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Decentralized control in active distribution grids via supervised and reinforcement learning

Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

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Computer Modeling in engineering & sciences 2024年第2期138卷 1427-1467页

作者： Mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Enhanced dynamics of Al^(3+)/H^(+) ions in aqueous aluminum ion batteries:Construction of metastable structures in vanadium pentoxide upon oxygen vacancies

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Journal of Energy Chemistry 2025年第2期101卷 562-569,I0011页

作者： Zhibao Wang Hanqing Gu Tianci Wu Wenming Zhang Zhanyu Li Hebei Key Laboratory of Optic-Electronic Information and Materials National&Local Joint Engineering Laboratory of New Energy Photoelectric DevicesCollege of Physics Science and TechnologyHebei UniversityBaoding 071002HebeiChina

In recent years,aqueous aluminum ion batteries have been widely studied owing to their abundant energy storage and high theo retical *** in-depth study of vanadium oxide materials is necessary to address the precipitation of insoluble products covered cathode surface and the slow reaction ***,a method using a simple one-step hydrothermal preparation and oxalic acid to regulate oxygen vacancies has been reported.A high starting capacity(400 mAh g^(-1))can be achieved by Ov-V2O5,and it is capable of undergoing 200 cycles at 0.4 A g^(-1),with a termination discharge capacity of103 mAh g^(-1).Mechanism analysis demonstrated that metastable structures(AlxV2O5and HxV2O5)were constructed through the insertion of Al^(3+)/H^(+)during discharging,which existed in the lattice intercalation with *** incorporation of oxygen vacancies lowers the reaction energy barrier while improving the ion transport *** addition,the metastable structure allows the electrostatic interaction between Al3+and the main backbone to establish protection and optimize the transport *** parallel,this work exploits ex-situ characterization and DFT to obtain a profound insight into the instrumental effect of oxygen vacancies in the construction of metastable structures during in-situ electrochemical activation,with a view to better understanding the mechanism of the synergistic participation of Al3+and H+in the *** work not only reports a method for cathode materials to modulate oxygen vacancies,but also lays the foundation for a deeper understanding of the metastable structure of vanadium oxides.

关键词： Vanadium pentoxide Oxygen vacancies Electrochemical activation Metastable structure

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Offline Reinforcement Learning with Constrained Hybrid Action Implicit Representation Towards Wargaming Decision-Making

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Tsinghua science and technology 2024年第5期29卷 1422-1440页

作者： Liwei Dong Ni Li Guanghong Gong Xin Lin School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191ChinaState Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijing 100191Chinaand also with Zhongguancun LaboratoryBeijing 100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191Chinaand also with State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijing 100191China

Reinforcement Learning(RL)has emerged as a promising data-driven solution for wargaming ***,two domain challenges still exist:(1)dealing with discrete-continuous hybrid wargaming control and(2)accelerating RL deployment with rich offline *** RL methods fail to handle these two issues simultaneously,thereby we propose a novel offline RL method targeting hybrid action space.A new constrained action representation technique is developed to build a bidirectional mapping between the original hybrid action space and a latent space in a semantically consistent *** allows learning a continuous latent policy with offline RL with better exploration feasibility and scalability and reconstructing it back to a needed hybrid ***,a novel offline RL optimization objective with adaptively adjusted constraints is designed to balance the alleviation and generalization of out-of-distribution *** method demonstrates superior performance and generality across different tasks,particularly in typical realistic wargaming scenarios.

关键词： offline Reinforcement Learning(RL) wargaming decision-making hybrid action space

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A composite peptide-supramolecular microneedle system for melanoma immunotherapy

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Nano Research 2023年第4期16卷 5335-5345页

作者： Mengzhen Li Minxuan Wang Lingyun Li Limin Zhang Bing Ma Weizhi Wang Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologyKey Laboratory of Cluster Science of Ministry of EducationBeijing Key Laboratory of Photoelectronic/Electro-photonic Conversion MaterialsSchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China

Despite recent advances in melanoma treatment through the use of antibody immunotherapy,the clinical benefit remains restricted by its inefficient infiltration and immunosuppression within the tumor microenvironment(TME).In addition,immunerelated adverse events(irAEs)have often occurred due to the off-target binding of therapeutic drugs to normal tissues after systematic ***,we constructed an integrated and cascaded drug delivery system for the treatment of *** addition to blocking the programmed cell death protein 1 or its ligand(PD-1/PD-L1)axis,the PD-L1 targeting peptide(FE)with spherical micelle self-assembly characteristics could also effectively encapsulate the immune adjuvant resiquimod(R848),and form a complete nano ***^(R)was further integrated into tumor-responsive microneedles(MNs)to establish FE^(R)@MN and could reach the cascaded ***^(R)could be sustainedly released from the MN system and disassemble into monomers,achieving PD-1/PD-L1 axis blockade whilst reprogramming the immunosuppressive ***,FE^(R)@MN permits the controllable release and retention enhancement of the targeting peptide in the TME,thus causing prolonged PD-L1 blockade *** is demonstrated that this synergistic treatment could efficiently inhibit melanoma growth,providing a new strategy for the combination treatment of melanoma.

关键词： targeting peptide cascaded drug delivery microneedle immune checkpoint blockade self-assembly

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Enhancing H^(+)intercalation kinetics and stability in Cu^(2+)pre-intercalatedδ-MnO_(2)for aqueous aluminum batteries

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Journal of Energy Chemistry 2025年第3期102卷 126-133页

作者： Hanqing Gu Mingjun Chen Zhibao Wang Wenming Zhang Zhanyu Li Hebei Key Laboratory of Optic-Electronic Information and Materials National&Local Joint Engineering Laboratory of New Energy Photoelectric DevicesCollege of Physics Science and TechnologyHebei UniversityBaoding 071002HebeiChina

Aqueous aluminum ion batteries(AAIBs)have garnered extensive attention due to their environmental friendliness,high theoretical capacity,and low ***,the sluggish reaction kinetics and severe structural collapse of the cathode material,especially manganese oxide,during the cycling process have hindered its further ***,Cu^(2+)pre-interca la ted layeredδ-MnO_(2)was synthesized via a hydrothermal *** pre-intercalated Cu^(2+)ions not only improve the conductivity of MnO_(2)cathode but also stabilize the structure to enhance stability.X-ray absorption fine structure(XAFS)combined with density functional theory(DFT)calculations confirm the formation of the covalent bond between Cu and O,increasing the electronegativity of O atoms and enhancing the H^(+)adsorption ***,ex-situ measurements not only elucidate the Al^(3+)/H^(+)co-insertion energy storage mechanism but also demonstrate the high reversibility of the Cu-MnO_(2)cathode during *** work provides a promising modification approach for the application of manganese oxides in AAIBs.

关键词： Aqueous aluminum-ion batteries Energy storage mechanism MnO_(2)cathodes Cu^(2+)pre-intercalated

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Coastal Regions Sea Surface Salinity Retrieval of SMAP MISSION Based on Light Gradient Boosting Model

Coastal Regions Sea Surface Salinity Retrieval of SMAP MISSI...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Song, Haoming Zhang, Yifan Zhang, Biao Lv, Yanfang Zhang, Lanjie Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems China Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology China

ISBN: (纸本)9798350375909

Sea surface salinity (SSS) as an important indicator of ocean water circulation, affects global ecology and climate change. However, the accuracy of L-band satellite sea surface salinity retrieval products has been severely affected in coastal regions due to factors such as Radio Frequency Interference (RFI) and Land Sea Continuation (LSC). In this study, coastal regions observational data from the Soil Motion Active Passive (SMAP) satellite, and established the LGBM model for SSS retrieval. The results indicated that LGBM performed excellently in the retrieval of SSS in coastal regions. In comparison to in-situ SSS, the LGBM SSS demonstrated a mean bias (MB) of 0.11psu, a root mean squares error (RMSE) of 0.68psu. This paper also added the bias between LGBM SSS and in situ SSS for secondary correction of LGBM. This correction reduced the MB to 0.03psu, the RMSE to 0.54psu. This study affirms the feasibility of using LGBM for SSS retrieval in coastal regions. Future efforts will focus on integrating more auxiliary data that may influence SSS in coastal regions, aiming to further enhance the precision of the coastal regions' SSS retrieval product. © 2024 IEEE.

关键词： Radio interference

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An Attention-based LSTM price prediction model based on a single time series information 6

An Attention-based LSTM price prediction model based on a si...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Tian, Jiaming Dang, Yuzhuo Huang, Hongbin National University of Defense Technology Laboratory for Big Data and Decision Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9798331516550

With the continuous improvement of people's living standards, people's income from work has gradually increased, and their remaining savings have also gradually increased. In order to avoid the problem of currency devaluation and shrinking savings caused by inflation, investment is regarded as an indispensable asset allocation method. Among investment products, gold has always been favored for its store of value;in the past decade, the cryptocurrency Bitcoin has been limited in number due to its unique technology, causing a new wave of investment in Bitcoin. At present, most of the existing forecasting models are for forecasting stock prices, and there are multiple forecasting models. We used grayscale prediction, time series analysis method and LSTM prediction model based on Attention mechanism to predict and analyze the results of two investment products. The experiment found that the LSTM prediction model based on Attention mechanism had the best prediction performance of the two investment products. © 2024 IEEE.

关键词： Prediction models

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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Computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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