Reliability improvement in distribution systems employing an integrated voltage sag mitigation method using binary gravitational search algorithm

A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.

A Comprehensive Classification and Analysis of Geomagnetic Storms Over Solar Cycle 24

A geomagnetic storm is a global disturbance of Earth?s magnetosphere,occurring as a result of the interaction with magnetic plasma ejected from the Sun.Despite considerable research,a comprehensive classification of storms for a complete solar cycle has not yet been fully developed,as most previous studies have been limited to specific storm types.This study,therefore,attempted to present complete statistics for solar cycle 24,detailing the occurrence of geomagnetic storm events and classifying them by type of intensity(moderate,intense,and severe),type of complete interval(normal or complex),duration of the recovery phase(rapid or long),and the number of steps in the storm?s development.The analysis was applied to data from ground-based magnetometers,which measured the Dst index as provided by the World Data Center for Geomagnetism,Kyoto,Japan.This study identified 211 storm events,comprising moderate(177 events),intense(33 events),and severe(1 event)types.About 36%of ICMEs and 23%of CIRs are found to be geoeffective,which caused geomagnetic storms.Up to four-step development of geomagnetic storms was exhibited during the main phase for this solar cycle.Analysis showed the geomagnetic storms developed one or more steps in the main phase,which were probably related to the driver that triggered the geomagnetic storms.A case study was additionally conducted to observe the variations of the ionospheric disturbance dynamo(Ddyn)phenomenon that resulted from the geomagnetic storm event of 2015July 13.The attenuation of the Ddyn in the equatorial region was analyzed using the H component of geomagnetic field data from stations in the Asian sector(Malaysia and India).The variations in the Ddyn signatures were observed at both stations,with the TIR station(India)showing higher intensity than the LKW station(Malaysia).

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

基于消息代理的OPC UA发布/订阅模式研究与实现

针对目前OPC UA客户端/服务器通信模式中存在的紧耦合、服务器性能瓶颈等问题,进行了OPC UA发布/订阅通信模式总体架构的研究。首先具体分析了UA的核心功能,包括地址空间技术和数据编码技术,并在两者的基础上,利用消息代理机制,完成了发布者和订阅者的功能开发。还通过实验进一步验证了功能的稳定性和对数据的传输能力,结果表明,此模式可以满足大多数工业需求。

基于粒子滤波算法的锂离子电池剩余寿命预测方法研究

运用粒子滤波算法,进行了锂离子电池剩余寿命(RUL)的预测,提出了一种基于模型法和数据驱动法相融合的简单有效的RUL预测方法。该方法通过模型法和数据驱动法的融合,将双指数经验退化模型进行变形,以减少模型参数,降低参数训练难度,利用粒子滤波算法跟踪电池容量衰退的过程;为提高预测精确度,引入自回归(AR)时间序列模型修正状态空间方程的观测值。实验证实,该方法可以有效地预估出锂电池的剩余寿命。

Topology, Size, and Shape Optimization in Civil Engineering Structures: A Review

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

Power system stabilizer design using hybrid multi-objective particle swarm optimization with chaos

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.

NOx Reduction and Desorption Studies Under Electric Discharge Plasma Using a Simulated Gas Mixture:A Case Study on the Effect of Corona Electrodes

In this study, reduction and desorption of oxides of nitrogen （NOx） were conducted using an electrical discharge plasma technique. The study was carried out using a simulated gas mixture to explore the possibility of re-generation of used adsorbents by a nonthermal plasma desorption technique. Three different types of corona electrodes, namely, pipe, helical wire, and straight wire, were used for analyzing their effectiveness in NOx reduction/desorption. The pipe- type corona electrode exhibited a nitric oxide （NO） conversion of 50%, which is 1.5 times that of the straight-wire-type electrode at an energy density of 175 J/L. The helical-wire-type corona electrode exhibited a NOx desorption efficiency almost 4 times that of the pipe-type electrode, indicating the possibility that corona-generated species play a crucial role in desorption.

Comparative study of effectiveness of different var compensation devices in large-scale power networks

A comparison of the effectiveness of installing reactive power compensators,such as shunt capacitors,static var compensators(SVCs),and static synchronous compensators(STATCOMs),was presented in large-scale power networks.A suitable bus was first identified using modal analysis method.The single shunt capacitor,single SVC,and single STATCOM were installed separately on the most critical bus.The effects of the installation of different devices on power loss reduction,voltage profile improvement,and voltage stability margin enhancement were examined and compared for 57-and 118-bus transmission systems.The comparative study results show that SVC,and STATCOM are expensive compared to shunt capacitor,yet the effect of installing STATCOM is better than SVC and the effect of installing SVC is better than that of shunt capacitor in achieving power loss reduction,voltage profile improvement and voltage stability margin enhancement.

Gravitational search algorithm for coordinated design of PSS and TCSC as damping controller

A newly developed heuristic global optimization algorithm, called gravitational search algorithm （GSA）, was introduced and applied for simultaneously coordinated designing of power system stabilizer （PSS） and thyristor controlled series capacitor （TCSC） as a damping controller in the multi-machine power system. The coordinated design problem of PSS and TCSC controllers over a wide range of loading conditions is formulated as a multi-objective optimization problem which is the aggregation of two objectives related to damping ratio and damping factor. By minimizing the objective function with oscillation, the characteristics between areas are contained and hence the interactions among the PSS and TCSC controller under transient conditions are modified. For evaluation of effectiveness and robustness of proposed controllers, the performance was tested on a weakly connected power system subjected to different disturbances, loading conditions and system parameter variations. The cigenvalues analysis and nonlinear simulation results demonstrate the high performance of proposed controllers which is able to provide efficient damping of low frequency oscillations.

Power swing and voltage collapse identification schemes for correct distance relay operation in power system

The new techniques were presented for preventing undesirable distance relay maloperation during voltage collapse and power swings in transmission grids. Initially, the work focused on the development of a fast detection of voltage collapse and a three-phase fault at transmission lines by using under impedance fault detector (UIFD) and support vector machine (SVM). Likewise, an intelligent approach was developed to discriminate a fault, stable swing and unstable swing, for correct distance relay operation by using the S-transform and the probabilistic neural network (PNN). To illustrate the effectiveness of the proposed techniques, simulations were carried out on the IEEE 39-bus test system using the PSS/E and MATLAB software.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Steganalysis Using Fractal Block Codes and AP Clustering in Grayscale Images

This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.

Identification of voltage sag source location using S and TT transformed disturbance power

An alternative method was introduced for voltage sag source location based on S and TT transformed disturbance powers. It is done to avoid the wrong and inconclusive detection of conventional disturbance power method proposed in the literature. Unlike in the case of the traditional method, the proposed method first transforms the recorded voltage and current during the sag event to some special features before calculating the new version of disturbance powers. The effectiveness of the proposed method has been verified through simulation and actual data from an industrial power system. The results show that the presented method can correctly detect the location of voltage sag source.

Metamaterial-Based Compact Antenna with Defected Ground Structure for 5G and Beyond

In this paper,a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure(DGS)is investigated as the principle radiating element of an antenna.The unit cell antenna causes even or odd mode resonances similar to the unit cell structure depending on the orientation of the microstrip feed used to excite the unit cell.However,the orientation which gives low-frequency resonance is considered here.The unit cell antenna is then loaded with a meander line which is parallel to the split bearing side and connects the other two sides orthogonal to the split bearing side.This modified structure excites another mode of resonance at high frequency when a meander line defect is loaded on the metallic ground plane.Specific parameters of the meander line structure,the DGS shape,and the unit cell are optimized to place these two resonances at different frequencies with proper frequency intervals to enhance the bandwidth.Finally,the feed is placed in an offset position for better impedance matching without affecting the bandwidth The compact dimension of the antenna is 0.25λL×0.23λL×0.02λL,whereλL is the free space wavelength with respect to the center frequency of the impedance bandwidth.The proposed antenna is fabricated and measured.Experimental results reveal that the modified design gives monopole like radiation patterns which achieves a fractional operating bandwidth of 26.6%,from 3.26 to 4.26 GHz for|S11|<−10 dB and a pick gain of 1.26 dBi is realized.In addition,the simulated and measured crosspolarization levels are both less than−15 dB in the horizontal plane.

A compact transmit/receive switch for 2.4 GHz reader-less active RFID tag transceiver

Radio frequency identification(RFID) is a ubiquitous identification technology nowadays. An on-chip high-performance transmit/receive(T/R) switch is designed and simulated in 0.13-μm CMOS technology for reader-less RFID tag. The switch utilizes only the transistor width and length(W/L) optimization, proper gate bias resistor and resistive body floating technique and therefore,exhibits 1 d B insertion loss, 31.5 d B isolation and 29.2 d Bm 1-d B compression point(P1d B). Moreover, the switch dissipates only786.7 n W power for 1.8/0 V control voltages and is capable of switching in 794 fs. Above all, as there is no inductor or capacitor used in the circuit, the size of the switch is 0.00208 mm2 only. This switch will be appropriate for reader-less RFID tag transceiver front-end as well as other wireless transceivers operated at 2.4 GHz band.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Review of the structure and performance of through-holed anodes and cathodes prepared with a picosecond pulsed laser for lithium-ion batteries

To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review paper,the employment of through-holing structures of anodes and cathodes prepared with a picosecond pulsed laser has been proposed.The laser system and the structure for improving the battery performance were introduced.The performance of laminated cells constructed with through-holed anodes and cathodes was reviewed from the viewpoints of the improvement of high-rate performance and energy density,removal of unbalanced capacities on both sides of the current collector,even greater high-rate performance by hybridizing cathode materials and removal of irreversible capacity.In conclusion,the points that should be examined and the problem for the through-holed structure to be in practical use are summarized.

Target Capturing in an Ellipsoidal Region for a Swarm of Double Integrator Agents

In this paper we focus on the target capturing problem for a swarm of agents modelled as double integrators in any finite space dimension.Each agent knows the relative position of the target and has only an estimation of its velocity and acceleration.Given that the estimation errors are bounded by some known values,it is possible to design a control law that ensures that agents enter a user-defined ellipsoidal ring around the moving target.Agents know the relative position of the other members whose distance is smaller than a common detection radius.Finally,in the case of no uncertainty about target data and homogeneous agents,we show how the swarm can reach a static configuration around the moving target.Some simulations are reported to show the effectiveness of the proposed strategy.