Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still,the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First,the XOR function will be applied to plain text,and then salt technique will be used.Finally,a reversing mechanism will be implemented on that data to provide more data security.To decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be implemented.At the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities,people are more inclined towards the use of online services.The service providers shelve their items for e-users.These users post their feedbacks,reviews,ratings,etc.after the use of the item.The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items.Sentiment Analysis(SA)is a technique that performs such decision analysis.This research targets the ranking and rating through sentiment analysis of these reviews,on different aspects.As a case study,Songs are opted to design and test the decision model.Different aspects of songs namely music,lyrics,song,voice and video are picked.For the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a dataset.Different machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are applied.ANN performed the best with 74.99%accuracy.Results are validated using K-Fold.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

汽车零件淬火技术模拟的动向

淬火技术,从齿轮的简单浸淬到复杂的压淬,在汽车制造业中的应用非常广泛。由于在制造过程中会发生复杂的物理现象,所以为了获得要求的性能,淬火工艺参数的选定也很复杂。近几十年来,已成功地采用计算机模拟来优化热处理工艺。本文列举了一些实例来阐述模拟技术的状况,包括淬火过程的物理现象,用于预测汽车零件热处理后的显微组织、力学性能和畸变的耦合模型的理论背景。

Applying ANN,ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO_(2)

In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.

Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Emotion detection from the text is a challenging problem in the text analytics.The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions.However,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance degradation.To overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset.The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad f-measure.Finally,a classifier with the best performance is recommended for the emotion classification.

Effect of Ultrasound on the Pseudoelasticity of Shape Memory Alloys

The paper aims to develop a model describing the ultrasound-assisted pseudoelastic deformation of shape memory alloys. Experimental results record that acoustic energy reduces the value of stresses needed to induce pseudoelastic deformation (martensitic transformation). At the same time, the ultrasound-assisted deforming develops with a more significant strain hardening. The model presented here is based on the synthetic theory of inelastic deformation. To catch the phenomena caused by acoustic energy, we enter into the basic equation of the synthetic theory terms reflecting the effect of ultrasound on the processes governing the peculiarities of pseudoelastic deformation in the acoustic field. The analytical results fit good experimental data.

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.Similarly,the field of health informatics is also considered as an extremely important field.This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis.The developed system has two front ends,the first dedicated for the user to perform the photographing of the trace report.Once the photographing is complete,mobile computing is used to extract the signal.Once the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the cloud.Once this is done,the second interface,intended for the use of the physician,can download and view the trace from the cloud.The data is securely held using a password-based authentication method.The system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

Comparison of Three Image Classification Methods in Urban Environment

The article is dealing with different classification methods applied for urban aerial photos having visible and infrared channels. An accuracy assessment was carried out to compare the results gained from different classification methods.

Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems.This study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process.The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark functions.Moreover,GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness.In addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization methods.The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and RCGA.Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

Critical roles of control engineering in the development of intelligent and connected vehicles

1 Introduction In recent years,advancements in onboard computing hardware and wireless communication technology have remarkably stimulated the development of intelligent and connected vehicles(ICVs).Specifically,some researchers have investigated the issue of employing various advanced control techniques to optimize the performance of autonomous vehicles in practice(Sun et al.,2023;Zhang et al.,2023a,2023b).Therefore,this article aims to discuss why and how control engineering plays an essential role in the development of ICVs.

A Global Best-guided Firefly Algorithm for Engineering Problems

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating.This article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement process.The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution.However,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main loop.Additionally,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA.The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values.Additionally,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms.In all cases,GbFA provides the optimal result compared to other methods.Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa.

基于多源遥感的建筑区绿化垂直分布特征与空间配置分析--以匈牙利塞克什白堡市为例

为定量描述城市绿化垂直分布特征及其与建筑空间的数量配置关系,以匈牙利塞克什白堡市为例,基于多源遥感数据提取城市绿地和建筑物三维信息,采取垂直空间高度抽样方法,量化不同高度层上绿色空间和建筑空间的分布数量,并依据二者配置的数量关系,将高度空间划分为"相对匮乏层"和"相对饱和层",对比分析了不同功能区的垂直空间配置特征,及其与建筑群结构的相关性。研究结果表明:(1)住宅区和商业区的绿色空间垂直配置结构相似,但住宅区在配置数量上占优;(2)因配置数量不足、垂直配置结构单一等因素,导致商业区三层及以下建筑位置在绿化配置数量上处于较低水平;住宅区因高空绿化配置数量远少于高层建筑数量,存在"高空绿色匮乏"现象;(3)高密度的低建筑群是造成商业区"低空绿色匮乏"的主要建筑结构来源;而忽视高建筑物低密度区的"高空绿化"则是造成住宅区绿色空间匮乏的首要原因。

An effective Building Neighborhood Green Index model for measuring urban green space

Urban green space forms an integral part of urban ecosystems.Quantifying urban green space is of substantial importance for urban planning and development.Considering the drawbacks of previous urban green space index models,which established either through a grid method or green distribution,and the difficulty of the validation process of earlier urban green space index models,this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index(BNGI)model.The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters–Green Index(GI),proximity to green,building sparsity,and building height.Comparing BNGI with GI in different types of characteristic building regions,it was found that BNGI evaluates urban green space more sensitively.It was also found that high-rise low-sparsity area has a lower mean value of BNGI(0.56)as compared with that of low-rise low-sparsity neighborhood(0.62),whereas mean GI(0.24)is equal for both neighborhoods.Taking characteristics of urban building and green types into consideration,BNGI model can be effectively used in many fields such as land suitability analysis and urban planning.

Infrared neural stimulation and inhibition using an implantable silicon photonic microdevice

Brain is one of the most temperature sensitive organs.Besides the fundamental role of temperature in cellular metabolism,thermal response of neuronal populations is also significant during the evolution of various neurodegenerative diseases.For such critical environmental factor,thorough mapping of cellular response to variations in temperature is desired in the living brain.So far,limited efforts have been made to create complex devices that are able to modulate temperature,and concurrently record multiple features of the stimulated region.In our work,the in vivo application of a multimodal photonic neural probe is demonstrated.Optical,thermal,and electrophysiological functions are monolithically integrated in a single device.The system facilitates spatial and temporal control of temperature distribution at high precision in the deep brain tissue through an embedded infrared waveguide,while it provides recording of the artefact-free electrical response of individual cells at multiple locations along the probe shaft.Spatial distribution of the optically induced temperature changes is evaluated through in vitro measurements and a validated multi-physical model.The operation of the multimodal microdevice is demonstrated in the rat neocortex and in the hippocampus to increase or suppress firing rate of stimulated neurons in a reversible manner using continuous wave infrared light(λ=1550 nm).Our approach is envisioned to be a promising candidate as an advanced experimental toolset to reveal thermally evoked responses in the deep neural tissue.