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Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance

Autonomous Intelligent Systems 2023年第1期3卷 84-95页

作者： Panpan Zhou Shupeng Lai Jinqiang Cui Ben M.Chen Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinN.T.Hong Kong. Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore. Peng Cheng Laboratory Shenzhen518055China.

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered *** proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision *** specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free ***,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion ***,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

关键词： Multi-agent system Unmanned systems Rotorcraft systems Formation control Consensus control

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Distributed Privacy-Preserving State-of-Charge Balance control for Multiple Energy Storage Systems in Smart Grid

Distributed Privacy-Preserving State-of-Charge Balance Contr...

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2023 China automation Congress, CAC 2023

作者： Liu, Weijia Guo, Fanghong Xing, Lantao Su, Hongye Wang, Lei College of Control Science and Engineering Zhejiang University Hangzhou China Zhejiang University of Technology Department of Automation Hangzhou China School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350303759

This paper studies the distributed state of charge (SoC) balance control strategy in battery energy storage systems (BESSs), satisfying privacy-preserving requirement of BESSs privacy-sensitive data, i.e., configurations and SoC, which may be transmitted among BESSs. Considering each BESS communicates with its neighboring BESS, we use a distributed dynamic average consensus algorithm to do parameter estimations. Using obtained parameters, we set proper power output of each BESS and maintains overall maximum capability. Different from common algorithms, this algorithm not only converges to the exact average of reference signals and thus help set control power outputs, but also ensures the desired differential privacy by adding zero mean Laplace noises, thus protecting privacy-sensitive data from possible adversaries. Effectiveness of the presented approach is demonstrated by simulations. © 2023 IEEE.

关键词： Charging (batteries)

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A Novel Deep Learning Model for Classifying Power Quality Problems in PV-integrated Microgrids using CNN-LSTM 1

A Novel Deep Learning Model for Classifying Power Quality Pr...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Channa, Irfan Ali Li, Dazi Dahri, Fida Hussain Abro, Ghulam E. Mustafa Zahid, Faizan University of Chemical Technology Institute of Automation Beijing Beijing100069 China Southeast University School of Computer Science and Engineering Nanjing China Saudi Arabia University of Catania Department of Electrical Electronic and Computer Engineering Italy

ISBN: (纸本)9798350348637

Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed energy sources in microgrids (MG) deteriorate the operation of distribution and utility of power supply. In this work, a new CNN-LSTM based deep learning approach is proposed to identify power quality disturbances (PQDs) in PV-integrated microgrid. In a proposed framework, full closed-loop neural architecture containing different layers to facilitate automatic feature extraction and long-time short memory (LSTM) network provide temporal feature for intelligent classifier to improve the training speed. Moreover, comparison with other deep neural networks proves that proposed approach can simplify the procedure of PQ problems with the conventional signal analysis and feature selection methods. A typical PV-integrated microgid is simulated on PSCAD software to prove the robustness of classifier for classification of PQDs in renewable energy integrated distribution networks. © 2024 IEEE.

关键词： Microgrids

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CO2 Emission Mitigation in Container-Based Cloud Computing by the Power of Resource Management

CO2 Emission Mitigation in Container-Based Cloud Computing b...

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作者： Ashry, Nesma Attia, Radwa Nashaat, Heba Rizk, Rawya Computer and Control Department Faculty of Engineering Port-Said University Port-Said Egypt

The increasing amount of data processing through cloud systems leads to high energy costs and CO2 emissions challenges. Advanced monitoring tools are used to track resource utilization and identify possible optimization areas to mitigate CO2 emissions and reduce energy consumption. Thus, resource and power management in cloud environments is essential to ensure efficient performance and effective cost. Recently, container-based clouds become more widely used than hypervisor-based clouds. Scheduling the huge number of user-submitted tasks and optimizing both network and cloud resources are important aspects of cloud computing to decrease network overhead, reduce both Data Center (DC) energy consumption and Service-Level Agreement (SLA) violations and in turn, mitigate CO2 emissions. However, the state-of-the-art resource schedulers that consider power management and network transmissions are unfortunately not container-aware schedulers. The main objective of this paper is to introduce a solid analysis of the most significant techniques and tools in resource management to minimize power consumption and CO2 emissions with the advantage of being a container-aware scheduler;Applying the first stage of the proposed solution leads to mitigating the CO2 by an average of 2.12% against the Non-Clustered approach and 18.04% against the Clustered approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Containers

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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computer Systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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Enhancing Septic Shock Detection through Interpretable Machine Learning

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computer Modeling in engineering & Sciences 2024年第12期141卷 2501-2525页

作者： Md Mahfuzur Rahman Md Solaiman Chowdhury Mohammad Shorfuzzaman Lutful Karim Md Shafiullah Farag Azzedin Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Science Taif UniversityTaif21974Saudi Arabia College of Applied Arts and Technology Seneca PolytechnicTorontoM2J 2X5Canada Department of Control&Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health *** traditional methods,which often lack transparency in decision-making,our approach focuses on early detection,offering a proactive strategy to mitigate the risks of *** integrating advanced machine learning algorithms with interpretability techniques,our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s ***,we introduce a preference-based matching algorithm to evaluate disease severity,enabling timely interventions guided by the analysis *** innovative integration significantly enhances the effectiveness of our *** leverage a clinical health dataset comprising 1,552,210 Electronic Health Records(EHR)to train our interpretable machine learning models within a cloud computing *** techniques like feature importance analysis and model-agnostic interpretability tools,we aim to clarify the crucial indicators contributing to septic shock *** transparency not only assists healthcare professionals in comprehending the model’s predictions but also facilitates the integration of our system into existing clinical *** validate the effectiveness of our interpretable models using the same dataset,achieving an impressive accuracy rate exceeding 98%through the application of oversampling *** findings of this study hold significant implications for the advancement of more effective and transparent diagnostic tools in the critical domain of sepsis management.

关键词： Sepsis prediction machine learning cloud computing

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Overview of Load Disaggregation Models, Methods and Hardware 59

Overview of Load Disaggregation Models, Methods and Hardware

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59th International Universities Power engineering Conference, UPEC 2024

作者： Lancranjan, Mircea N. Muresan, Alexandru Jurj, Dacian I. Berciu, Alexandru Polycarpou, Alexis Czumbil, Levente Micu, Dan D. Technical University of Cluj-Napoca Department Electrical Engineering Cluj-Napoca Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca Romania Department of Electrical and Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (纸本)9798350379730

In the dynamic landscape of energy efficiency, a domain garnering ever increasing attention and urgency, the cornerstone of informed decision-making resides in the acquisition of reliable data. This paper summarizes the main approaches, methods and hardware used to gather, process and disaggregate such data on an appliance-by-appliance basis. The paper explores a critical analysis of the various models and techniques that support the gathering of energy data at the appliance level. A range of approaches, including hardware-based solutions such as smart meters, submeters, and sensor arrays, placing a special focus on intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) algorithms that employ advanced signal processing and machine learning techniques, are examined for their effectiveness, scalability, and suitability for both industrial and commercial contexts. Furthermore, the challenges of data acquisition capabilities, reconciling scalability, cost-effectiveness, and user-friendliness - prerequisites for widespread acceptance and uptake are also investigated. © 2024 IEEE.

关键词： AI Data Cleaning Energy Efficiency Energy Signature Load Disaggregation Load Monitoring NILM

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Comparative Analysis of Production Functions of Complex Variables of the Predictive Model of Resource Support for the Process of Heat Generation

Comparative Analysis of Production Functions of Complex Vari...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Bazil, Gulmira Mukhanov, Bakhyt Iskakova, Gulmira Orakbayev, Yerbol Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev Department of Automation and Control Almaty Kazakhstan

ISBN: (纸本)9798350335040

The article presents methods for calculating the production function based on complex-valued economics. The uniqueness of the work lies in the fact that the production function is presented as a criterion for managing and forecasting heat supply. Data from the boiler plants of the heat supply company in Taldykurgan are used as initial data. As a result of the analysis, it was decided to use the production function of the complex argument, which will increase the number of process indicators and conduct a more complete analysis of the production of thermal energy. © 2023 IEEE.

关键词： Heat generation

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