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The multi-source data-driven faulted segment identification methodology has spurred research interest recently. Based on analyzing the post-fault physical features of the three-core cable earthed strap current, this study develops an approach to identify the faulty cable segment(s) of neutral low-resistance grounding distribution grids with distributed generators. To eliminate the potential uncertainty of recorded data, introducing Mixed Integer Linear Programming to construct the fault diagnosis model. The model employs traditional fault indications, including alarms from remote fault indicators and metering signals of zero-sequence current, to promote the validity of identifications. Under the causal logic constraint of events, employing the expected fault scenario with the minimum discrepancy to available data to determine the true faulty cable segment(s). Additionally, the model decision variables can be used as the reference for anomalous data determination. The effectiveness and robustness of the proposed methodology are validated by applying it to a simulation model based on a real-life three-core cable utility feeder.

The optimization of most combinatorial problems is NP-hard, which can be solved by heuristic algorithms to obtain approximately optimal solutions, especially for large-scale problems. Many heuristic algorithms can apply the 2-opt local search to find better solutions. In complete 2-opt local search, every valid neighboring solutions of swapping mechanism will be compared, which is very time consuming especially when a sequential algorithm is adopted. Nowadays, graphic processing units (GPUs) have evolved into powerful and flexible parallel computation platforms that have been widely used to accelerate the solving of NP-hard problems. Therefore, in this study, we focus on how to use GPUs to solve the single-row facility layout problem (SRFLP) with the 2-opt-based simulated annealing (SA) heuristic algorithm. As far as we know, this study is the first to solve SRFLP by using a GPU. After analyzing the fitness function and the move gains calculation between parent and child solutions, we propose a prefix-sum formula table to eliminate a large amount of replicated computation. To calculate move gains for the 2-opt local search during each iteration, many GPU threads are created and they construct and lookup the table in parallel. According to experimental results, if the prefix-sum formula table is not adopted, the GPU version outperforms the sequential CPU counterpart with the best speedup of 123. However, if the table is used in the GPU version, the best speedup can reach up to 3208. Because the proposed parallelization approach with the prefix-sum formula table is based on the features of the 2-opt local search and the SRFLP fitness function, it can be applied to any 2-opt-based heuristic algorithm for solving SRFLP even though the SA algorithm is used in this study.

Extreme learning machine (ELM) has fast learning speed and perfect performance, at the same time, ELM provides a unified learning framework with a widespread type of feature mappings which can be applied in multiclass classification applications directly. These advantages make ELM become one of the best classification algorithms, and ELM has attracted great attention in supervised learning and semi-supervised learning. However, noise and outliers of data are usually existed in the real world, which will affect the performance of ELM. To improve the robustness and classification performance of ELM, we propose the Kernel Risk-Sensitive Mean p-power Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRP-HRELM) method. On the one side, as a nonlinear similarity measure defined in the reproducing kernel space, the kernel risk-sensitive mean p-power loss (KRP) can effectively weaken the negative effects caused by noise and outliers. Therefore, the KRP is introduced into ELM to enhance its robustness. Then, the application of hyper-graph can help the ELM to explore higher-order geometric structures among more sampling points, thereby obtaining more comprehensive data information. In addition, to obtain a more sparsity network model, the L2,1-norm is used to constrain the output weight. On the other side, improving the practical application ability of KRP-HRELM is also the focus of our research, so KRP-HRELM is extended to semi-supervised learning, which is called the semi-supervised KRP-HRELM (SS-KRP-HRELM). Notably, the results of the robustness experiment have proved that our method has extraordinary robustness. At the same time, by using four evaluation measures such as accuracy, recall, precision, and F1-measure, to evaluate the classification results, we can find that our method has obtained better classification performance than other advanced methods.

Frontal impacts are the most frequent crash types and may generate extreme body in white (BIW) deformations. Vehicles are subjected to impact evaluation rating protocols such as 100 % Front Rigid Barrier (FRB) impact, 40 % Offset Deformable Barrier (ODB) impact and 25 % overlap (small overlap) impact. This paper proposes a collaborative optimization process using optimal Latin hypercube design (Opt LHD) and response surface methodology (RSM), to improve the vehicle crashworthiness in the frontal impacts, by considering displacement oriented structure (DOS). Upper and lower engine bay structure were considered herein to increase the lateral vehicle displacement during small overlap impact and decrease the impact force that transferred to the passenger compartment in the frontal impacts. The optimal results indicated that the acceleration of the B-pillar was reduced 0.4g in the FRB impact, the intrusion of the firewall was reduced by 55.13 % in the ODB impact, and the intrusion of passenger compartment during small overlap impact was decreased by an average of 53.88 %, with a maximum percentage of 71.41 % around left toepan, leading to an IIHS rating to acceptable from poor. The proposed crashworthiness design approach is effective in vehicle structure optimization for better frontal impacts performance.

Recently, extreme learning machine (ELM) has attracted a lot of attention due to its high performance and extreme speed. However, how to improve the robustness of ELM has always been a problem. Considering the problems of noise and outliers in the experimental data, in this paper, we introduce the kernel risk-sensitive mean p-power error (KRP) into ELM and propose a robust ELM method named kernel risk-sensitive mean p-power error based robust extreme learning machine (KRPELM), on the basis of the high efficiency and robustness of KRP. In KRPELM, KRP function instead of square loss is integrated into ELM as the loss function, which can improve the robustness of ELM to noise and outliers. We also propose an efficient iterative adjustment strategy to optimize KRPELM. Nine benchmark datasets are utilized to verify the classification performance of the proposed KRPELM. In addition, we apply the proposed method to the classification of cancer samples. The experimental results on five cancer gene expression datasets show that KRPELM can identify different cancer types more accurately.

Small overlap accidents of sedans often result in severe injuries. In this article, a crashworthiness design method, including engine-room energy management, suspension safety design and passenger ...

In this paper, a feedback compensator (FBC) and a Feedforward compensator (FFC) are proposed to construct a novel compensated peak current mode control pulse width modulation (CPC-PWM) for primary-side controlled flyback converters. Using the proposed FBC, the PWM duty cycle of an abnormal operating flyback converter would be descended to limit the output current for reducing power dissipation. Using the proposed FFC, the effect of delay time would be descended to reduce the over-flow current for increasing the current accuracy. In this paper, the operating principle and mathematical model are described and analyzed. Then, the component values are well designed to satisfy the electrical specifications. Finally, a prototype is designed and realized to access system performance. The experimental results show that the proposed CPC-PWM can validate in a wide input voltage range and output short conditions, which also has good current accuracy and reduces power dissipation by about 68%.

Due to prominent attenuation of travelling waves, double-end fault location method produces considerable errors when applied to ultra-high voltage AC half-wavelength transmission (UAHT) lines. In order to address this problem, this study presents a novel fault location scheme based on the attenuation, refraction and reflection principles of aerial-mode voltage travelling waves (AVTWs). In the scheme, the faulted section of an UAHT line is first determined according to the accumulated amplitude ratio of the pre-determined frequency components of AVTWs asynchronously sampled at both terminals of the line. Then, the Teager energy of the first two wavefronts of the AVTW - measured at the terminal in the faulted section - is used to define two factors for selecting the appropriate accurate fault location method. Finally, the single-end travelling wave method is adopted for locating the faults near the line terminal. The presented double-end method utilises the amplitude ratio of AVTWs to locate the faults far from the line terminal. A 1000 kV UAHT line model is considered with various fault conditions in PSCAD/EMTDC simulations. The simulation results and analysis demonstrate that the proposed fault location scheme is accurate and immune to fault distances, fault types, fault impedances and fault inception angles.

Based on distribution characteristics of the zero-sequence voltages and currents of the distributed lines, this paper proposes a novel single-phase-to-earth fault (SPTEF) location scheme for distribution network. Firstly, the distributed parameter model of distribution feeder lines is appropriately simplified according to Taylor series expansion of the hyperbolic functions in the model. The fault factor is constructed to select the faulted feeder. Then, the distribution features of the zero-sequence voltage (ZV) along the feeder line are analyzed. In each section of the faulted feeder, golden section search algorithm is adopted to distinguish the faulted section. Finally, based on the concept of maximum fault current, accurate fault location is realized by genetic algorithm (GA). A radial distribution network model containing different types of distribution lines is established in PSCAD/EMTDC. The calculation results of MATLAB prove the validity, reliability and accuracy of the proposed fault location scheme.

This article describes how as one of the hot parallel processors, the general-purpose graphics processing unit (GPU) has been widely adopted to accelerate various time-consuming algorithms. Dynamic programming (DP) optimization is a popular method to solve a particular class of complex problems. This article focuses on serial-monadic DP problems onto NVIDIA GPUs. As 0/1 knapsack is one of the most representational problems in this category and it often arises in many other fields of applications. The previous work proposed the compression method to reduce the amount of data transferred, but data in shared memory cannot be reused. This article demonstrates how to apply a more condensed data structure and the inter-block synchronization to efficiently map the serial-monadic DP onto GPUs. Computational experiments reveal that the best performance improvement of the approach is about 100% comparing with the previous work.

In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.

Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

It is very important to train practical ability of the students in logistics engineering. In the traditional practice teaching, often only pay attention to the software operation of the laboratory and neglect the practical training that combines with the actual enterprises in order to help the enterprises solve their practical problems. This will lead to the disconnection between the students and the reality, which prevents the students from applying the theory and expertise they has learned. To cultivate innovative talents in logistics, students need to integrate more practical problems, and theoretically and practically analyze and solve them. In this paper, we design the teaching of logistics system planning and simulation practice in the logistics system planning and simulation course. We proposed how to analyze and design the production logistics system in combination with actual cases, and simulate the actual system. Through the case design, it can improve the students’ ability to solve the practical problems in combination with actual case. This design can provide the instructors the practical case of the related course, and also give references for how to improve the practical and innovative ability of logistics engineering students.

Combined with Auto Encoder (AE), Extreme Learning Machine Auto Encoder (ELM-AE) has attracted the interest of researchers in recent years. Considering the classification tasks of single-cell Ribonucleic Acid sequencing (scRNA-seq) data, in this paper, we propose a novel supervised learning method based on ELM-AE, which is named Robust Graph Regularized Extreme Learning Machine Auto Encoder (RGELMAE). The method introduces L2,1-norm minimization on loss function to improve the robustness, and combines with the manifold regularization framework to explore the internal local structure between data points. Finally, RGELMAE is applied to the classification tasks of scRNA-seq data. The experimental results indicate that our method can effectively extract the key information representing the original data, and improve the classification performance of ELM.

Kernel Risk-Sensitive Loss (KRSL) is a nonlinear similarity measure defined in kernel space, which enable the gradient based method to achieve higher accuracy while effectively weakening the negative effects caused by noise and outliers. Defined as a kernel function expectation between two random variables, KRSL has been successfully applied in robust machine learning and signal processing. Extreme Learning Machine, as one of the most popular methods of machine learning, has attracted great attention in supervised learning and semi-supervised learning. However, when the data contains noise and outliers, the manifold structure of the data is not considered or the neural network structure is too complex, the performance of traditional ELM methods will decline. Therefore, based on KRSL, hyper-graph regularization and L 2,1 -norm, we first propose a more robust ELM method named Kernel Risk-Sensitive Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRSL-HRELM). In KRSL-HRELM, KRSL is introduced into ELM to enhance its ability to handle noise and outliers. Moreover, the hyper-graph regularization is integrated into the method to learn the higher-order geometric structure information between the data. In addition, the L 2,1 -norm is introduced to constrain the output weight matrix to obtain a sparse network model. Inspired by other semi-supervised ELM methods, we extend KRSL-HRELM to semi-supervised learning and propose its semi-supervised version semi-supervised KRSL-HRELM (SS- KRSL-HRELM). Empirical studies on a large number of real-world datasets show that the proposed methods are competitive with other advanced supervised or semi-supervised learning methods in terms of robustness and efficiency.

During the mining process, many energy resources are produced additionally, which have a promising prospect to supply electricity, heat and cooling energy. However, different energy inertia and diverse uncertainties from photovoltaic, wind turbine and gushing water outputs will pose challenges to the operation of a coal mine integrated energy system. This paper constructs a coal mine integrated energy system operation model considering energy resources recovery. Coal mine integrated energy system consists of energy supply subsystem, energy recovery subsystem, and energy storage subsystem, and it integrates multiple energies such as electricity, heat and cooling. A two-stage robust stochastic optimization method is proposed to adapt to the uncertainties. At the day-ahead stage decision, the optimal operation problem is solved at long time intervals to determine the heat dispatching. Meanwhile, the intra-day stage decision is focused on dealing with diverse uncertainties at short time intervals to enhance operational robustness. Heat energy equipment is scheduled at intra-day stage with the day-ahead dispatching result. A real case of coal mine is demonstrated to validate the effectiveness and robustness of the proposed method. The result reveals that the proposed method, compared to the conventional methods, can fully improve the economic benefits, the energy recovery and the system operational robustness.

Battery cells connected in series have been widely used in high-voltage and high-power applications. In this paper, a single-phase battery energy storage system with battery balance charging, battery balance discharging, and power factor correction capabilities was developed. A prototype suitable for a single-phase 110V power supply was designed and implemented for verification. To verify its performance, three 48V/7 Ah battery modules were used as normal batteries, and a 48V/5Ah battery module was used as a retired battery. The experimental results showed that the battery energy storage system has excellent battery balancing capability. Compared with that of the conventional system without balanced control, the balancing performance of the proposed system is increased by about 15.25% and 26.92%, respectively, when the system was operated in the converter and rectifier modes. In addition, the proposed system also had battery fault tolerance, and was compatible with recycled batteries and an independent power supply.

Genetic algorithm (GA), a global search method, has widespread applications in various fields. One very promising variant model of GA is the island model GA (IMGA) that introduces the key idea of migration to explore a wider search space. Migration will exchange chromosomes between islands, resulting in better-quality solutions. However, IMGA takes a long time to solve the large-scale NP-hard problems. In order to shorten the computation time, modern graphic process unit (GPU), as highly-parallel architecture, has been widely adopted in order to accelerate the execution of NP-hard algorithms. However, most previous studies on GPUs are focused on performance only, because the found solution qualities of the CPU and the GPU implementation of the same method are exactly the same. Therefore, it is usually previous work that did not report on quality. In this paper, we investigate how to find a better solution within a reasonable time when parallelizing IMGA on GPU, and we take the UA-FLP as a study example. Firstly, we propose an efficient approach of parallel tournament selection operator on GPU to achieve a better solution quality in a shorter amount of time. Secondly, we focus on how to tune three important parameters of IMGA to obtain a better solution efficiently, including the number of islands, the number of generations, and the number of chromosomes. In particular, different parameters have a different impact on solution quality improvement and execution time increment. We address the challenge of how to trade off between solution quality and execution time for these parameters. Finally, experiments and statistics are conducted to help researchers set parameters more efficiently to obtain better solutions when GPUs are used to accelerate IMGA. It has been observed that the order of influence on solution quality is: The number of chromosomes, the number of generations, and the number of islands, which can guide users to obtain better solutions efficiently with moderate increment of execution time. Furthermore, if we give higher priority on reducing execution time on GPU, the quality of the best solution can be improved by about 3%, with an acceleration that is 29 times faster than the CPU counterpart, after applying our suggested parameter settings. However, if we give solution quality a higher priority, i.e., the GPU execution time is close to the CPU&rsquo, s, the solution quality can be improved up to 8%.

Logistics distribution path optimization as an NP-hard problem is one of the important problems in logistics. Many intelligent algorithms are considered to be used to solve such problems. In this paper, an improved genetic algorithm is proposed for solving Travelling Salesman Problem (TSP), a kind of classical logistics distribution path optimization problem. The method uses island model genetic algorithm to formulate rules that are more suitable for TSP, through applying greedy algorithm in the generation of initial population, modifying the selection method and discussing different migration strategies, the computation time of solving the problem is shorten, the calculation efficiency is improved, and the probability of falling into the local optimal solution is reduced. Finally, experiments are conducted to discuss the effectiveness of our method.

With the development of the economy, the demands for food quality have gradually increased. Cold chain as a temperature-controlled supply chain has emerged to ensure the fresh goods. In recent years, the establishment and improvement of the cold chain logistics, as complex system engineering, is very important. While the cold chain logistics system has been vigorously developing, the common problems we face include: selection of suitable location for cold chain distribution center as well as optimization of distribution route. In this paper, we mainly focus on how to optimize the cold chain logistics distribution for a fresh processing factory M. We propose the methods to optimize the cold chain logistics distribution for M based on linear programming model. First, we adopt location set covering problem (LSCP) to model the location problem of cold chain logistics distribution center for Factory M, and use zero-one programming to solve the distribution center location. Then, we use transshipment problem, which is one of the transportation problem, to model and solve the distribution route for M. Experiments are conducted on IBM ILOG CPLEX Optimization platform, and the suitable plan of logistics distribution for Factory M is figured out at the end. The experiment result shows that the optimal solution using our optimization method can save about 15% of transportation cost compared to the result without any planning.

With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L 2 , 1 -norm named L 2 , 1 -Extreme Learning Machine ( L 2 , 1 -ELM) has been proposed. The method introduces L 2 , 1 -norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples.

In allusion to such problems as real-time requirement dissatisfaction and significant recognition difference caused by dimension difference existing in the imaging and recognition algorithm for pedestrian in dark scene, a fast head detection and recognition method for pedestrian at night based on fast support vector machine (FC-SVM) algorithm optimization and entropy weight is established in this paper according to relevant principle of statistics. Based on entropy weight, this method aims at improving the extraction process based on histogram gradient features in order to establish threebranch SVM for the deep recognition of pedestrian at night; meanwhile, FC-SVM algorithm is combined to optimize the recognition calculation overhead in order to ensure the real-time property of the recognition algorithm. Furthermore, the falsely detected pedestrians are evaluated on the basis of the head detection mode so as to improve pedestrian imaging matching accuracy. The simulation result shows that this method can not only effectively recognize FIR target of pedestrian at night, but also effectively adapt to such different application environments as urban and suburban areas on the basis of ensuring the real-time requirement for pedestrian recognition, thus presenting good practicability.

Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.

In order to be able to apply quantum encryption communication to large-scale networks, the adoption of SDN networking in quantum key distribution networks is considered to be a feasible solution. this paper discusses the optimization scheme of deploying quantum encryption controller in QKD under various optimization objectives, and proposes a controller deployment algorithm based on improved NSGA2 algorithm according to network topology characteristics, which is intended to guarantee the algorithm. At the same time of effectiveness, the convergence speed of the algorithm is improved.

In smart factory, automatic processing mode with high-tech content has become the main method of the modern sorting system. In small manufacturing enterprises, most of the warehouse sorting system is not done with automation but by manual labor. However, along with the increase in demand for products and the expansion of production scale, an automated warehouse sorting system is necessary. Automated sorting system is a typical discrete logistics system. Applying modern logistics simulation technology can help to analyze and evaluate the actual systems. In this paper, we take a small scale bag manufacturing enterprise for example, to design an automated sorting system in the warehouse. Applying discrete event simulation, model and simulate the system with the Automod as one of the most popular logistics simulation tools. Through conducting the experiments and analyzing the experimental results, verify the feasibility of our design and put forward the improvement of the system. The proposed method can provide the references for other small manufacturing enterprises and help business managers to make decisions.

The distribution center plays an important role in modern supply chain. Choosing appropriate distribution center location can avoid many detrimental conditions. Distribution center location as optimization problems are widely studied and applied in a lot of fields. In this paper, we take selection of the tobacco distribution center in Area F as the research object. Based on capacitated facility location problem (CFLP) model, apply mathematical programming methods and analytic hierarchy process (AHP) to design the logistic distribution center location. Through the establishment of mathematical model of CFLP, use IBM ILOG CPLEX studio to calculate the optimized solutions, and further to analyze the results of the solution for candidates. Then use AHP method to find the better location among the candidates. The work provides a good way and ideas for the distribution center location.

In the electrical power system, there is a wide range of communication services and service needs, and the realization of differentiated services of different types is quite necessary. Thus, based on the combination of characters and requirements of electric power communication business, this paper makes an argument for a routing and wavelength assignment algorithm based on prediction and layered graph model (PLGMRWA). Through the wavelength number prediction mechanism, it realizes the distribution of wavelength resource according to the needs, and uses the layered graph model to wholly solve the problems of route and wavelength assignment in comprehensive considerations of hops and the usage of wavelength. The simulation results indicate that, algorithm PLGMRWA has advantages of blocking capacity and wavelength resource utilization over algorithms GWAS (Grouped wavelength assignment strategy) and DPWA (Dynamic and priority-based wavelength assignment algorithm) for all levels of business in electrical power communication network.

Electricity plays a crucial role in the well-being of humans and is a determining factor of the economic development of a country. Electricity issues have encouraged researchers to focus on improving power availability and quality along with reliability. This pursuit has increasingly raised the intention to integrate renewable energy (RE) into power systems to curb the problem of energy deficiency. However, intermittency in the sources of RE supply coupled with fluctuating changes in demand with respect to time has induced high risk in maintaining system reliability in terms of providing adequate supply to consumers. Whilst an energy storage system (ESS) is not another source of electricity, it is proven to be effective and viable in solving the aforementioned issues. Thus, this paper comprehensively reviews the development of ESS technologies and discusses the benefits and real-life applications of these technologies. The concept of reliability in power systems is also explored to provide an improved understanding of this study. Lastly, notable studies that have addressed the reliability impact of ESSs on power systems are discussed. This review paper therefore is expected to provide a critical analysis of ESS developments, as well as recognize their research gaps in terms of reliability studies in modern RE-integrated power networks.

Electric pulse field methods are wildly used in biotechnology and industrial applications. A pulse generator (PG) is the device that provides the desired electric pulse field. In this paper, a novel ac-powered PG (ACPG) that is directly powered by normal ac power (i.e., utility power) is proposed. Since there is no high-voltage dc power supply in the proposed ACPG, the circuit complexity and cost are effectively reduced. Furthermore, the voltage and duration of the generated pulse of the ACPG can easily be controlled to match specific applications. In order to assess the performance of the proposed ACPG, a prototype for providing a unipolar and a bipolar pulse train is designed and implemented. Experimental results show that the ACPG can actually generate a 0-1040-V pulse with a 750-ns to 118-mus duration without overshooting and ringing

This paper discusses the designing plan of ORACLE-based Bohai Sea engineering geology database structure from requisition analysis, conceptual structure analysis, logical structure analysis, physical structure analysis and security designing. In the study, we used the object-oriented Unified Modeling Language (UML) to model the conceptual structure of the database and used the powerful function of data management which the object-oriented and relational database ORACLE provides to organize and manage the storage space and improve its security performance. By this means, the database can provide rapid and highly effective performance in data storage, maintenance and query to satisfy the application requisition of the Bohai Sea Oilfield Paradigm Area Information System.

Since existing load balancing algorithms designed for heterogeneous integrated networks have failed to consider the service priority and the network resource sufficiently, a load balancing algorithm based on binary particle swarm optimization (BPSO) is proposed from access control perspective. Firstly, considering different requirements of communication services, the optimization model of the network access control is built. The method sets the maximum entropy of the load rate as the optimization goal. Then, BPSO algorithm is employed to maximize the entropy of the load rate, and the optimal access solution is obtained. Simulation results demonstrate that the services with different priorities are differently served, and loads among the overall heterogeneous system can be effectively balanced. Furthermore, while the service blocking probability can be dramatically decreased, the resource utilization of overall heterogeneous system is greatly improved.

In order to ease congestion phenomenon in the case of limited resources of nodes, this paper presents an adaptive congestion control algorithm based on the node's priority. The proposed algorithm confirms prioritization according to the degree of congestion situation, and then adaptively chooses its probability of accessing channel in order to quickly relieve the congestion in this region. Experimental simulation results indicate that the algorithm can quickly and effectively to relieve the congestion and improve performance of network.

The work admission policy which is suited for partial grid system is proposed in this paper. The strategy reserves resources for the general grid work from VIP user, as well as providing preemptive priority for general grid work to take up the service resources from best-effort work. The simulation results show that this strategy can qualify the work service for the general work from VIP user adequately when the work service quality of the general work from non-VIP user and best-effort work are least affected. At the same time, the number of reservation resource can be automatically adjusted due to the proportion between the intensity of general work and the best-effort work .

A dynamic channel allocation scheme is proposed in this paper, which is applied to integrated voice/data services in TD-SCDMA mobile communication system. The scheme provides the reserved channel for handoff calls to increase the rate of successful accession. Meanwhile, the new call queuing strategy is adopted to limit the deterioration of the new call blocking probability caused by the reservation scheme. When a serving call goes away, the new call in the queue can obtain the free channel in the FIFO manner. It reveals that the scheme can not only reduce the blocking probability of handoff call, the blocking probability of data call and the delay of data transmission, but also improve the new call blocking probability obviously.

This paper proposes a novel variable step size LMS adaptive filtering algorithm. The algorithm is established based on nonlinear relationship between step and error signal in hyperbolic tangent function. The step is adjusted by the autocorrelation value of the error signal which is only influenced by the input signals. So this algorithm can accurately reflect the adaptive state and make the weight vector approach the best value. The algorithm not only performs faster in convergence speed and tracking speed, but also has better steady-state performance even in low signal to noise ratio (SNR) environment. Theoretical analysis and computer simulation show that this algorithm outperformed the other algorithms described in this article.

Channel estimation which utilizes OFDM is one of the key issues to be resolved in broadband wireless access. A channel estimation algorithm is proposed on account of the fast time-varying characteristics in wireless channel. Firstly, the rough estimation to the pilot frequency information is acquired by LS algorithm. Then fractal interpolation is applied to the impulse response of the pilot frequency point channel estimated. Finally the accurate estimation results are received by adaptive filtering. Simulation results illustrate that the algorithm can estimate the properties of wireless channel effectively, and reduce the error rate. So this algorithm has a good channel estimation performance.

According to network selection requirements for heterogeneous wireless networks, combining with the advantages and disadvantages of fuzzy logic algorithm, and using information fusion technology,a triangle module operator and fuzzy logic based handoff algorithm for heterogeneous wireless networks is designed in this paper. The proposed algorithm fused the result of the traditional signal strength based algorithm with the quality of service (QoS) parameters based algorithm to achieve the network handoff selection. The simulation results showed that the method overcame the shortcomings of traditional methods, which can not only make a more reasonable choice of network, but also ensure the QoSof the network and effectively reduce the switching frequency and the ping-pong effect.

This paper proposes the improvements of the new structure of a brushless DC (BLDC) motor for the ceiling fan. The new type ceiling fan is first designed by using BLDC servo motor theory and the six-step wave form for the purpose of the manufacture cost reduction. The energy also can be saved at least 50% when the new type motor is compared to the conventional fan based on the induction motor. The new BLDC motor is required to meet both of the basic ceiling fan control and the cost reduction.

This paper presents the analysis of the equivalent 10-Hz voltage flicker measurement ( Delta V10) based on the IEC flickermeter standard. The Delta V10 method has been employed to evaluate voltage flicker for more than two decades in several Asian countries and not been updated since 1978. Therefore, a careful inspection from various aspects was made against the Delta V10 method. The results uncover the deficiencies in the reliability of the Delta V10 method for voltage flicker measurement.

OFDM has become the hotspot in the research of wireless communication, and channel estimation is one of the key techniques of OFDM. In this paper, a channel estimation algorithm based on iterative interpolation for wireless communication is proposed. This algorithm first obtains initial estimate of the next receiving symbol, then the precise estimate is acquired by the amendment of the frequency response of the symbols. The simulation results demonstrate that this algorithm can effectively track the channel properties, which leads to its good performance in channel estimation.

A new two-dimensional chaotic system is proposed and is used in the secure communication in this paper. Because of the high Lyapunov exponent, complex chaotic signal and the nonlinear spread spectrum technology, it enhances the randomness and unpredictability of chaos carrier signals. Based on it, a new chaotic masking and encrypted method is developed. The simulation results show that the encryption effect is well, and the recovered information signal is less different from the original information signal. This system has the high safety performance and wide application.

A novel simple control method to improve the ignition behavior of cold cathode fluorescent lamp in digital-dimming mode is proposed in this paper. The half-bridge resonant inverter is employed in the presented back-light system. To extend the lamp life, we realize a digital-dimming controller with frequency-shift technique (DDC-FST) to reduce the high ignition voltage and to eliminate the ignition current spike. Complete analysis and design considerations are discussed in detail in this paper. Simulation and experimental results are close to the theoretical prediction. The overall efficiency of the system achieved at the rated power is over 91%. The ignition voltage is reduced about 30% without any lamp current spike occurred under digital-dimming operation.

Fuzzy set theory had been successfully used to develop a fuzzy pulse pump controller (FPPC) for a phase/frequency locked loop servomechanism (PFLS). For self-tuning the membership functions and control rules of the FPPC, a genetic algorithm has been used to construct a FPPC. However, this self-tuning approach is time-consuming in obtaining a near optimal solution. In this paper, an easy tuning fuzzy pulse pump controller (ET-FPPC) is proposed for solving the above mentioned problem. ET-FPPC is designed and constructed by using the /spl alpha/-cut based adjustable defuzzification method (/spl alpha/-ADM). Only one parameter needs to be tuned in the ET-FPPC without tuning the membership functions and control rules. Finally, simulation and experiment are conducted and their results are very close to the theoretical prediction. In comparison with the fuzzy pulse pump controller and the adaptive digital pump controller, the acquisition time of the PFLS is reduced by 45% and 57%, respectively.

A three-dimensional chaotic system is presented. The equilibrium point, the chaotic attractor and the Lyapunov exponent are investigated. According to the chaotic system, we propose a novel chaos synchronization method in which only the amplitude of chaotic signal is used to realize chaos synchronization. This feature can be used in chaotic communication system to improve the communication quality and speed. By computing the conditional Lyapunov exponents and numerical simulation the synchronization method is proved to be feasible and effective. The synchronous speed of the method is the same as that of the traditional linear feedback method.

A new four-dimensional chaotic system is reported in this paper. Basic dynamic properties of the new system are investigated via theoretical analysis，numerical simulation，Lyapunov exponent，Lyapunov dimension and Poincare diagrams. Finally the chaotic circuit is designed and realized by the Multisim software. It confirms that the chaotic system is different from the exisiting chaotic systems and is a new hyperchaotic system.