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TransCell:In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning

Genomics, Proteomics & Bioinformatics 2024年第2期22卷 157-170页

作者： Shan-Ju Yeh Shreya Paithankar Ruoqiao Chen Jing Xing Mengying Sun Ke Liu Jiayu Zhou Bin Chen department of Pediatrics and Human Development Michigan State UniversityGrand RapidsMI 49503USA department of Pharmacology and Toxicology Michigan State UniversityGrand RapidsMI 49503USA department of computer Science and engineering Michigan State UniversityEast LansingMI 48824USA

Gene expression profiling of new or modified cell lines becomes routine today;however,obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines,including those derived from underrepresented groups,is not trivial when resources are *** gene expression to predict other measurements has been actively explored;however,systematic investigation of its predictive power in various measurements has not been well ***,we evaluated commonly used machine learning methods and presented TransCell,a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and *** these models,TransCell had the best performance in predicting metabolite,gene effect score(or genetic dependency),and drug sensitivity,and had comparable performance in predicting mutation,copy number variation,and protein ***,TransCell improved the performance by over 50%in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score ***,predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines,and predicted gene effect scores reflected BRAF resistance in melanoma cell ***,we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal(http://***/transcell/)that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

关键词： Genomics Transcriptomics Cancer dependency Drug repurposing Transfer learning

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Storing Freshwater Versus Storing Electricity in Power Systems with High Freshwater Electric Demand

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 323-333页

作者： Mubarak J.Al-Mubarak Antonio J.Conejo department of electrical and computer engineering The Ohio State UniversityColumbusOHUSA department of electrical engineering Kuwait UniversityP.O.Box 5969Safat 13060Kuwait department of Integrated Systems engineering and the department of electrical and computer engineering The Ohio State UniversityColumbusOH 43210USA IEEE

We consider a power system whose electric demand pertaining to freshwater production is high(high freshwater electric demand),as in the Middle east,and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation *** storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours,which is generally beneficial in term of cost and ***,to what extent?We analyze this question considering three power systems with different generation-mix configurations,i.e.,a thermal-dominated mix,a renewable-dominated one,and a fully renewable *** generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle *** production uncertainty is compactly modeled using chance *** draw conclusions on how both storage facilities(freshwater and electricity)complement each other to render an optimal operation of the power system.

关键词： Power system operation freshwater production and transportation coordination day-ahead scheduling

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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

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A Review of state-of-the-art Flexible Power Point Tracking Algorithms in Photovoltaic Systems for Grid Support:Classification and Application

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 1-21页

作者： Mina Haghighat Mehdi Niroomand Hossein Dehghani Tafti Christopher D.Townsend Tyrone Fernando the department of electrical engineering University of IsfahanIsfahanIran the department of electrical Electronic and Computer EngineeringUniversity of Western AustraliaCrawley WA 6009Australia

To maximize conversion efficiency,photovoltaic(PV)systems generally operate in the maximum power point tracking(MPPT)***,due to the increasing penetra tion level of PV systems,there is a need for more developed control functions in terms of frequency support services and voltage control to maintain the reliability and stability of the power ***,flexible active power control is a manda tory task for grid-connected PV systems to meet part of the grid ***,a significant number of flexible pow er point tracking(FPPT)algorithms have been introduced in the existing *** purpose of such algorithms is to real ize a cost-effective method to provide grid support functional ities while minimizing the reliance on energy storage *** paper provides a comprehensive overview of grid support functionalities that can be obtained with the FPPT control of PV systems such as frequency support and volt-var *** of these grid support functionalities necessitates PV sys tems to operate under one of the three control strategies,which can be provided with FPPT *** three control strate gies are classified as:①constant power generation control(CP GC),②power reserve control(PRC),and③power ramp rate control(PRRC).A detailed discussion on available FPPT algo rithms for each control strategy is also *** paper can serve as a comprehensive review of the state-of-the-art FPPT algorithms that can equip PV systems with various grid support functionalities.

关键词： Maximum power point tracking(MPPT) flexi ble power point tracking(FPPT) power reserve control(PRC) power ramp rate control(PRRC) low voltage ride-through(LVRT) constant power generation(CPG)

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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

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China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of electrical and Electronic engineering Shandong University of TechnologyZibo 255000China department of electrical and computer engineering COMSATS University IslamabadSahiwal Campus department of electrical and computer engineering College of EngineeringDhofar UniversitySalalah 211Oman department of electrical and computer engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia department of electrical engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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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

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and computer engineering North China Electric Power UniversityChina department of Mathematics&computer Science Fayetteville State UniversityFayettevilleNCUSA department of electrical and computer engineering Stevens Institute of TechnologyHobokenNJ07030USA department of computer Science and engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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A Comprehensive Review on Charging Topologies and Power Electronic Converter Solutions for Electric Vehicles

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 675-694页

作者： Abdelfatah Ali Hossam H.H.Mousa Mostafa F.Shaaban Maher A.Azzouz Ahmed S.A.Awad the department of electrical engineering American University of Sharjah26666 SharjahUnited Arab Emirates the department of electrical engineering South Valley University83523 QenaEgypt the department of electrical and computer engineering University of WindsorWindsorON N9B 3P4 Canada electrical engineering department Qatar UniversityDoha 2713Qatar department of electrical and computer engineering Sultan Qaboos UniversityMuscat123Oman

Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)***,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)*** electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in ***,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV *** examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power *** can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC *** address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV ***,power-train converters have been modified for high-efficiency and reliability in EV *** paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.

关键词： Charging station charging topology electric vehicle power electronic converter vehicle-to-grid

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Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System state Estimation

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1126-1134页

作者： Behrouz Azimian Shiva Moshtagh Anamitra Pal Shanshan Ma School of electrical Computer and Energy EngineeringArizona State UniversityTempeAZ 85281USA the School of electrical Computer and Energy EngineeringArizona State UniversityTempeAZ 85281USA Quanta Technology RaleighNC 27607USA

Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution *** this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input *** has already been shown that evaluating perfor-mance based only on the test dataset might not effectively indi-cate the ability of a trained DNN to handle input *** such,we analytically verify the robustness and trustworthi-ness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)*** ability of batch normalization in addressing the scalability limitations of the MILP formulation is also *** framework is val-idated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.

关键词： Deep neural network(DNN) distribution system state estimation(DSSE) mixed-integer linear programming(MILP) robustness trustworthiness

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Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

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Cybersecurity 2024年第1期7卷 1-15页

作者： Armita Kazeminajafabadi Mahdi Imani department of electrical and computer engineering Northeastern UniversityBostonMAUSA

Early attack detection is essential to ensure the security of complex networks,especially those in critical *** is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network *** attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the *** attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of *** paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of *** exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman *** adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined *** and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

关键词： Multi-stage attacks Bayesian attack graph Attack detection Optimal monitoring

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