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Flare Forecast Model Based on DS-SMOTE and SVM with Optimized Regular Term

Research in Astronomy and Astrophysics 2023年第6期23卷 38-46页

作者： Jie Wan Jun-Feng Fu Ren-Qing Wen Ke Han Meng-Yao Yu Peng E School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Laboratory for Space Environment and Physical Sciences Harbin Institute of TechnologyHarbin 150001China School of Computer and Information Engineering Harbin University of CommerceHarbin 150028China

The research of flare forecast based on the machine learning algorithm is an important content of space *** order to improve the reliability of the data-driven model and weaken the impact of imbalanced data set on its forecast performance,we proposes a resampling method suitable for flare forecasting and a Particle Swarm Optimization(PSO)-based Support Vector Machine(SVM)regular term optimization *** the problem of intra-class imbalance and inter-class imbalance in flare samples,we adopt the density clustering method combined with the Synthetic Minority Over-sampling Technique(SMOTE)oversampling method,and performs the interpolation operation based on Euclidean distance on the basis of analyzing the clustering space in the minority *** the same time,for the problem that the objective function used for strong classification in SVM cannot adapt to the sample noise,In this research,on the basis of adding regularization parameters,the PSO algorithm is used to optimize the hyperparameters,which can maximize the performance of the ***,through a comprehensive comparison test,it is proved that the method designed can be well applied to the flare forecast problem,and the effectiveness of the method is proved.

关键词： Sun:flares Sun:magnetic fields Sun:X-rays gamma-rays (Sun:)sunspots

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Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

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Fluid Dynamics & Materials Processing 2023年第6期19卷 1369-1383页

作者： Xing Deng Feipeng Da Haijian Shao School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional *** work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells,namely,eosinophils,neutrophils,monocytes,and lymphocytes,known for their relationship with human body damage,inflammatory regions,and organ illnesses,in particular,and with the health of the immune system and other hazards,such as cardiovascular disease or infections,more in *** results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%,97%,and 89%,respectively,with regard to the training,verification,and testing of the corresponding datasets.

关键词： Blood cell analysis deep learning models classification-detection

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A Data Set and Reference Experiments for Multipath Wireless Emulation on Public Testbeds

A Data Set and Reference Experiments for Multipath Wireless ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Aydin, Ilknur Fund, Fraida Panwar, Shivendra S. Farmingdale State College - State University of New York Computer Systems Department United States NYU Tandon School of Engineering Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665494274

As mobile devices with dual WiFi and cellular interfaces become widespread, network protocols have been developed that utilize the availability of multiple paths. To evaluate the performance of these protocols on a testbed, researchers often use link emulation with either synthetic traces or real traces from live WiFi and cellular networks. However, in a multipath scenario, the two links are not necessarily independent. The available bandwidth on the WiFi and cellular links may be negatively correlated (e.g. when moving from indoors to outdoors or vice versa) or positively correlated (e.g. when the local density of other users increases, so that both networks become more congested at the same time), and the result of an experimental evaluation of a multipath protocol will depend heavily on this relationship. Link traces of WiFi and cellular links measured at the same time, in the same place are necessary for the reproducible evaluation of multi path protocols in a testbed environment. The contributions of this work, therefore are: first ever data set of wireless link traces measured simultaneously on WiFi and cellular interfaces of a mobile device, and reference experiments for replaying these traces on emulated links on the CloudLab and FABRIC testbeds. © 2023 IEEE.

关键词： Testbeds

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Relationship Between the Long-Term Practice of Tai Chi Stance with Neural Patterns of the Brain: An Exploratory Study

Relationship Between the Long-Term Practice of Tai Chi Stanc...

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2024 China Automation Congress, CAC 2024

作者： Li, Mengmeng Zhang, Jianbo Wan, Hong School of Electrical and Information Engineering Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350368604

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8-13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health. © 2024 IEEE.

关键词： Electroencephalography

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Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated systems for Damping Ultra-low Frequency Oscillation

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Journal of Modern Power systems and Clean Energy 2023年第6期11卷 1770-1783页

作者： Guozhou Zhang Junbo Zhao Weihao Hu Di Cao Nan Duan Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 610054China IEEE Department of Electrical and Computer Engineering University of ConnecticutStorrsCT06269USA Lawrence Livermore National Laboratory LivermoreUSA Department of Energy Technology Aalborg University9220 AalborgDenmark

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

关键词： Bi-level robust parameter optimization deep reinforcement learning deep deterministic policy gradient ultralow frequency oscillation damping control stability

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Photogrammetry for Digital Twinning Industry 4.0 (I4) systems 21

Photogrammetry for Digital Twinning Industry 4.0 (I4) System...

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2024 IEEE/ACS 21st International Conference on computer systems and Applications, AICCSA 2024

作者： Alhamadah, Ahmed Mamun, Muntasir Harms, Henry Redondo, Mathew Lin, Yu-Zheng Pacheco, Jesus Salehi, Soheil Satam, Pratik University of Arizona Department of Systems and Industrial Engineering United States University of Arizona Department of Electrical and Computer Engineering United States School of Industrial Engineering University of Sonora Mexico

ISBN: (纸本)9798331518240

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increased productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, and representing it in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97 % and the overall standard deviation error is 5.54% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time. © 2024 IEEE.

关键词： Stereo vision

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Control of a Wind Turbine Emulator Based on Variable Pitch Angle with a Self-Excited Induction Generator 4

Control of a Wind Turbine Emulator Based on Variable Pitch A...

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4th International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2024

作者： Boutahiri, Chaymaâ Nouaiti, Ayoub Mesbahi, Abdelouahed Hsaini, Abdallah Marhraoui Bouazi, Aziz Moulay Ismail University Ievia Team Est Laboratory Of Computer Science Applied Math And Electrical Engineering Meknès Morocco Systems Hassan Ii University Ensem Laboratory Of Energy & Electrical Casablanca Morocco

ISBN: (纸本)9798350309508

This article highlights the control of an electromechanical wind emulator designed to mimic the behavior of a wind turbine. The emulator employs a self-excited induction generator, which is driven by a DC motor. A DC-DC Buck converter adjusts the rotation speed, adapting to changes in the wind profile. A PI controller is employed to oversee and fine-tune the output current from the converter, ensuring alignment with the characteristics of the wind turbine model in use. The control strategy is crafted to oversee the wind turbine power based on the wind speed. This is achieved by adjusting the pitch angle value and tip speed, aiming to prevent wind turbine from stalling. The efficacy of the implemented control strategy is demonstrated in the Matlab-Simulink environment, where the proposed system is evaluated. The results show accurate tracking and no overshoot. © 2024 IEEE.

关键词： DC motors

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Performance Optimization Across the Edge-Cloud Continuum: A Multi-agent Rollout Approach for Cloud-Native Application Workload Placement

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SN computer Science 2024年第3期5卷 318页

作者： Soumplis, Polyzois Kontos, Georgios Kokkinos, Panagiotis Kretsis, Aristotelis Barrachina-Muñoz, Sergio Nikbakht, Rasoul Baranda, Jorge Payaró, Miquel Mangues-Bafalluy, Josep Varvarigos, Emmanuel School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Institute of Communication and Computer Systems Athens Greece Department of Digital Systems University of Peloponnese Sparta Greece Services as Networks (SaS) Centre Tecnològic Telecomunicacions CTTC/CERCA) Catalunya Spain

The advancements in virtualization technologies and distributed computing infrastructures have sparked the development of cloud-native applications. This is grounded in the breakdown of a monolithic application into smaller, loosely connected components, often referred to as microservices, enabling enhancements in the application’s performance, flexibility, and resilience, along with better resource utilization. When optimizing the performance of cloud-native applications, specific demands arise in terms of application latency and communication delays between microservices that are not taken into consideration by generic orchestration algorithms. In this work, we propose mechanisms for automating the allocation of computing resources to optimize the service delivery of cloud-native applications over the edge-cloud continuum. We initially introduce the problem’s Mixed Integer Linear Programming (MILP) formulation. Given the potentially overwhelming execution time for real-sized problems, we propose a greedy algorithm, which allocates resources sequentially in a best-fit manner. To further improve the performance, we introduce a multi-agent rollout mechanism that evaluates the immediate effect of decisions but also leverages the underlying greedy heuristic to simulate the decisions anticipated from other agents, encapsulating this in a Reinforcement Learning framework. This approach allows us to effectively manage the performance–execution time trade-off and enhance performance by controlling the exploration of the Rollout mechanism. This flexibility ensures that the system remains adaptive to varied scenarios, making the most of the available computational resources while still ensuring high-quality decisions. © The Author(s) 2024.

关键词： Cloud edge continuum Cloud native applications Multi-agent rollout Reinforcement learning Resource allocation

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ILRM: Imitation Learning-Based Resource Management for Integrated CPU–GPU Edge systems With Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This letter focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability constraints. We propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: 1) offline Oracle generation and 2) online IL. In the offline phase, we design a fast-converging heuristic to generate near-optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize IL using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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Multi-Objective Transmission Expansion: An Offshore Wind Power Integration Case Study

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 519-535页

作者： Khanal, Saroj Graf, Christoph Liang, Zhirui Dvorkin, Yury Unel, Burcin Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States New York University School of Law Institute for Policy Integrity New YorkNY10012 United States StanfordCA94305 United States Johns Hopkins University Departments of Electrical and Computer Engineering and Civil and Systems Engineering BaltimoreMD21218 United States

Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind and other clean-energy resources in onshore grids, highlights the urgent need for a coordinated planning framework. Our paper describes a multi-objective, multistage generation, storage and transmission expansion planning model to facilitate efficient and resilient large-scale adoption of offshore wind power. Recognizing regulatory emphasis and, in some cases, requirements to consider externalities, this model explicitly accounts for negative externalities: greenhouse gas emissions and local emission-induced air pollution. Utilizing an 8-zone ISO-NE test system and a 9-zone PJM test system, we explore grid expansion sensitivities such as impacts of optimizing Points of Interconnection (POIs) versus fixed POIs, negative externalities, and consideration of extreme operational scenarios. Our results indicate that accounting for negative externalities necessitates greater upfront investment in clean generation and storage (balanced by lower expected operational costs). Optimizing POIs could significantly reshape offshore topology or POIs, and lower total cost. Finally, accounting for extreme operational scenarios typically results in greater operational costs and sometimes may alter onshore line investment. © 2024 IEEE.

关键词： Investments

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