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Interworking between Modbus and internet of things platform for industrial services

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Digital Communications and Networks 2024年第2期10卷 461-471页

作者： Sherzod Elamanov Hyeonseo Son Bob Flynn Seong Ki Yoo Naqqash Dilshad JaeSeung Song department of Convergence Engineering for Intelligent Drone Sejong UniversitySouth Korea SyncTechno Inc South Korea Exacta global smart Solutions USA Coventry University UK

In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial *** to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and *** fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous *** of the major protocols widely used today in industrial electronic devices is ***,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer ***2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial ***,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M *** the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this *** analyze our solution and provide an evaluation by implementing it based on a solar power management use *** results demonstrate that our model is feasible and can be applied to real case scenarios.

关键词： Internet of things Interoperability Interworking Modbus oneM2M

Detecting XSS with Random Forest and Multi-Channel Feature Extraction

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Computers, Materials & Continua 2024年第7期80卷 843-874页

作者： Qiurong Qin Yueqin Li Yajie Mi Jinhui Shen Kexin Wu Zhenzhao Wang smart city College Beijing Union UniversityBeijing100101China

In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal *** these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by ***,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low *** address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature *** model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information *** feature extraction is conducted through three parallel *** I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global ***,effective classification and prediction of XSS are performed by fusing the features of the three *** test the effectiveness of the model,we conduct experiments on six *** achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.

关键词： Random forest feature enhancement three-channel parallelism XSS detection

A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng Dong Yonghua Song College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Internet of Things for smart city and department of Electrical and Computer Engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first *** that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is *** with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are ***,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power *** experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark ***,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional ***,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for smart city and department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable *** to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their *** tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible ***,since ADNs are mostly operated in a normal status,only very few historical samples are ***,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network *** address this issue,we propose an enhanced constraint learning ***,it leverages constraint learning to train a neural network as surrogate of ADN's ***,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical *** incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural ***,we establish a trust region to constrain and thereafter enhance reliability of the *** confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow

Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 535-546页

作者： Yanqi Liu Dundun Liu Hongcai Zhang State Key Laboratory of Internet of Things for smart city and department of Electrical and Computer Engineering University of MacaoMacao 999078China University of Macao Zhuhai UM Science and Technology Research Institute ZhuhaiChina

To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon ***,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal *** published papers only consider predefined typhoon trajectories neglecting *** address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon *** adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon *** formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.

关键词： Unit commitment two-stage stochastic programming offshore wind farm typhoon

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

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Computers, Materials & Continua 2024年第1期78卷 735-757页

作者： Yang Yang Zhenying Qu Zefan Yan Zhipeng Gao Ti Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Product Development department China Unicom Smart City Research InstituteBeijing100044China

Nowadays,ensuring thequality of networkserviceshas become increasingly *** are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device *** research paper presents a novel entity extraction method that leverages a combination of active learning and attention ***,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert *** approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample *** the labeled samples are utilized to train the model for network configuration entity ***,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture *** enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed *** comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the ***,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,*** tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

关键词： Entity extraction network configuration knowledge graph active learning transformer

Multi-population particle swarm optimization algorithm for automatic design of steel frames

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 89-103页

作者： Wenchen SHAN Jiepeng LIU Yao DING Y.Frank CHEN Junwen ZHOU Institute of smart city and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610000China School of Civil Engineering Chongqing UniversityChongqing 400045China department of Civil Engineering the Pennsylvania State UniversityMiddletownPA 17057USA

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost ***,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design *** are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative *** proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design *** related parameter influences on optimization results are *** the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design *** addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO *** purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design *** aims to provide a reference for automatic structural optimization design problems even with dimensional *** proposed method can be easily generalized to the optimization problem of other structural systems.

关键词： steel frame multi-population particle swarm optimization automatic structural optimization design

Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

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China Communications 2024年第4期21卷 178-192页

作者： Shao Liwei Qian Liping Wu Mengru Wu Yuan College of Information Engineering Zhejiang University of TechnologyHangzhou 310014China State Key Laboratory of Internet of Things for smart city University of MacaoMacaoChina department of Computer and Information Science University of MacaoMacaoChina

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower ***,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery *** this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this ***,we use UAVs to assist the WSN in collecting *** the current UAV-assisted WSN,the clustering and routing schemes are determined ***,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and *** efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing *** the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing ***,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution *** point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor *** avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are *** emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of *** with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

关键词： Monte-Las search strategy triangle path optimization unmanned aerial vehicles wireless sensor networks

Debris flow runout behaviors considering the influences of densely populated buildings

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Journal of Mountain Science 2024年第8期21卷 2696-2712页

作者： ZHANG Shuai FANG Zhe DAI Cong WANG Shuairong PENG Jingyu ZHOU Yiling SHEN Ping MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering Zhejiang UniversityHangzhou 310058China State Key Laboratory of Internet of Things for smart city and department of Ocean Science and Technology University of MacaoMacao SAR 999078China

Debris flows pose serious risks to communities in mountainous areas,often resulting in large losses of human life and *** impeding presence of urban buildings often affects the runout behavior and deposition of debris *** the impact of different building densities and sizes on debris flow dynamics has yet to be quantified to guide urban planning in debris flow risk *** study focused on a debris flow that occurred in Zhouqu County,Gansu Province,China on August 7th,2010,which was catastrophic and destroyed many *** FLO-2D software was used to simulate this debris flow in two scenarios,*** presence and the absence of buildings,to obtain debris-flow intensity *** developed model was then used to further analyze the influence of large buildings and narrow channels within the urban *** simulation results show that considering the presence of buildings in the simulation is essential for accurate assessment of debris flow intensity and deposition *** layout of buildings in the upstream urban area,such as large buildings or parallel buildings which form narrow channels,can affect the flow velocity and depth of debris flow heading towards downstream *** mitigate damage to downstream buildings,the relative spacing(d/a)between upstream and downstream buildings should not exceed a value of two and should ideally be even *** findings provide valuable insights for improving the resistance of mountainous cities to urban debris flows.

关键词： Debris flow Risk Building blockage effect Zhouqu Urban layout