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Particle Swarm Optimization is a novel and powerful algorithm for continuous and discrete functions optimization problems. In this work, the concept of typical PSO is modified and applied to the feeder reconfiguration problems. Feeder reconfiguration problems are nonlinear discrete optimization problems in nature; and further, there are some defects to use typical BPSO directly for feeder reconfiguration. This research try to construct a binary coding particle swarm optimization based on typical PSO to solve this problem. The operators of typical PSO algorithm have been reviewed and redefined in this research to fit the application of distribution feeder reconfiguration. In addition, minimizing total line losses and load balancing without violating operation constraints are the objective functions used in this research. The experimental results show that the proposed method can solve the feeder reconfiguration problem more effectively.

The objective of this research is to convert heat from the sunlight to electricity by using thermoelectric generator (TEG). Electrical power is produced by temperature difference between the hot and cool junctions at the thermoelectric generator. The proposed system consists of magnifying glass and is equipped with the heat sink. The research shows that heat collected by sunlight reflection can be used to generate electrical energy through TEG. The maximum voltage 0.235 volts for one TEG at daytime peak.

Electrical energy demands requested from down-stream sectors in a smart grid continuously increase recently. One way to meet those demands is to monitor and manage industrial, commercial as well as residential electrical loads efficiently in response to demand response programs for Demand-Side Management (DSM). Compared with energy management systems, Non-Intrusive Load Monitoring (NILM), a cost-effective technique, deduces used electrical appliances from a measured total load according to the individual characteristics. This paper presents an NILM system based on Particle Swarm Optimization (PSO) for DSM, which is considered as a combinatorial optimization problem. The PSO-based load disaggregation presented in this paper is evaluated in a real house environment. As the experimentation reported in this paper shows, the presented NILM approach gave an average load identification rate of 64.06%.

In a smart grid, electricity energy demands requested from down-stream sectors continuously increase. One way to meet the energy demands is utilizing a Non-Intrusive Load Monitoring (NILM)-style system to monitor and manage residential and commercial electrical appliances in respond to demand response programs. NILM is as an electricity energy audit to energy-saving issues.This paper aims at developing an NILM identification considering the influence of voltage variations. An NILM system normally consists of "Data Acquisition", "Event Detection and Feature Extraction", and "Load Classification." The goal of load classification in NILM is to identify the operation ON/OFF status of individual household appliances. The k-Nearest Neighbors Classifier is used as the load classifier of the NILM. In NILM, measured physical phenomena, voltage signals/power profiles, to load classification for long-term load monitoring vary. The variations affect the identification performance. In this paper, a cross-validation strategy is conducted and used to deal with sampled data with systematic errors from classified loads. Different types of loads are used to verify the influence of the voltage variations to load identifications. As the experimentation reported in this paper shows, a satisfactory recognition rate of 97.60% to the NILM load identification addressed in this paper is achieved. The proposed NILM system is able to classify loads with proper robustness.

In recent years, the development of electric vehicles has drawn a lot of attention. Locations and pricing strategies can effect a car owner's willingness to park and recharge their cars in a parking lot. Different living habits also result in different parking behavior. Even with the dynamic energy prices, it is still unable to identify the energy demand at different peak times. In order to save the costs of construction or expand substations transformers and conductors in feeders, a smarter system needs to be implemented. The system will analyze the amount of energy required at different peak times and report it back in order to adjust the topology accordingly to improve load factor of the distribution systems. This paper develops a parking lot decision-making system that is able to manage and exchange data by implementing of the Smart Object System. The control of distribution systems and parking lots is optimized through Genetic Algorithm. Analysis of power flow of electric power system is performed by using PowerFactory. To minimize the impact to the existing distribution system operations, line and transformer ampacities as well as voltage profile are considered as constraints such that the operation of car parks does not violate the normal operation of distribution systems. The simulation results show that the proposed system can efficiently minimize the charging time while maintain the voltage current constraints in IEEE 14-bus.

Identification of electricity energy consumption on individual household appliances used in a smart house is the first important step for making the use and conservation of electricity energy more efficient. In the past, Non-Intrusive Load Monitoring (NILM) techniques, which are part of smart grid techniques realized to improve electricity energy usage efficiency, have been developed to identify individual appliances with avoiding installing many smart meters for appliances in a field. In this paper, a new NILM technique that integrates an efficient Genetic Programming (GP)-based feature optimizer with pattern recognition techniques is proposed to identify which appliance is being turned on or off. The proposed GP-based feature optimizer with Fisher criterion is used to generate a more efficient feature than original potential transient features extracted from captured transient response of household appliances through analysis of NILM. The new feature generated by GP is used by pattern recognition techni...

In contrast with a centralized Home Energy Management System, a Non-intrusive Load Monitoring (NILM) system as an energy audit identifies power-intensive household appliances non-intrusively. In this paper, an NILM system with a novel hybrid classification technique is proposed. The novel hybrid classification technique integrates Fuzzy C-Means clustering-piloting Particle Swarm Optimization with Neuro-Fuzzy Classification considering uncertainties. In reality, household appliances or operation combinations of household appliances in a house field may be identified under similar electrical signatures. The ambiguities on electrical signatures extracted for load identification exist. As a result, the Fuzzy Logic theory is conducted. The ambiguities are addressed by the proposed novel hybrid classification technique for load identification. The proposed NILM system is examined in real lab and house environments with uncertainties. As confirmed in this paper, the proposed approach is feasible.

In a smart house connected to a smart grid via advanced metering infrastructure, a nonintrusive load monitor (NILM) that identifies individual appliances by disaggregating composite electric load signal from the minimal number of sensors installed at the main distribution board in the field can be regarded as a part of a home/building energy management system. This type of load monitoring technique, not only for domestic but also for industrial applications, is relevant to electricity energy management and conservation issues. In this paper, an improved time–frequency analysis-based NILM composed of three system components, including data acquisition, transient feature extraction, and load identification, is proposed. The improved NILM proposed in this paper incorporates a multiresolution S-transform-based transient feature extraction scheme with a modified 0–1 multidimensional knapsack algorithm-based load identification method to identify individual household appliances that may either be energized simultaneously or be identified under similar real power consumption. For the load identification process, an ant colony optimization algorithm is employed to perform combinatorial search that is formulated as a modified 0–1 multidimensional knapsack problem. As shown from the experimental results, the improved NILM strategy proposed in this paper is confirmed to be feasible.

A distribution system consists of a number of feeders, switches and load zones. The distribution system topology may be reconfigured by changing the statuses of switches on the feeders such that the system real power loss can be reduced. However, the loads in the zone may be varied at different time in reality which leads to the system losses changed from time to time. This paper applies Evolutionary Programming (EP) approach attempt to find a switch operation strategy that can be applied to change the topology of the distribution system while minimizing the system total energy loss for a period of time. As the feeder reconfiguration is a multi-objective problem in nature, Grey Correlation Grade (GCG) is employed to gratify the selection operation in EP. The required information of this paper is retrieved from the Outage Management System (OMS) of Taiwan Power Company (TPC). The results indicate the system energy loss can be reduced with limited number of switch operations when multiple objectives are considered simultaneously under load variation conditions.

Traditionally, electricity energy consumption of a residence can be monitored through the watt-hour meter installed at the panel. However, the power consumption of individual appliances cannot be identified directly. To track and control the energy consumption of each appliance, many meters are needed. This approach results in high financial costs. To reduce the installation costs, the Non-Intrusive Appliance Load Monitoring (NIALM) approach was proposed recently. In this paper, a novel Adaptive Non-Intrusive Appliance Load Monitoring (ANIALM) system that integrates appliance energizing and de-energizing transient feature extraction methods with soft-computing techniques is developed to keep track of the energy consumption of each appliance. The energizing and de-energizing transient responses of appliances can be captured through the analyses of ANIALM. Two recognizers, k -Nearest Neighbor Rule ( k -NNR) and Back-Propagation Artificial Neural Network (BP-ANN), are used to identify different types of appliances and their operation status under different single-load and multiple-load operation scenarios. The Artificial Immune Algorithm (AIA) with the Fisher criterion is employed to adaptively adjust the feature parameters in order to improve the identification performance of recognizers when a new type of appliance is added for monitoring. From the experimental results obtained in different actual environments, the proposed ANIALM system is confirmed that it is able to identify the operation status of appliances. Also, although the generalization of both recognizers is similar and excellent, the k -NNR recognizer used by the ANIALM system is preferred from the aspects of recognizers’ identification mechanism and training performance due to its simplicity in computation and implementation.

Demand side power consumption management will become increasingly important in the near future, as power networks move from a “consumption-oriented generation” to a “generation-oriented consumption” paradigm. This requires decision making capabilities at the demand side for deciding how much power will be consumed, when energy prices fluctuate or demand responses are requested. The goal of this research is to develop a generic system which enables individual devices to autonomously determine their power consumption. The proposed system for smart devices is based on the concept of intelligent products, in which decision making agents provide intelligence to individual devices, turning them into smart devices capable of managing their power consumption. The evaluation demonstrates the feasibility and usefulness of the proposed system.

SUMMARY Distribution system reconfiguration is performed during normal or emergency operations by changing the status of switches. System reconfiguration can be used for system loss reduction or voltage profile improvement during normal operations. For emergency operations, de-energized loads can be transferred to adjacent energized feeders through system reconfiguration. Several objectives, for example, system real power loss reduction and transformer load balancing improvement, are considered simultaneously during system reconfiguration. In order to consider the objectives simultaneously, weighted sum method was commonly used. Only one solution may be obtained when weighted sum method was applied. However, several compromise solutions were preferred for the distribution system operators. Multi-objective approach was one of the methods that can be used to provide multiple compromise solutions. In this paper, the fitness function in non-dominated sorting genetic algorithm and the reproduction procedure in evolutionary programming were integrated. The proposed non-dominated sorting evolutionary programming was applied to the multi-objective problems in distribution systems in order to provide system operators with different compromise solutions. The results of this study indicated that the proposed non-dominated sorting evolutionary programming was capable of identifying better solutions than that of non-dominated sorting genetic algorithm for distribution system reconfiguration problems. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes an effective approach based on the particle swarm optimization with integer coded to determine the switch operation schemes for feeder reconfiguration. The proposed approach, enhanced integer coded particle swarm optimization (EICPSO), is able to improve the search efficiency for feeder reconfiguration problems by considering the historical local optimal solutions when generating new particles. The local optimal solutions found during the evolution process are used when new particle are generated. Three different distribution systems are used in this paper to verify and validate the effectiveness of the proposed method. Simulation results show that the proposed method can find the solutions for feeder reconfiguration problems faster than other approaches such as discrete particle swarm optimization, modified binary particle swarm optimization, and genetic algorithm.

By changing the status of switches on distribution feeders, service to de-energized customers can be restored after an occurrence of a fault. In this paper, multi-agent system (MAS) is applied for the power distribution system service restoration task using local information by utilizing the characteristics of information sharing. Each software agent is running autonomously and is able to exchange information within limited distance. Every agent is capable of performing service restoration such that service restoration can be performed in parallel when a multiple fault scenario occurs. As a result, the outage period can be reduced. BDI (believe–desire–intention) agent architecture is implemented in this paper for group and zone service restoration for power distribution systems. Zone/switch relation table is built and used by agents in order to identify adequate switching actions during the service restoration planning stage. The proposed MAS is implemented on JADE (Java Agent Development) platform. The simulation results show that the proposed BDI-based service restoration agent system is capable of providing adequate restoration switching plan in several scenarios. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents an effective approach based on simple examination and retrieval procedure (SERP) that can be used in evolutionary algorithms, such as genetic algorithm (GA) and binary particle swarm optimization (BPSO), for solving distribution feeder reconfiguration problems. The proposed approach is able to improve the search efficiency by identifying and modifying the illegal chromosomes that represent switching operation plans. The concept of simple examination is done by checking the numbers of normally opened switches on a branchless line segment in distribution systems. By applying the proposed approach, the number of illegal chromosomes during the regeneration process in evolutionary algorithms can be reduced. The idea of retrieval procedure is done by trying to use the information from historical local optimal solutions to repair the illegal chromosomes as many as possible. A 33-Bus distribution system and a practical distribution system of Taiwan Power Company (TPC) are used in this paper to verify and validate the effectiveness of the proposed procedure. The simulations consider the distribution system with and without distributed generation (DG) for power losses problems. Simulation results show that integration of the proposed procedure to GA and PSO provides more efficient and stable solutions for feeder reconfiguration problems. Copyright © 2010 John Wiley & Sons, Ltd.

Feeder reconfiguration is a common technique that is used by distribution system operators during normal or emergency operational planning. By changing the status of switches on the distribution systems, the feeders can be reconfigured. During a feeder reconfiguration, more than one objective is considered by the distribution system operators. Due to the complexity of the reconfiguration problems, the system operators are looking for assistance from computer program that can provide adequate switching plans to reconfigure the feeders such that the desired goal can be achieved. Thus, the feeder reconfiguration is a type of discrete multi-objective optimization problems. Evolutionary programming (EP) technique is a method that can be applied to identify an optimal switching plan for feeder reconfiguration. A fitness function is required in EP for chromosome selection during reproduction process. The fitness function needs to integrate the objectives to provide a measure for each chromosome. Normalizing the objectives is a typical method for multi-objective optimizations such that these objectives are comparable. In this paper, Gray CoRrelation Analysis (GCRA) method is proposed. The proposed method is used to integrate the objectives and provide a relative measure to a particular switching plan associated with a chromosome without any prior knowledge of the system under reconfiguration. Two different distribution systems are used in this paper to demonstrate how the proposed GCRA is applied during the selection process of EP. Several simulations show that the EP can identify the solution more accurately when GCRA is applied than other methods.

In recent years, several frequency estimation techniques have been proposed by which to estimate the frequency variations in power systems. In order to properly identify power quality issues under asynchronously-sampled signals that are contaminated with noise, flicker, and harmonic and inter-harmonic components, a good frequency estimator that is able to estimate the frequency as well as the rate of frequency changes precisely is needed. However, accurately estimating the fundamental frequency becomes a very difficult task without a priori information about the sampling frequency. In this paper, a better frequency evaluation scheme for power systems is proposed. This method employs a reconstruction technique in combination with orthogonal filters, which may maintain the required frequency characteristics of the orthogonal filters and improve the overall efficiency of power system monitoring through two-stage sliding discrete Fourier transforms. The results showed that this method can accurately estimate the power system frequency under different conditions, including asynchronously sampled signals contaminated by noise, flicker, and harmonic and inter-harmonic components. The proposed approach also provides high computational efficiency.

The signal processing technique is one of the principal tools for diagnosing power quality (PQ) issues in electrical power systems. The Discrete Fourier Transform (DFT) is a frequency analysis technique used to process power system signals and identify PQ problems. However, the DFT algorithm may lead to spectral leakage and picket-fence effect problems for asynchronously sampled signals that contain harmonic, inter-harmonic, and flicker components. To resolve this shortcoming, a hybrid method for frequency estimation based on a second-level DFT approach and a frequency-domain interpolation algorithm to obtain the accurate fundamental frequency of a power system is proposed in this paper. This method uses a second-level DFT to compute the cosine and sine parts for the fundamental frequency components of the acquired signals. Then, a frequency-domain interpolation approach is adopted to determine the amplitude ratio for the cosine and sine parts of the system's fundamental frequency. To demonstrate the performance of the proposed frequency estimation method, the observation window used by this paper to evaluate different estimation algorithms is 200 ms. According to the IEC standards, a 200 ms acquisition window is recommended for power system quality assessment. A set of mixed signals with harmonic, inter-harmonic, and flicker components with the fundamental frequency deviation is used. The evaluation results demonstrate the superiority of the new method over other approaches for assessing asynchronously sampled signals contaminated with noise, harmonic, inter-harmonic, and flicker components.

Distribution restoration process is done by switching actions of sectionalizing and tie switches on the feeders. The state of these switches can be determined by applying logical reasoning. Thus, knowledge-based systems are ideal tools to accomplish the task. In this paper, an expert system is developed by utilizing its fast reasoning mechanism and object-oriented features. The feeder component and configuration data are organized in a hierarchy way using the object-oriented programming paradigm. On the other hand, facts are used for representing the information during inference process. Service restoration procedure proposed in this paper takes load variation into consideration. The simulation results indicate that more solutions can be obtained for service restoration problems when load variation is considered. Another unique property of the proposed system is that it is capable of proposing multiple restoration plans. Multiple plans are important for the system operators since the best solution proposed by the system may not be workable in reality. By proposing multiple plans, the system operator can choose a plan which is more suitable to the real situation for service restoration problems.

Electric vehicles are no longer an unreachable dream, a vast amount of electric vehicles have gradually appear on the market recently. To charge the batteries of electric vehicles, constructing of charging station is needed. However, due to the conductors and transformers capacities in feeders, not all the power capacity can be allocated to one parking lot. To improve the balance between the power providers and consumers on power grid, charging schedule is required. This paper developed a simulation environment which can achieve charging schedule more efficiently through Particle Swarm Optimization. Moreover, the developed system can verify the voltage profile of the distribution system as well as the conductors and transformer constraints. The simulation results show that the schedule obtained through Particle Swarm Optimization can minimize the charging time under the power constraints.

In recent years, the development of electric vehicles has gained a lot progress. Many infrastructures are being installed for the electrical vehicles. However, due to the limited power availability, not every electric vehicle can be charged simultaneously in parking lots. This paper proposed a simulation environment which is a Genetic Algorithm based charging control system that can achieve more efficient charging schedule, and take the power constraints into consideration as well. The results of three simulated scenarios are presented. The simulations show that the proposed Genetic Algorithm based charging control system can efficiently maximize the profit or minimize the charging time according to the objectives of different parking lots.

This paper proposes an effective and fast approach based on the hybrid fuzzy-grey method to evaluate restoration plans for power distribution systems. The proposed method consists of two stages: the fuzzy multicriteria evaluation and the grey relational analysis. The fuzzy multicriteria were adopted to deal with imprecise linguistic descriptions in the operators' heuristic rules, while the grey relational analysis was used to judge the ranking of preference for each feasible restoration plan. To demonstrate the effectiveness of the proposed method, a typical distribution system of Taiwan Power Company was examined. The test results showed that the proposed method was efficient and expeditious in obtaining a preferable restoration plan by using simple numerical operations.

A correct and rapid inference is required for practical use of an online fault diagnosis in power substations. This paper proposes a novel approach for on-line fault section estimations and fault types identification using the hybrid cause-effect network/fuzzy rule-based method in distribution substations. A cause-effect network, which is well suited to parallel processing, represents the functions of protective relays and circuit breakers for selection of faulted sections. Therefore the inference speed can be improved significantly. In order to deal with the uncertainties involved in the process of clarifying faults, a fuzzy rule-based method is derived. The proposed approach has been practically verified by testing on a typical Taiwan Power Company's (Taipower) secondary substation. The experimental results reveal that the correct and rapid diagnosis is obtained even for the fault domains involving multiple faults and failure operations of protective devices. Moreover, it is easy to implement and transplant into different substations.

In the near future, more and more vehicles will be driven by electricity instead of fossil fuels. Consequently, these electric vehicles need charging, for example when parked in car parks. Charging many electric vehicles simultaneously in a car park leads to various problems which need to be tackled. This is mainly due to the limited power availability, caused by the limited capacity of the conductors and transformers in place. Moreover, every vehicle has different charging requirements, due to the variety in parking time and driving distance after parking. Because of these reasons, every car park will need a system which controls the charging of electric vehicles, and determines which vehicles are charged at what time. In order to evaluate different charging control systems for car parks, a simulation environment is proposed in this paper. The proposed simulation environment is based on the Smart Objects System, and enables the simulation and evaluation of various charging control systems for car parks. Initial simulations performed with the proposed simulation environment show promising results.

Feeder reconfiguration of the distribution system can be accomplished by changing the statuses of switches. When the distribution system topology is changed, the real power losses may be reduced. In this paper, a CSS algorithm with real number strings is applied for providing a switching operation strategy in order to solve the distribution system real power losses minimization problems. The new solutions are produced by CSS during the problems solving process. Coding and decoding schemes are proposed in this paper in order to avoid generating invalid solutions as well as improve the search efficiency when CSS searches the optimal solution. A 33-bus distribution system is tested in this paper. The simulation results show that the approach proposed in this paper is able to find the optimal solution more effectively.

Due to the global warming and climate change, it is very important to effectively improve the efficiency of the electricity energy consumption. Monitoring the power consumption of residences and buildings is one of the approaches that can improve the efficiency of the electricity energy consumption. In this paper, a Non-Intrusive Appliance Load Monitoring (NIALM) system, which applies a neuro-fuzzy pattern recognizer (NFPR) with Linguistic Hedges (LHs) to recognize the operation status of individual appliances, is proposed. A two-stage fuzzy pattern recognition process is presented in this paper. First, Fuzzy C-Means (FCM) clustering is employed to coarsely estimate the parameters used in NFPR. Following this stage, the Scaled Conjugate Gradient (SCG) training algorithm is applied to adaptively fine tune the parameters. In the proposed NIALM system, either load energizing or load de-energizing transient features are extracted from an acquired transient current waveform. NFPR performs load recognition based on these transient features. The recognition results obtained from different real experimental environments confirm that the proposed approach is able to identify the operational status of individual appliances.

Electricity is one of the most popular forms of energy in modern society. In a smart house, the smart meter directly interacts with intelligent appliances to disaggregate the total electricity energy consumption. However, this results in high financial costs and low monitoring reliability due to the installation of massive meters for appliances. In this paper, a Non-Intrusive Appliance Load Monitoring (NIALM) system is proposed. The proposed NIALM system, which integrates feature extraction and feature optimization methods with pattern recognition techniques, is used to identify the operation status of individual appliances. Both energizing and de-energizing transient current waveforms of an unidentified appliance can be identified through analyses of the proposed system. Different potential transient features, extracted from an acquired transient current waveform, form a feature space to be selected. In pattern recognition, it is important to select the most effective features from the original feature set and to appropriately scale them by real-valued numbers simultaneously in order to improve the identification accuracy and construct a small and efficient identification system. In this paper, the proposed NIALM system employs the Genetic Algorithm (GA) with the Fisher criterion to search for the optimal sub-set of features. After the most influential features are selected, different identifiers, including k-Nearest-Neighbor Rule (k-NNR), Back-Propagation Artificial Neural Network (BP-ANN) and Learning Vector Quantization (LVQ) are used to perform load identification. The identification results confirm that the proposed system is able to identify the operation status of each appliance under different single-load and multiple-load operation scenarios at different real experimental environments. Besides, this paper comments that the k-NNR identifier is preferred for the development of NIALM, due to its simplicity.

Distribution system reconfiguration is one of the methods that is commonly used in order to improve the system reliability and reduce the system real power loss. The distribution system reconfiguration is normally done by altering the status of switches. When the status of switches on the distribution system is changed, the topology of the system is also changed. During the normal operations, load currents change at different time due to the power usages of different customers. The operation conditions of distribution systems change when the load currents vary. As a result, the topology of distribution system needs to be altered from time to time so the system real power loss can be reduced and the reliabilities can be improved. In this paper, integration of Evolutionary Programming (EP) and Ant Colony Optimization (ACO) is proposed. The objective is to minimize the system real power loss with minimum number of switch operations by considering the load currents variations. Each day is divided into 12 time slots. The peak load current is used for each time slot. By considering the number of switch operations and system real power loss simultaneously, the proposed approach is able to identify the proper switch operations so the system real time loss within a period of time can be minimized. Instead of using constant values as the desirability of state transition during the search process of ACO, application of the grey correlation grade is proposed. Verification is performed using the loading information and the topology information obtained from the Outage Management System (OMS) of Taiwan Power Company (TPC). The results show that by applying the integrated EP and ACO with proposed desirability calculation, the distribution system real power loss can be reduced with proper switch operations for variable load currents.

In this paper, we use advanced multi-agent technologies to establish a smart power grid to solve the power distribution restoration problems. Specifically, we modeled switches in the power distribution systems as intelligent agents who can communicate and coordinate together to find a global optimal solution to the power distribution service restoration problems. We modeled the society of intelligent switch agents as local power committees in a distribution system and propose a coordination method for the distributed local power committees so that they could reach a consensus among many proposed service restoration solutions after faults are detected in the distribution feeders. We show how the local switch agent committees find the global optimal restoration solution to all local power demand issues through communicating and propagating the power demand issues, proposing local candidate solutions and combine issues and solutions and finally finding solution by constraint satisfaction. We also compare our multiagent approach to the traditional methods and benchmark problems from power restoration research literatures and show satisfactory performance results.

Objective: The purpose of this study was to compare the dimensional measurements and surface topography of geometry reconstructed from high-resolution computerized tomography (HRCT) of temporal bone with the same natural ears.Method: HRCT of temporal bone was performed using simultaneous acquirement of 64 sections with a slice thickness of 0.5 mm. Corresponding image data were uploaded into software Amira 4.1 for segmentation and three-dimensional reconstruction. A comparison of the surface topography of the CT reconstructed ear and the natural ear was made.Results: This prospective study included 10 subjects, including 5 males and 5 females with normal hearing and no previous otologic disorders. A significant correlation of coefficients of various measurements on reconstructed auricles and natural auricles was made. The shape and volume of reconstructed ears by Amira was identical to the natural ear.Conclusion: The shape and volume of reconstructed auricles by Amira was identical to the natural ears. CT ...

Due to the global warming and climate changes, using and conserving of the electrical energy effectively becomes a very important issue recently. If each appliance operates efficiently, the power energy can be used and conserved effectively. Further, the greenhouse gases can also gradually be reduced. This paper* proposes a Non-Intrusive Load Monitoring (NILM) system that employs a decision mechanism based on hierarchical support vector machines (HSVM) to identify the energizing and de-energizing statuses of each load. In the proposed system, the load energizing and de-energizing transient features are extracted from load current waveforms that are acquired at the power entering point. The HSVM performs load identification tasks based on the features. Although a support vector machine (SVM) identifier can solve nonlinear separable cases and optimize the decision hyper-plane, it is a two-class identifier. On the other hand, the NILM problem is a multi-class identification problem. Since any multi-class identification problem can be decomposed into a series of two-class identification sub-problems, the proposed system applies a group of binary SVMs that are arranged into a form of hierarchical tree to identify the operation status of each load. Each node within the tree represents a binary SVM identifier, and each identifier is assigned to solve a corresponding two-class sub-problem. Through this hierarchical decision process, the class of an undetermined datum can be identified. Finally, the results in different experiment environments show that the proposed system is able to identify the operation status of each load, and it also has appropriate robustness.

Due to global warming and climate changes, it is very important to use and conserve the power energy effectively. Monitoring the electrical consumption of consumers is one of the methods that can improve the energy usage efficiency. In this paper, a Non-Intrusive Load Monitoring (NILM) system, which applies a fuzzy classifier with the Fuzzy C-Means (FCM) clustering and optimization algorithms to identify the energizing and de-energizing statuses of each appliance, is proposed. Load energizing and de-energizing transient features are extracted, and the fuzzy classifier performs load identification based on these features. A two-stage fuzzy classifier is used in this paper. For the first stage, the FCM clustering is used to coarsely determine the parameters of the fuzzy classifier. Following this stage, two optimization algorithms, Error Back-Propagation Algorithm (EBPA) and Genetic Algorithm (GA), are employed to fine tune those parameters. As the classification results obtained from different realistic experimental environments, the proposed system is confirmed that it is able to identify the operational status of each appliance.