Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

Automated Spam Review Detection Using Hybrid Deep Learning on Arabic Opinions

Online reviews regarding purchasing services or products offered are the main source of users’opinions.To gain fame or profit,generally,spam reviews are written to demote or promote certain targeted products or services.This practice is called review spamming.During the last few years,various techniques have been recommended to solve the problem of spam reviews.Previous spam detection study focuses on English reviews,with a lesser interest in other languages.Spam review detection in Arabic online sources is an innovative topic despite the vast amount of data produced.Thus,this study develops an Automated Spam Review Detection using optimal Stacked Gated Recurrent Unit(SRD-OSGRU)on Arabic Opinion Text.The presented SRD-OSGRU model mainly intends to classify Arabic reviews into two classes:spam and truthful.Initially,the presented SRD-OSGRU model follows different levels of data preprocessing to convert the actual review data into a compatible format.Next,unigram and bigram feature extractors are utilized.The SGRU model is employed in this study to identify and classify Arabic spam reviews.Since the trial-and-error adjustment of hyperparameters is a tedious process,a white shark optimizer(WSO)is utilized,boosting the detection efficiency of the SGRU model.The experimental validation of the SRD-OSGRU model is assessed under two datasets,namely DOSC dataset.An extensive comparison study pointed out the enhanced performance of the SRD-OSGRU model over other recent approaches.

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in Computer Networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

基于效用EPON分布式动态带宽分配实现机制

EPON(Ethernet-based passive optical network)作为基于光纤的宽带网络接入技术,已经成为下一代接入网络的关键技术之一,但是IEEE 802.3ah EPON的Polling机制存在带宽使用率不高的问题,其DBA(dynamic bandwidth allocation)算法产生UWR(unused window remainder),USR(unused slot remainder),UQR(unused queue remainder)和UPR(unused package remainder),浪费了许多带宽资源.IPACT(interleaved polling with adaptive cycle time)EPON提出了一个带宽使用率较高的新的Poling机制,但没有解决DBA算法存在的缺点.在IPACT基础上,提出了一个基于效用的分布式EPON DBA实现机制,实现了对不同SLA(service level agreement)用户的相同业务类应用的差分处理.通过一种集中递归效用算法,有效地消除了再生UWR,通过一种分布式递归效用算法,有效地消除了UQR,通过一个分布式UPR消除机制,减少了UPR.提出了一个基于交织接力棒的USR消除机制,提高了消除USR条件满足的概率,通过将交棒者的USR追加到接棒者的授权中,提高了带宽的使用效率.仿真结果很好地验证了该机制的优点.

Reliable Braided Multipath Routing with Network Coding for Underwater Sensor Networks

Owing to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for underwater sensor networks. Moreover, network coding is proved to be an effective coding technique for throughput and robustness of networks. In this paper, we propose a Reliable Braided Multipath Routing with Network Coding for underwater sensor networks (RBMR-NC). Disjoint multi-path algorithm is used to build independent actual paths, as called main paths. Some braided paths on each main path are built according to the braided multi-path algorithm, which are called logic paths. When a data packet is transmitted by these nodes, the nodes can employ network coding to encode packets coming from the same group in order to further reduce relativity among these packets, and enhance the probability of successful decoding at the sink node. Braided multi-path can make the main paths to be multiplexed to reduce the probability of long paths. This paper mainly employs successful delivery rate to evaluate RBMR-NC model with theoretical analysis and simulation methods. The results indicate that the proposed RBMR-NC protocol is valuable to enhance network reliability and to reduce system redundancy.

Nested Transaction Concurrency Control in Parallel Real-Time Dat-abases

An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.

Design of Greenhouse Environment Control System Based on Internet of Things

There are some disadvantages, such as complicated wiring, high cost, poor monitoring flexibility, low accuracy and high energy consumption in traditional greenhouse environment monitoring system which based on previous wireless sensor networks(WSN). Aiming at these problems, a greenhouse environmental parameter monitoring system had been designed based on internet of things technology in this paper. A set of control system with good robustness, strong adaptive ability and small overshoot was set up by combining the fuzzy proportion-integral-derivative(PID) control. The system was composed of a number of independent greenhouse monitoring systems. The server could provide remote monitoring access management services after the collected data were transmitted. The data transmission part of greenhouse was based on Zig Bee networking protocol. And the data were sent to intelligent system via gateway connected to the internet. Compared to the classical PID control and fuzzy control, the fuzzy PID control could quickly and accurately adjust the corresponding parameters to the set target. The overshoot was also relatively small. The simulation results showed that the amount of overshoot was reduced 20% compared with classical PID control.

TWO PRIVACY-PRESERVING PROTOCOLS FOR POINT-CURVE RELATION

Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is essential and critical. In this paper, we have solved two problems regarding to how to determine the position relation between points and curves without revealing any private information. Two protocols have been proposed in order to solve the problems in different conditions. In addition, some building blocks have been developed, such as scalar product protocol, so that we can take advantage of them to settle the privacy-preserving computational geometry problems which are a kind of special secure multi-party computation problems. Moreover, oblivious transfer and power series expansion serve as significant parts in our protocols. Analyses and proofs have also been given to argue our conclusion.

Comparison of two theoretical models for electric leak location at landfill

解决当测量电极在班机下面被埋葬时，在电的漏缝地点使用的传统的模型的残疾，高电压 DC 漏缝察觉的一个新模型被开发。为更只限於一种商品的垃圾堆，在班机下面的浪费材料层， geomembrane 班机和土壤与无限的水平层被模仿。漏缝被认为是二部分，一是否定的在入口的当前的来源，和另外的积极电流在出口一样的尺寸采购原料。在新理论模型和传统的模型之间的比较证明那个传统的模型在在联系在班机上面扫描的偶极子的 geomembane 班机定位漏缝是有效的，但是当测量电极在班机下面被埋葬时，是无效的。新理论模型数据不仅在班机上面而且在班机下面在对试验性的数据的优秀同意。

Analysis of Detectors and Transmission Curve Correction of Mobile Rayleigh Doppler Wind Lidar

一架活动分子的 Doppler 风激光雷达(DWL ) 从 10km 在高度为风测量基于双边技术被介绍给 40km。三倍的 Fabry-Perot etalon 作为一个频率辨别者被采用决定与风速度成正比的 Doppler 移动。激光雷达用一个 45 厘米的孔望远镜和能提供完整的半球的指的一台匹配的 azimuth-over-elevation 扫描仪在 355 nm 操作。以便保证风精确性，在不同计数率反应范围的察觉者的刻度功能的不同形式将是特别珍贵的。因为线性的刻度函数的申请，风速度重复的精确性从 10km 特别在高度在极端低轻紧张极大地被改进到 40km。非线性的刻度功能分别地以大约 1 M 计数率使边隧道 1 和边隧道 2 增加的传播成为 38.9% 和 27.7% 。因为察觉者的反应功能的考虑，风域测量的动态范围可以也在他们的全可能的操作范围被扩大。

Using SVM to construct a Chinese dependency parser

In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

Identification of the Potential Function of circRNA in Hypertrophic Cardiomyopathy Based on Mutual RNA-RNA and RNA-RBP Relationships Shown by Microarray Data

The pathogenesis of hypertrophic cardiomyopathy(HCM)is very complicated,particularly regarding the role of circular RNA(circRNA).This research pays special attention to the relationships of the circRNA-mediated network,including RNA-RNA relationships and RNA-RNA binding protein(RNA-RBP)relationships.We use the parameter framework technology proposed in this paper to screen differentially expressed circRNA,messenger RNA(mRNA),and microRNA(miRNA)from the expression profile of samples related to HCM.And 31 pairs of circRNA and mRNA relationship pairs were extracted,combined with the miRNA targeting database;145 miRNA-mRNA relationship pairs were extracted;268 circRNA-mRNA-miRNA triads were established through the common mRNA in the 2 types of relationship pairs.Thus,268 circRNA-miRNA regulatory relationships were deduced and 30 circRNARBP relationship pairs were analyzed at the protein level.On this basis,a circRNA-mediated regulatory network corresponding to the two levels of RNA-RNA and RNA-RBP was established.And then the roles of circRNA in HCM were analyzed through circRNA-mRNA,circRNA-miRNA,and circRNA-RBP,and the possible role in disease development mas inferred.

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.

The Effect of Queuing Mechanisms First in First out (FIFO), Priority Queuing (PQ) and Weighted Fair Queuing (WFQ) on Network’s Routers and Applications

The paper presents the simulation results of the comparison of three Queuing Mechanisms, First in First out (FIFO), Priority Queuing (PQ), and Weighted Fair Queuing (WFQ). Depending on their effects on the network’s Routers, the load of any algorithm of them over Router’s CPUs and memory usage, the delay occurred between routers when any algorithm has been used and the network application throughput. This comparison explains that, PQ doesn’t need high specification hardware (memory and CPU) but when used it is not fair, because it serves one application and ignore the other application and FIFO mechanism has smaller queuing delay, otherwise PQ has bigger delay.

A NEW TWO-WAY AUTHENTICATION AND PRIVACY MECHANISM FOR WLAN SECURITY

Objective Aiming at lots of vulnerabilities in the Wired Equivalent Privacy (WEP) which threats the authentication and confidentiality in wireless communication, a new kind of mutual authentication and privacy mechanism named MWEP(Modified WEP) is proposed. Methods MWEP is based on pseudo random number generator (PRNG) and asymmetric cryptograph approach, it generates a unique session key like “One Time Password” for each data frame transmission between any two mobile stations. Results Using this session key to encrypt the transmission data, not only can it avoid replay attack, but also provide a good secure virtual channel for the sender and receiver. MWEP can be incorporated into IEEE 802.11. Conclusion It shows that the proposed mechanism is effective and practical after comparison with WEP and simulation.