Shannon Entropy Method Research Articles

THE CODIFICATION AND RANKING OF DEVELOPMENT STRATEGIES OF IRON AND STEEL INDUSTRIES IN AFGHANISTAN, WITH REGARDS TO INPUTS REQUIREMENTS AND USE OF THE SWOT TECHNIQUE, COMBINING SHANNON ENTROPY AND PROMETHEE METHODS

Considering the growth of iron and steel industry in the world, existence of iron ore rich resources, abundance of inputs required by the steel industries and higher demands for steel in Afghanistan, the idea of analyzing the strategy of iron and steel industries for codifying and defining of a development strategy in field of respective industries has been created in Afghanistan. Due to, the first strategy of iron and steel industries in the world and in selected countries (China, Iran, India and Pakistan) has been analyzed and evaluated. With the help of library studies, bylaw appraisal and interviews with experts, the effective internal and environmental factors for establishment of iron and steel industries in Afghanistan has been analyzed and determined. Based on the weaknesses and threats of selected countries regarding steel and iron industries and the current internal and environmental factors in Afghanistan, eight bold categories of strategy in respective of development projects in Afghanistan has been defined and codified by using SWOT technique. The Ranking of strategies have been done by using Shannon entropy and Promethee methods. As the result ST2 Strategy placed on first and SO2 Strategy placed on last position. KEYWORDS: Strategic Analysis, SWOT, Shannon entropy method and Promethee method.

Analyzing the Position of Safety Culture Education in 2019-2020 Elementary School Textbooks in Iran's Education System

INTRODUCTION: The natural hazard reduction curriculum is incredibly critical in facilitating and improving students' safety culture. Thus, we must attend to the content of textbooks to further comprehend the significance of safety issues regarding the process of student safety. The next step is developing a training program that meets all the fundamental needs of safety education. Hence, this research study strives to evaluate the position of safety culture education in elementary school textbooks. METHODS: This research is a quantitative content analysis borrowing a qualitative approach. The statistical population comprised all books on Persian language and literature, experimental sciences, and social studies in all the six primary levels. Counting was the sampling method in the present study. Experts in curriculum planning and health education confirmed the validity of this research. Moreover, the Scott test obtained a reliability of 0.72. FINDINGS: The findings of content analysis in assessing elementary school textbooks based on the Shannon Entropy method indicated that the following indices have the highest frequency and significance: Risk and disaster awareness in 150 samples (Wj = 0.273(, Communication skills, awareness of the environment, and human-environmental interactions with 46 samples (Wj = 0.0666(, On the contrary, the index of child rights aspects with zero cases (Wj = 0) was the least significant among the indices and components.Findings from the frequency of components revealed that safety culture components were not normally distributed. CONCLUSION: According to the research findings, we found out that the components in the fields of skill and attitude have received little to no attention. However, nurturing and promoting the capabilities and skills of children in the field of safety are some of the most vital indices of child safety. Contemplating these aspects will assist in properly performing relief, emergency, and law enforcement missions by preventing disasters

A Framework for Time-Cost-Quality Optimization in Project Management Problems Using an Exploratory Grid Concept in the Multi-Objective Simulated-Annealing

Time, cost, and quality are the three indispensable factors for the realization and success of a project. In this context, we propose a framework composed of a multi-objective approach and multi-criteria decision-making methods (MCDM) to solve time-cost-quality trade-off optimization problems. A multi-objective Simulated Annealing (MOSA) algorithm is used to compute an approximation to the Pareto optimal set. The concept of the exploratory grid is introduced in the MOSA to improve its performance. MCDM are used to assist the decision-making process. The Shannon entropy and AHP methods assign weights to criteria. The first methodology is for the inexperienced decision-makers, and the second concedes a personal and flexible weighting of the criteria weights, based on the project manager’s assessment. The TOPSIS and VIKOR methods are considered to rank the solutions. Although they have the same purpose, the rankings achieved are different. A tool is implemented to solve a time-cost-quality trade-off problem on a project activities network. The computational experiments are analyzed and the results with the exploratory grid in Simulated Annealing (SA) are promising. Despite the framework aims to solve multi-objective trade-off optimization problems, supporting the decisions of the project manager, the methodologies used can also be applied in other areas.

Four-Objective Optimizations for an Improved Irreversible Closed Modified Simple Brayton Cycle.

An improved irreversible closed modified simple Brayton cycle model with one isothermal heating process is established in this paper by using finite time thermodynamics. The heat reservoirs are variable-temperature ones. The irreversible losses in the compressor, turbine, and heat exchangers are considered. Firstly, the cycle performance is optimized by taking four performance indicators, including the dimensionless power output, thermal efficiency, dimensionless power density, and dimensionless ecological function, as the optimization objectives. The impacts of the irreversible losses on the optimization results are analyzed. The results indicate that four objective functions increase as the compressor and turbine efficiencies increase. The influences of the latter efficiency on the cycle performances are more significant than those of the former efficiency. Then, the NSGA-II algorithm is applied for multi-objective optimization, and three different decision methods are used to select the optimal solution from the Pareto frontier. The results show that the dimensionless power density and dimensionless ecological function compromise dimensionless power output and thermal efficiency. The corresponding deviation index of the Shannon Entropy method is equal to the corresponding deviation index of the maximum ecological function.

Locating suitable sites for rainwater harvesting (RWH) in the central arid region of Iran

Rainwater harvesting is an encouraging instrument for augmenting surface and ground waters to overcome the inequality between water demand and supply, growing due to the rising need for water. This study offers an approach that can enable water managers to use the GIS to determine the most suitable location for rainwater harvesting by employing the TOPSIS multi-criteria decision analysis and applying its coefficients to zoning the area. To select the most effective indices for locating water harvesting in the region, 31 indices were determined, based on the studies done in this field in the world. For evaluating each index, four criteria, including suitability for purpose, ease and accuracy, cost, time and acceptance rate in local communities, were weighted by the Shannon entropy method. Then, using the TOPSIS ranking algorithm, the most effective indices were used to locate water harvesting. Using Geographic Information System (GIS), a map was developed for each index given a numerical value between 1 and 4. Finally, the map of rainwater harvesting locations was prepared in two ways: the geometric mean of eight indices and applying TOPSIS coefficients of the eight effective indices. Results showed that the time criterion with the weight of 0.306 was the most important. The ease and accuracy criterion with the value of 0.128 was the least important to select rainwater harvesting indices. Of the 31 indices, eight indices were selected as the most effective indices and classified into four classes: hydrology, soil, vegetation, and climate. Results of the rainwater harvesting potential map were classified into two categories of medium and high. In contrast, the TOPSIS coefficients’ application was classified into three high, high, and medium classes. Based on this map, 1.38% of the area located in a very high class was found very suitable for rainwater harvesting.

A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using Shannon Entropy Method

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables’ (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS’s spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast.

The Analysis of the Curriculum for Empirical Science at Fourth, Fifth and Sixth Grades of Elementary School, Based on Learning Styles

Objective: The purpose of this study was to analyze the curriculum of empirical science at the fourth, fifth, and sixth grades of elementary school based on Vark's learning styles. Materials and Method:It was a content analysis descriptive research using Shannon entropy method. The statistical population included the curriculum (purpose, content, methodology and evaluation) of the fourth, fifth, and sixth grades of the elementary school. The statistical sample of curricula is social studies subject. In order to analyze the existing curriculum (intended) with each learning style, the objectives of the textbooks were first studied, then the content of the textbook was examined. And in order to review the proposed methods, we analyzed the teaching manual. In the evaluation section, the questions raised in the textbooks and the teaching guide were analyzed. The research tool was Vark's Learning Styles Checklist (1998), which was developed based on the VARK Learning Styles Questionnaire and questionnaire and provided coding analysis to three researchers in planning to ensure the reliability of the obtained analyzes. A lesson was agreed and the percentage of agreement was estimated to be 0.88. Result and Discussion: The results of data analysis showed that in the curriculum of empirical science at the fourth and sixth elementary grades, the visual style was first and the Read/write style in the fifth rank. In fifth grade, the auditory style was first, and the combinative style in the fifth rank. Therefore, it can be said that the preferred style in the empirical science curriculum is the visual style in the fourth and sixth grades and the auditory style in the fifth grade.

A cost allocation model based on the combination of data envelopment analysis and Shannon entropy (case study: branches of the central post of Isfahan)

The method used for allocation of costs in this study has several stages. In stage 1, various types of data envelopment analysis (DEA) models were used to assess the efficiency of decision-making units (DMUs), including constant return to scale and variable return to scale with a variety of input and output types in radial and non-radial states. In stage 2, Shannon entropy method was used to combine the obtained efficiencies for each decision making unit. In stage 3, the allocation of costs was done based on the combined efficiency number for each unit obtained in phase 2. The proposed cost allocation model was then implemented in an example and its fairness was compared with similar methods using the Gini coefficient method. Finally, the proposed model was investigated in Central Post of Isfahan.

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.

The Codification and Ranking of Development Strategies of Iron and Steel Industries in Afghanistan, with Regards to Inputs Requirements and Use of the Swot Technique, Combining Shannon Entropy and Promethee Methods

The Codification and Ranking of Development Strategies of Iron and Steel Industries in Afghanistan, with Regards to Inputs Requirements and Use of the Swot Technique, Combining Shannon Entropy and Promethee Methods

Multicriteria comprehensive evaluation framework for industrial park-level distributed energy system considering weights uncertainties

Distributed energy systems have been proved as an effective solution to energy depletion and environmental pollution. However, the decision making on the distributed energy system planning is difficult because multiple evaluative criteria and alternative systems are involved in the evaluation process. The study, therefore, is aimed at proposing a multicriteria comprehensive evaluation framework for the DES plan. First, the evaluation criteria system is established to offer a fundamental premise for the fair evaluation, based on numerous researches on DES evaluation. In the established criteria system, technology, economy, environment, and socio-politics are scrutinized. Second, the subjective weights, objective weights and the comprehensive weights of criteria are determined by the fuzzy analytic hierarchy process method, Shannon entropy method and a comprehensive method, respectively. The fuzzy analytic hierarchy process method combines the triangular fuzzy number and the analytic hierarchy process method. The triangular fuzzy number incorporates the decision-makers’ uncertainties during evaluation and thus improves the efficiency of decision making. The comprehensive method combines the single-objective optimization and Jaynes’ maximal entropy principle method. Third, the grey relational analysis method is used to achieve the alternatives’ ranking with and without preferences. The proposed framework is employed to evaluate various distributed energy systems for a cultural industrial park in Changsha of China. Results show that the system that integrates water source heat pump with the improved Combined Cooling, Heating & Power (WSHP + CCHP) is optimal in the integrated evaluation and the evaluations with technology priority and economy priority, with a grey relational degree of 0.7475, 0.7239, and 0.5764, respectively. The three systems that exploit renewable energy, i.e. the system that integrates ground source heat pump with the improved Combined Cooling, Heating & Power (GSHP + CCHP), the system that integrates water source heat pump with the improved Combined Cooling, Heating & Power (WSHP + CCHP) and the system that ground source heat pump with water source heat pump (GSHP + WSHP), have a better ranking than the other two systems in the evaluation with socio-political priority. The case study of the park demonstrates the proposed framework’s feasibility of multi-criteria comprehensive evaluation for industrial park-level distributed energy systems. The study hopes to provide some references for the multicriteria evaluation of distributed energy systems.

Power, Efficiency, Power Density and Ecological Function Optimization for an Irreversible Modified Closed Variable-Temperature Reservoir Regenerative Brayton Cycle with One Isothermal Heating Process

One or more isothermal heating process was introduced to modify single and regenerative Brayton cycles by some scholars, which effectively improved the thermal efficiency and significantly reduced the emissions. To analyze and optimize the performance of this type of Brayton cycle, a regenerative modified Brayton cycle with an isothermal heating process is established in this paper based on finite time thermodynamics. The isothermal pressure drop ratio is variable. The irreversibilities of the compressor, turbine and all heat exchangers are considered in the cycle, and the heat reservoirs are variable-temperature ones. The function expressions of four performance indexes; that is, dimensionless power output, thermal efficiency, dimensionless power density and dimensionless ecological function are obtained. With the dimensionless power density as the optimization objective, the heat conductance distributions among all heat exchangers and the thermal capacitance rate matching among the working fluid and heat reservoir are optimized. Based on the NSGA-II algorithm, the cycle’s double-, triple- and quadruple-objective optimization are conducted with the total pressure ratio and the heat conductance distributions among heat exchangers as design variables. The optimal value is chosen from the Pareto frontier by applying the LINMAP, TOPSIS and Shannon entropy methods. The results show that when the pressure ratio in the compressor is less than 12.0, it is beneficial to add the regenerator to improve the cycle performance; when the pressure ratio is greater than 12.0, adding the regenerator will reduce the cycle performance. For single-objective optimization, the four performance indexes could be maximized under the optimal pressure ratios, respectively. When the pressure ratio is greater than 9.2, the cycle is simplified to a closed irreversible simple modified Brayton cycle with one isothermal heating process and coupled to variable-temperature heat reservoirs. Therefore, when the regenerator is used, the range of pressure ratio is limited, and a suitable pressure ratio should be selected. The triple objective (dimensionless power output, dimensionless power density and dimensionless ecological function) optimization’ deviation index gained by LINMAP or TOPSIS method is the smallest. The optimization results gained in this paper could offer some new pointers for the regenerative Brayton cycles’ optimal designs.

A Novel Excellence Model of the ICT Industry: Case Study on Telecommunications Backbone Network of Iran

Todays, Information and Communications Technology (ICT), as one of the most effective factors of the advancement in all the aspects such as governance, security and risk, economics and technology, which is among the industries rapidly growing and developing. This paper examines the specific criteria and standards of ICT area for improving the quality of the services of the Telecommunications Backbone Network of Iran (TBNI). The research was done through the two methods of data collection, library and the field study. European Foundation for Quality Management Excellence Model (EFQM) was selected as the base model and with full attention to the specific requirements of ICT industry and due to adapt this model from the viewpoints of the security and risk, governance and economic, a novel integrated excellence model of TBNI was developed and customized. To achieve the highest level of reliability and internal coherence of each criterion, based on expert opinion polls, sub-criteria was categorized and analyzed in several stages using the factor analysis approach. The causal relationships and the influence of the criteria of this model on each other were investigated and the weights of each criterion and sub-criteria, using the structural analysis and shannon entropy methods, were determined. The self-evaluation of TBNI was systematically carried out and major opportunities for the improvement had been identified. Results showed that the establishment of this model had led to an improvement in a quality of services provided by this network and the products of all organizations that use any services of TBNI.

Urban Change Detection Analysis during 1978–2017 at Kolkata, India, using Multi-temporal Satellite Data

This paper focused on urban change detection and growth pattern analysis for the period of 1978–2017 at Kolkata using remote sensing data and GIS. The supervised Maximum Likelihood Classification technique is used to classify the multi-temporal satellite data in five classes which are urban built-up, open land, vegetation, agricultural land and water body. Results revealed that urban built-up area has progressively increased by about 21.17% (239.097 km2) during study period due to the new construction of roads, flyovers, settlements, etc. Other geographical features such as open land, vegetation, agricultural land and water body have gradually declined. To assess the accuracy of classification, 88.27%, 92.42%, 91.62%, 90.18% and 89.34% overall accuracy and 0.851, 0.904, 0.893, 0.875 and 0.866 Kappa statistic have been achieved for the images of 1978, 1988, 2000, 2010 and 2017, respectively. The degree of magnitude of urban sprawl has measured using the Shannon entropy method which demonstrates that the overall entropy values are progressively increased throughout the entire region that means the urban built-up is gradually extended in different positions. Moreover, entropy outcomes revealed that urban development occurred more in the northern and southern regions as compared with the other regions. From this study, four urban growth patterns have been found which are low density continuous, continuous linear, noncontiguous linear, and leapfrog development. The important patterns are continuous linear and noncontiguous linear because most of the urban development happened along the sides of the major roads or highways. Future prediction has been obtained using CA–Markov chain model and estimates that the urban built-up may be increased by about 56.18% (509.82 km2) in the year of 2031.

Spatiotemporal analysis of urban growth on forest and agricultural land using geospatial techniques and Shannon entropy method in the satellite town of Ethiopia, the western fringe of Addis Ababa city

Rapid conversion of conserved land and cropland to non-agricultural purposes is threatening the ecological areas and dominant agricultural activities that are the main sources of livelihood in urban fringe areas of Addis Ababa City. The combinations of government policy, socioeconomic, demographic dynamics, and biophysical triggers have driven such transformation. However, the recent fast urban expansion and infrastructural development could have accelerated the severity and rate at which urban growth impacts the ecosystem and fertile agricultural land. Yet, little attention is given about their impacts on forest and farming communities in the western fringe areas of Addis Ababa during the recent past. This study, therefore, aimed at quantifying and analyzing the trends of the urban growth and its impacts on flora and agricultural land in Sebeta-Awas town using an integrated GIS tool, remote sensing technique, and Shannon entropy method. Landsat TM of 1986, ETM+ of 2002, and OLI of 2019 were used to produce land use/land cover (LULC) classes. Object-based classification technique was carried out to generate the LULC and to measure the changes in the urban land-use class within the satellite town in the year 1986, 2002, and 2019. Shannon entropy method was applied to model study area’s urban sprawl, growth trend, and spatial change. Over the past three decades (1986–2019), Sebeta-Awas town has experienced severe urban sprawl following lack of proper development control and management. The annual urban growth rates of 1.2, 5.5, and ~15% for the periods of 1986, 2002, and 2019, respectively were obtained mainly at the expenses of agricultural land (25.48%) and forest land (16.6%), catalyzed urban sprawl which finally led to serious deforestation and reduction in rural farmland. The findings indicate that the average of entropy index increased from 0.02 in the year 1986 to 0.996 in the year 2019, indicating more dispersed urban growth to the outskirts, and spatially indicating anticlockwise shifting. In this regard, more than 90.2% of forest loss due to agricultural encroachment, built-up area expansion, and construction was widely observed in Sebeta and Alem Gena areas, of course, Northeast zone in this study. It is concluded that deforestation and continual evacuation of farming communities in the urban fringe areas due to rapid urban expansion in the name of investment and infrastructural development is expected to be worsened in the near future unless strong policy revision and management actions are undertaken.

Assessment of Progress towards Achieving Sustainable Development Goals of the “Agenda 2030” by Using the CoCoSo and the Shannon Entropy Methods: The Case of the EU Countries

The United Nations Member States adopted the “Agenda 2030” which contains 17 sustainable development goals (SDG) that involve a certain number of targets and indicators. Although the indicators are helpful in defining the position of the current country relative to the goals’ achievement, it is very complex to determine its position relative to other countries, because this requires an extensive analysis. Therefore, in this paper, the application of the multiple-criteria decision-making approach (MCDM) in defining the position of the EU (Europe Union) countries relative to the SDGs is proposed. The MCDM model is based on the Combined Compromise Solution (CoCoSo) and the Shannon Entropy methods. The final results highlight Sweden as the country that best implemented the set SD goals and has the best outputs relative to them, while Romania is in last place. The main reason for these kinds of results could be that the countries on the bottom of the list are relatively new EU members and have not been made to properly implement SDGs yet. The conclusion is that the obtained results are fully objective and rational, and that the applied model is applicable for performing this kind of analysis.

THE SELECTION OF PROSPECTIVE EMPLOYEES USING THE SHANNON ENTROPY WEIGHTING METHOD AND ARAS METHOD

This study discusses the selection of prospective employees using the Shannon Entropy weighting method and the Additive Ratio Assessment (ARAS) method which aims to determine the accuracy of the results obtained from the method. The Shannon Entropy method is a weighting method that assigns criteria weights based on the calculation of alternative employee selection data and the Additive Ratio Assessment (ARAS) method is a ranking method that has a utility function. Testing the data in this study using the Mean Absolute Error (MAE) method to get system accuracy results. Based on testing conducted using 6 criteria and 56 alternative data for prospective employees, the accuracy of the method used was 85.34%.

Multiclass EEG signal classification utilizing R\xe9nyi min-entropy-based feature selection from wavelet packet transformation

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.

Optimization of a piezoelectric energy harvester considering electrical fatigue

The application of piezoelectric materials in energy harvesting devices has become an interesting topic for designers in recent years. Most of the reported works consider electrical power as a major parameter throughout the design procedure. What is less discussed is considering other design factors like the fatigue life of the harvester as well as the financial costs of the device. In this research, a methodology is introduced to design an optimum harvester, taking into account the aforementioned design factors. The finite element model of a piezoelectric harvester used in roadways is developed and validated. The fatigue test constants of the piezoelectric material are extracted via a novel approach. A parametric study is conducted on the model to generate a dataset containing electrical and mechanical characteristics, where it is used as an input for training an artificial neural network to model the behavior of the harvester. In order to evaluate the level of importance of the optimization objectives, two methods are employed: the Shannon entropy method and equal weighting factors. Results show the effectiveness of the model, where considering the electromechanical characteristics of the module is important in terms of overall performance, efficiency, durability, and financial costs.