F*1 score have been developed. Table 9 shows TPRs vs. FPRs, for ROC curves, obtained by the well trained and tested feed-forward, multilayer ANN applied on the class-imbalanced test data for NIALM, where one ROC curve can be shown per class (the maximum AUC is 1, which corresponds to a perfect recognizer as all positives above all negatives—100% sensitivity of no false negatives and 100% specificity of no false positives—are ranked). The authors of [24] used two different types of ANNs, deep NNs by autoencoder and long short-term memory (LSTM), as load recognizers to perform load disaggregation for the UK-DALE dataset. Comparison among the autoencoder, the LSTM and the presented feed-forward, multilayer ANN for load recognition is shown in Table 10. As shown in Tables 8–10, the feed-forward, multilayer ANN that gives similar performance, in load recognition, against autoencoder outperforms the LSTM. The load recognizer of the presented NIALM is a shallow neural network, not a deep neural work. As reported in this section, the presented feed-forward, multilayer ANN is able to discriminate the targeted electrical appliances from the house environment well.

**Table 10.** Comparison among the autoencoder, the LSTM and the presented feed-forward, multilayer

1 Across its all states.

### **4.  NIALM, a cost-effective load disaggregation approach for (residential) DSM, is able to disaggregate measured total power consumption into appliance-level power consumption based on unique electrical characteristics (features) extracted from electrical appliances concerned. In this paper, we have presented a parallel computing accelerated GA-based NIALM approach, where the presented methodology has been experimentally validated by the publicly available UK-DALE dataset as a reference. It is necessary to parallelize and thus accelerate metaheuristics by exploiting parallel computing, as metaheuristics such as GAs would require very high computational requirements due to its large amounts of data optimized, population-based candidate solutions evaluated as routines, and/or algorithmic iterations executed repeatedly. Besides the parallel computing accelerated GA-based NIALM approach, a feed-forward, multilayer ANN that can learn from training data instances across all available workers of a parallel pool on a machine in parallel computing addresses the same NIALM problem. Therefore, we performed a comparative study. Where, different load recognition approaches in the literature were compared. Comparison among the CO, the parallel computing accelerated GA and the feed-forward, multilayer ANN was shown. The feed-forward, multilayer ANN outperformed, in load recognition, the parallel computing accelerated GA that was slightly superior in load recognition to the CO. Moreover, a comparison among the autoencoder, LSTM, and feed-forward, multilayer ANN was shown. The feed-forward, multilayer ANN that gave similar performance, in load recognition, against the autoencoder outperformed the LSTM. As reported in this paper, the presented NIALM methodology whose performance in load recognition has been improved and compared is able to recognize the targeted/concerned electrical appliances from the house environment well for a future research direction of NIALM in ADLs. In this paper, AI speeded up in parallel computing has been developed and suited for NIALM. In the future, an additional electrical feature or more features will be considered for load disaggregation (Equation (2)). Moreover, high-performance distributed computing harnessing graphics processing units (GPUs) will be developed for the presented methodology in NIALM (batch load disaggregation) for its large-scale evaluation.

**Author Contributions:** Y.-C.H. conceived, designed, and performed the experiments as well as contributed related experimental tools/materials to analyze the experimental data. Y.-H.L. conceived, designed, and performed the experiments as well as contributed related experimental tools/materials to analyze the experimental data. Y.-H.L. also wrote the paper. C.-H.L. conceived, designed, and performed the experiments as well as contributed related experimental tools/materials to analyze the experimental data. All authors have read and agreed to the published version of the manuscript.

**Funding:** The Ministry of Science and Technology, Taiwan, under grant nos. MOST 109-3116-F-006-017-CC2 and MOST 109-2221-E-131-006-MY2 partly supported the work that has been done in this paper. First International Computer, Inc. (FIC), Taiwan, under the Industry-Academia Collaboration Project with grant no. O01109E048 partly supported the work as well.

**Acknowledgments:** The authors would like to sincerely thank the reviewers and editor for their valuable comments and suggestions on this paper.

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

### **References**

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**A Psycholinguistic Perspective on Development of Phonetic Category Formation in Bilingual Children Phonetic Category Formation in Bilingual Children**

**A Psycholinguistic Perspective on Development of**

DOI: 10.5772/intechopen.72112

Sue Ann S. Lee Sue Ann S. Lee Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72112

#### **Abstract**

This chapter reviews theories and research about phonetic category formation in bilingual children. Investigating phonetic categories provides us with a way to answer one of the longstanding theoretical issues in bilingualism, that is, whether bilingual children possess one versus two linguistic systems in the learning of their respective languages. In this chapter, theoretical backgrounds of phonetic categories in bilingual adults and children are reviewed. Then, empirical evidence showing phonetic categories in bilingual children is summarized. Finally, a development model of phonetic category formation in simultaneous and sequential bilingual children is proposed. Based on the model, detailed phonetic categories do not form across-the-board and bilingual children may invoke multi-dimensional representations of phonetic categories.

**Keywords:** phonetic category, stop consonants, vowels, acoustics, bilingual children


*Edited by Mohammed Muzibur Rahman, Abdullah Mohammed Asiri and Mohammad Asaduzzaman Chowdhury*

*Carbon Nanotubes - Recent Advances, New Perspectives and Potential Applications*  covers all the important nanotechnological aspects of carbon materials, including their growth and characterization, processing and fabrication, development and potential applications. Recent developments, innovative preparation methods and promising new frontiers around this advanced nanotechnology are reviewed under the editorship of an internationally renowned scientist. This book will be useful to a diverse range of readers and will be important for research organizations, government research centers, academic libraries, and those involved in the research and development of carbon materials.

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Carbon Nanotubes - Recent Advances, New Perspectives and Potential Applications

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**2. System description**

As illustrated in **Figure 5**, the configuration consists of four circuits: an ORC cycle circuit represented by the red color, a circuit of the VCC cycle which is in blue, a circuit in purple color of the desalinated seawater, and a red circuit in the heated water. We will couple our facility with a limited renewable energy source which is thermal photovoltaic center, at low temperature (100–130°C). Our approach is to lower the condensing temperature between 10 and 10°C of the ORC cycle so that the delivered work can be increased. So, a cold part produced by VCC will be dedicated to condense the fluid of the ORC cycle. For this, we will integrate an exchanger regenerator1 which is used to condense the ORC fluid by a quantity of cold produced laying vapor phase VCC side.

**Figure 5.** *System of study.*

• **Mode 3**: cogeneration (cold production and electricity power). **Figure 3**

electric power by ORC cycle and cooling capacity by the VCC cycle.

system with a limited renewable energy source.

working fluid as the classic systems.

and biomass.

**4**

**Figure 4.**

*Electrodialysis*

*Tri-generation and desalination mode.*

presents the basic architecture. It receives an external renewable source in the boiler. Through this source, it allows us to have mechanical work at the turbine: this is partially transmitted to the VCC cycle compressor as mode 2. The power provided by the VCC cycle evaporator is partially operated by the condenser ORC cycle. So this operation mode requires a renewable source and offers an

• **Mode 4**: tri-generation and desalination of seawater are illustrated in **Figure 4**. This configuration has four circuits: an ORC cycle circuit that is represented by the red color, a circuit of the VCC cycle in blue, a circuit in purple color of the desalinated seawater, and a red circuit of the heated water. We will couple the

In addition, each installation mode has several configurations depending on the recovery points that will be integrated later, besides its adaptation to any energy source, where we can use biomass, solar, and heat rejects of industry at low temperatures (60–130°C). This system could produce a negative and a positive cold. Although, due to its architecture, it is also characterized by many combinations of selection fluid for the ORC and VCC cycles, it is not necessary to have the same

The main purpose of this presented study is to analyze the performance of a new system that combines the steam compression cycle and the Rankine cycle for trigeneration (electricity, cold, hot) as well as the desalination of water. This system uses a low-temperature heat source such as solar energy, heat from industrial waste,

#### **2.1 Desalinated water circuit description**

First, the seawater is pumped by a pump PMP1 and preheated by the **exchanger 3**. Then, it will be evaporated at constant pressure in the boiler by a solar collector. After having saturated steam, the latter passed the condensation phase using the **regenerator 2** in order to equate the water at a hot temperature which is equal to that of the evaporation. With integrity of exchanger H2, we started the first phase of cooling the salty water and chaffered sanitary water. In the end, for the desalted water to complete this phase of cooling, also we have to warm the water out of the sea, using an exchanger H3.

#### **2.2 Circuit description of heated domestic water**

This is the simplest circuit in our loop. It is enough the sanitary water enters the exchanger H3 to become hot thanks to the quantity of heat provided by the desalted water.

So our system produces electricity due to the mechanical work obtained by the turbine, a refrigeration quantity by the evaporator 1, and de-watered water obtained using two serial transformations (evaporation, condensation) and produces hot water by the exploitation of the hot quantity from the de-watered water.

## **3. The different configurations developed for ORC-VCC combination**

#### **3.1 Configuration A**

The cycle A is the basic configuration. We will combine the two ORC and VCC cycles without any recovery for cogeneration. As shown in **Figure 6**, the only combination is made at the heat exchanger H1 which serves as the condenser of the ORC cycle fluid. This configuration allows having cogeneration with positive or negative cold according to our needs.

almost the same as the temperature of condensation which varies between 10 and 10°C, the idea is to exploit this temperature to make the sub-cooling of the VCC cycle to improve its performance. Cycle B is shown in **Figure 7**, and it is also

*Schematic and T-S diagrams of the configuration A. (a) Schematic of the configuration A, (b) T-S diagrams*

*Performance Analysis of a New Combined Organic Rankine Cycle and Vapor Compression…*

As shown in **Figure 8**, cycle C is used also for cogeneration. Unlike the conventional ORC cycle, which is used only for electricity production at the turbine state, cycle C allows the generation of electricity and cold in the ORC cycle. So, the configuration C is used to produce negative cold, positive cold, and electricity.

We will use the heat quantity at low temperature following the pumping step in the ORC cycle in order to produce a positive cold at 18°C for air conditioning. For this reason, we will integrate the H3 exchanger for the heat transfer between the

During this study, we treated the thermodynamic equations as well as the resolution by a calculation program developed by the EES software. Also, this software

allows us to realize the different curves and tables presented in the study.

developed to make cogeneration with a negative cold.

*for ORC cycle, and (c) P-H diagrams for VCC cycle.*

*DOI: http://dx.doi.org/10.5772/intechopen.91871*

**4. Mathematical modeling and validation of the model**

**3.3 Configuration C**

**Figure 6.**

ambient air and the ORC cycle fluid.

**4.1 Thermodynamic modeling**

**7**

The operating principle is described as follows:

First, the ORC cycle fluid enters the boiler in order to heat it up to 100°C by a renewable external source (biomass, industrial and solar thermal discharge, etc.). It suffices that the fluid reaches a saturated vapor phase; it enters a turbine to generate a mechanical work. This phase allows the fluid to pass from the high pressure to the low pressure. After this phase, a condensation phase is necessary to make the fluid in a liquid state. For our application, the condensation is done at low temperature which requires a cold external source. For this, we combined the ORC cycle condenser with the VCC cycle evaporator by integrating a H1 exchanger. For this configuration, after condensation, the fluid goes to the pumping phase.

In addition, the VCC cycle operation is the inverse of those ORC cycle. The VCC fluid is compressed with a mechanical compressor and then condensed at a temperature of 30°C. In this configuration, after this phase, the fluid is released directly by an expansion valve. Then it is evaporated in two phases.

#### **3.2 Configuration B**

For cycle B, we kept the same basic architecture as in cycle A, except that we will integrate an H2 exchanger. This exchanger is mounted just after the pumping phase of the ORC cycle. Seeing that the temperature obtained at the pumping point is

*Performance Analysis of a New Combined Organic Rankine Cycle and Vapor Compression… DOI: http://dx.doi.org/10.5772/intechopen.91871*

#### **Figure 6.**

**2.1 Desalinated water circuit description**

**2.2 Circuit description of heated domestic water**

sea, using an exchanger H3.

**3.1 Configuration A**

pumping phase.

**3.2 Configuration B**

**6**

negative cold according to our needs.

The operating principle is described as follows:

by an expansion valve. Then it is evaporated in two phases.

water.

*Electrodialysis*

First, the seawater is pumped by a pump PMP1 and preheated by the **exchanger 3**. Then, it will be evaporated at constant pressure in the boiler by a solar collector. After having saturated steam, the latter passed the condensation phase using the **regenerator 2** in order to equate the water at a hot temperature which is equal to that of the evaporation. With integrity of exchanger H2, we started the first phase of cooling the salty water and chaffered sanitary water. In the end, for the desalted water to complete this phase of cooling, also we have to warm the water out of the

This is the simplest circuit in our loop. It is enough the sanitary water enters the exchanger H3 to become hot thanks to the quantity of heat provided by the desalted

So our system produces electricity due to the mechanical work obtained by the

**3. The different configurations developed for ORC-VCC combination**

cycles without any recovery for cogeneration. As shown in **Figure 6**, the only combination is made at the heat exchanger H1 which serves as the condenser of the ORC cycle fluid. This configuration allows having cogeneration with positive or

The cycle A is the basic configuration. We will combine the two ORC and VCC

First, the ORC cycle fluid enters the boiler in order to heat it up to 100°C by a renewable external source (biomass, industrial and solar thermal discharge, etc.). It suffices that the fluid reaches a saturated vapor phase; it enters a turbine to generate a mechanical work. This phase allows the fluid to pass from the high pressure to the low pressure. After this phase, a condensation phase is necessary to make the fluid in a liquid state. For our application, the condensation is done at low temperature which requires a cold external source. For this, we combined the ORC cycle condenser with the VCC cycle evaporator by integrating a H1 exchanger. For this configuration, after condensation, the fluid goes to the

In addition, the VCC cycle operation is the inverse of those ORC cycle. The VCC fluid is compressed with a mechanical compressor and then condensed at a temperature of 30°C. In this configuration, after this phase, the fluid is released directly

For cycle B, we kept the same basic architecture as in cycle A, except that we will integrate an H2 exchanger. This exchanger is mounted just after the pumping phase of the ORC cycle. Seeing that the temperature obtained at the pumping point is

turbine, a refrigeration quantity by the evaporator 1, and de-watered water obtained using two serial transformations (evaporation, condensation) and produces hot water by the exploitation of the hot quantity from the de-watered water.

*Schematic and T-S diagrams of the configuration A. (a) Schematic of the configuration A, (b) T-S diagrams for ORC cycle, and (c) P-H diagrams for VCC cycle.*

almost the same as the temperature of condensation which varies between 10 and 10°C, the idea is to exploit this temperature to make the sub-cooling of the VCC cycle to improve its performance. Cycle B is shown in **Figure 7**, and it is also developed to make cogeneration with a negative cold.

#### **3.3 Configuration C**

As shown in **Figure 8**, cycle C is used also for cogeneration. Unlike the conventional ORC cycle, which is used only for electricity production at the turbine state, cycle C allows the generation of electricity and cold in the ORC cycle. So, the configuration C is used to produce negative cold, positive cold, and electricity.

We will use the heat quantity at low temperature following the pumping step in the ORC cycle in order to produce a positive cold at 18°C for air conditioning. For this reason, we will integrate the H3 exchanger for the heat transfer between the ambient air and the ORC cycle fluid.

#### **4. Mathematical modeling and validation of the model**

#### **4.1 Thermodynamic modeling**

During this study, we treated the thermodynamic equations as well as the resolution by a calculation program developed by the EES software. Also, this software allows us to realize the different curves and tables presented in the study.

**Table 1** illustrates the different thermodynamic models used throughout the

*Performance Analysis of a New Combined Organic Rankine Cycle and Vapor Compression…*

The approach followed to validate our model is based on two procedures of the developed model. The ORC and the VCC are validated, respectively, in Sections

The model developed for the ORC is tested with the results by Saleh et al. [29], which is the most appropriate configuration to validate the current model using the

Boiler (1) *Qb* ¼ *m*\_ 1ð Þ *h*3 � *h*2

Condenser *Qcond* ¼ *m*\_ 1ð Þ *h*4 � *h*1 Thermal efficiency μ*orc* ¼ ð Þ *WT*�*Wp*

First evaporator *Qev*1 ¼ *m*\_ 2ð Þ *h*14 � *h*13 Second evaporator *Qev*2 ¼ *m*\_ 2ð Þ *h*10 � *h*14

Net work *Wnet* ¼ *WT* � *Wp* � *WC*

Efficacity (1 and 2) E ¼ *Qev*1

*Thermodynamic modeling of different configurations ((1) cycle A; (2) cycle B, and (3) cycle C).*

Overall evaporator *Qev* ¼ *m*\_ 2ð Þ¼ *h*10 � *h*13 *Qev*1 þ *Qev*2

ð Þ *h*2*s*�*h*1 ƞ*pump*

*Qb*

*COPVCC* ¼ *Qev*1þ*Qev*2 ð Þ *Wcomp*

(3) E ¼ *Qev*1þ*Qhx*3 *Wnet*

*R*2 ¼ *m*1*=m*4 *R*3 ¼ *m*2*=m*4 *R*4 ¼ *m*3*=m*4

(1 and 2) *COPs* ¼ ð Þ *Qev*1þ*Wnet*

*Wnet*

Exchanger 1 *Qhx*1 ¼ *Qev*2 ¼ *Qcond*

*Qexh*3 ¼ *m*\_ 1ð Þ¼ *h*5 � *h*4 *m*\_ 3ð Þ *h*18 � *h*17 *Qexh*4 ¼ *m*\_ 3ð Þ¼ *h*19 � *h*18 *m*\_ 4ð Þ *h*22 � *h*21 *Qexh*5 ¼ *m*\_ 3ð Þ¼ *h*20 � *h*19 *m*\_ 3ð Þ *h*15 � *h*14

(3) *COPs* ¼ *Qev*1þ*Wnet*þ*Qhx*3

*Qb*

Exchanger 2 *Qhx*2 ¼ *m*\_ 1ð Þ¼ *h*5 � *h*2 *m*\_ 2ð Þ *h*12 � *h*15 Exchanger 3 *Qhx*2 ¼ *m*\_ 1ð Þ¼ *h*5 � *h*2 *m*\_ 2ð Þ *h*16 � *h*17

*Qb*

ð Þ *h*11*s*�*h*10 ƞ*comp*

(2) *Qb* ¼ *m*\_ 1ð Þ *h*3 � *h*5 (3) *Qb* ¼ *m*\_ 1ð Þ *h*3 � *h*6

work and the different configurations for cogeneration mode.

ORC Turbine *WT* ¼ *m*\_ 1ð Þ *h*3 � *h*4*s :*ƞ*exp*

VCC Compressor *WC* ¼ *m*\_ 2

Coefficient of performance

the system

Exchangers for cogeneration

Exchangers for trigeneration

Mass ratio *R*1 ¼ *m*3*=m*4

Overall performance of

Pump *Wp* ¼ *m*\_ 1

**4.2 Validation of the model**

*DOI: http://dx.doi.org/10.5772/intechopen.91871*

4.2.1 and 4.2.2.

*4.2.1 ORC validation*

Overall performance of

ORC-VCC

**Table 1.**

**9**

#### **Figure 7.**

*Schematic and T-S diagrams of the configuration B. (a) Schematic of the configuration B, (b) T-S diagrams for ORC cycle, and (c) P-H diagrams for VCC cycle.*

#### **Figure 8.**

*Schematic and T-S diagrams of the configuration C system. (a) Schematic of the configuration C, (b) T-S diagrams for ORC cycle, and (c) P-H diagrams for VCC cycle.*

*Performance Analysis of a New Combined Organic Rankine Cycle and Vapor Compression… DOI: http://dx.doi.org/10.5772/intechopen.91871*

**Table 1** illustrates the different thermodynamic models used throughout the work and the different configurations for cogeneration mode.

### **4.2 Validation of the model**

The approach followed to validate our model is based on two procedures of the developed model. The ORC and the VCC are validated, respectively, in Sections 4.2.1 and 4.2.2.

#### *4.2.1 ORC validation*

The model developed for the ORC is tested with the results by Saleh et al. [29], which is the most appropriate configuration to validate the current model using the

**Table 1.**

*Thermodynamic modeling of different configurations ((1) cycle A; (2) cycle B, and (3) cycle C).*

**Figure 7.**

*Electrodialysis*

**Figure 8.**

**8**

*ORC cycle, and (c) P-H diagrams for VCC cycle.*

*Schematic and T-S diagrams of the configuration B. (a) Schematic of the configuration B, (b) T-S diagrams for*

*Schematic and T-S diagrams of the configuration C system. (a) Schematic of the configuration C, (b) T-S*

*diagrams for ORC cycle, and (c) P-H diagrams for VCC cycle.*

**5. Selection of the working fluid**

*DOI: http://dx.doi.org/10.5772/intechopen.91871*

is isentropic like R600 and R600a.

than 0.95 of steam rate.

**Figure 9.**

**11**

*The three classes on a T-S diagram.*

The choice of the working fluid for an ORC or VCC cycle is an important criterion to improve the cycle performances. Generally, there are three families of organic fluids. **Figure 9** shows these three classes on a T-S diagram. The distinction between these different types essentially depends on the slope between the saturation temperature and the isentropic variation (ΔT/Δs). If a negative slope is said, the fluid is wet, such as H2O, NH3, and R134a. For a positive slope, we speak of a dry fluid such as benzene and pentane. In cases where the slope is infinite, it is said that this fluid

*Performance Analysis of a New Combined Organic Rankine Cycle and Vapor Compression…*

For the ORC cycle is to have fluid hot admits a weak latent heat in the evaporator, in order to minimize the quantity received by the boiler. Thus, a low latent heat in the condenser minimizes the amount of cold delivered by the VCC cycle. In addition, we are looking for a fluid with a positive slope to avoid vapor having less

We guarantee the elimination of the oxidation effect in the turbine especially that we will make it lower concerning the condensation temperature to 10°C. Based on these criteria and conditions mentioned above, it is necessary to choose a dry or isentropic ORC cycle fluid. We choose the n-hexane; the chemical formula is C6H14. The thermophysical characteristics of this fluid are presented in **Table 4**. The R600 is selected as a working fluid for the VCC cycle. It is a hydrocarbon of formula C4H10 crude which is found in the gas status under normal conditions of temperature and pressure. The physical characteristics of this fluid are presented in **Table 4**. Furthermore, our choice is toward the use of n-hexane for the ORC cycle. This choice is essentially due to the steam rate which is equal to 1 even when the condensation temperature is lowered to a low degree. This allows us to have a margin of confidence and turbine safety (avoid the effect of oxidation). During our

study, we chose the R600 as a working fluid for the VCC cycle. This fluid is characterized by its robustness in the market, so it is used in recent years in several

researches. In addition, we find that the environmental damage is minimal.

set some parameters and define their limits. For example, the network and

To reassure the efficiency and rentability of the system, it is necessary that we

**6. System settings and boundary conditions**

#### **Table 2.**

*Validation results for ORC cycle.*

similar working applied fluid. The comparative results are illustrated in **Table 2**. These results show a small deviation of 2.09% concerning the thermal efficiency. It is worthy to notice that certain changes in the developed model are made for an appropriate comparison. Specifically, the condensation temperature was 40°C and the isentropic efficiency at 85%.


*Edited by Ahmed R. G.*

The World Health Organization estimated that each year there are more than 13 million deaths caused by environmental causes. Exposure to endocrine disrupting chemicals (EDCs) during development may cause long-term health outcomes. This book, Endocrine Disruptors, includes eight chapters that illustrate potential endocrinedisrupting activities in water, sediments, crops, animals, and humans. This book assesses the relationship between the EDCs and development, reproduction, or obesity. Finally, detection of the levels of EDCs by autobioluminescent cellular bioreporters is discussed. Scientists, physicians, neuroendocrinologists, neurotoxicologists, and lay readers who have engaged in EDC studies or practice will discover that this book offers insight into all areas of EDC research.

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