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A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

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Science China(Technological Sciences) 2024年第7期67卷 2074-2086页

作者： WANG Rui LI Ming-Jia WANG YiBo SUN QiuYe ZHANG PinJia College of information Science and engineering Northeastern UniversityShenyang 110819China School of Mechanical engineering Beijing Institute of TechnologyBeijing 100081China department of electrical engineering Tsinghua UniversityBeijing 100084China

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

关键词： regional integrated electric-thermal energy systems real-time power balance virtual asynchronous machine adaptive inertia small-signal stability

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MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge

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Microsystems & Nanoengineering 2024年第3期10卷 421-432页

作者： Xiaowei Guo Wuhao Yang Xingyin Xiong Zheng Wang Xudong Zou The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina QILU Aerospace information Research Institute JinanChina

Reservoir computing(RC)is a bio-inspired neural network structure which can be implemented in hardware with *** has been applied across various fields such as memristors,and electrochemical reactions,among which the microelectro-mechanical systems(MEMS)is supposed to be the closest to sensing and computing *** previous MEMS RCs have demonstrated their potential as reservoirs,the amplitude modulation mode was found to be inadequate for computing directly upon *** achieve this objective,this paper introduces a novel MEMS reservoir computing system based on stiffness modulation,where natural signals directly influence the system stiffness as *** this innovative concept,information can be processed locally without the need for advanced data collection and *** present an integrated RC system characterized by small volume and low power consumption,eliminating complicated setups in traditional MEMS RC for data discretization and *** simulation and experiment were conducted on our *** performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask ***,our MEMS RC is capable of both classification and forecasting,surpassing the capabilities of our previous non-delay-based *** method successfully processed word classification,with a 99.8%accuracy,and chaos forecasting,with a 0.0305 normalized mean square error(NMSE),demonstrating its adaptability for multi-scene data *** work is essential as it presents a novel MEMS RC with stiffness modulation,offering a simplified,efficient approach to integrate sensing and *** approach has initiated edge computing,enabling emergent applications in MEMS for local computations.

关键词： computing hardware stiffness

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Joint Transmit and Reflective Beamforming Design for Active IRS-Aided SWIPT Systems

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Chinese Journal of Electronics 2024年第2期33卷 536-548页

作者： Weiping SHI Qingqing WU Di WU Feng SHU Jiangzhou WANG School of Electronic and Optical engineering Nanjing University of Science and Technology department of Electronic engineering Shanghai Jiao Tong University School of information and communication engineering Hainan University department of Automation Shanghai Jiao Tong University School of engineering University of Kent

To further improve the performance of passive intelligent reflecting surface(IRS)-assisted communication systems and mitigate the serious path loss due to “double-fading ” of IRS-assisted links, an active IRS-aided simultaneous wireless information and power transfer(SWIPT) system is investigated. This paper jointly optimizes the transmit beamforming at the base station(BS) and the phase shifts at the active IRS in order to maximize the power collected by the energy harvesting receiver under both perfect and imperfect channel state information(CSI)states, subject to the signal-to-interference-noise ratio constraint of the information decoding receiver, and the power constraints of the BS/IRS. Under perfect CSI, the alternating optimization algorithm is utilized for obtaining the transmit beamforming at the BS and the phase shifts at the active IRS. For each subproblem, we first transform nonconvex objective function and constraints into convex ones by performing a first-order Taylor expansion. Then, each subproblem is solved by using the interior point method. Given that obtaining perfect CSI is impractical, two robust beamforming designs are proposed for imperfect CSI case. Under the bounded CSI error model, we first transform the non-convex optimization problem into two semidefinite programming subproblems, and then solve each subproblem based on S-procedure and sequential rank-one constraint relaxation(SROCR) techniques. Under the stochastic CSI error model, the alternating optimization method is applied in an iterative manner based on Bernstein-type inequality and SROCR technique. Simulation results show that both robust and non-robust schemes for active IRS-assisted SWIPT systems can achieve extremely superior performance over conventional passive IRS-assisted systems under the same overall power budget.

关键词： Wireless communication Semidefinite programming Array signal processing Simulation Stochastic processes Transforms Receivers

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Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

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Computer Modeling in engineering & Sciences 2024年第6期139卷 2673-2703页

作者： Yan Wang You Lu Yuqing Zhou Zhijian Zhao department of Computer and communication engineering Northeastern UniversityQinhuangdao066004China

Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application *** paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization *** in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional *** method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of ***,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement ***,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position *** results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

关键词： Maximum correntropy criterion unscented Kalman filter inertial navigation system ultra-wideband bisecting kmeans clustering algorithm

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun department of electrical engineering Faculty of Engineering University of Malaya Faculty of Data Science and information Technology INTI International University Faculty of electrical and Electronic engineering Technology Universiti Teknikal Malaysia Melaka Faculty of Electronic and Computer engineering Universiti Teknikal Malaysia Melaka

Graphene oxide(GO) is a 2D coating material used to improve fiber optics sensors' response to relative humidity. Microbottle resonators(MBRs) have garnered more attention as sensing media structures. An MBR with a 190 μm diameter was coated with GO. Then, tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25 °C. The MBR showed a higher Q factor before and after GO coating. The sensitivity of 0.115 d B/%RH was recorded with the 190 μm GO-coated MBR sample compared to a sensitivity of 0.022 d B/%RH for the uncoated MBR sample. These results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： A

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia department of Mechanical engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia department of electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan electrical engineering department College of EngineeringNajran UniversityNajran61441Saudi Arabia department of electrical engineering The University of LahoreLahore54000Pakistan Faculty of Computer and information Systems Islamic University of MadinahMadinah42351Saudi Arabia department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 487-501页

作者： Nianyin Zeng Xinyu Li Peishu Wu Han Li Xin Luo department of Instrumental and electrical engineering Xiamen UniversityXiamen 361005China IEEE College of Computer and information Science Southwest UniversityChongqing 400715China

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

关键词： Attention mechanism knowledge distillation(KD) object detection tensor decomposition(TD) unmanned aerial vehicles(UAVs)

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Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2061-2102页

作者： Hong Zhang Haijian Shao School of electrical information engineering Jiangsu University of TechnologyChangzhou213001China department of electrical and Computer engineering University of NevadaLas Vegas89154USA

OpenAI and ChatGPT, as state-of-the-art languagemodels driven by cutting-edge artificial intelligence technology,have gained widespread adoption across diverse industries. In the realm of computer vision, these models havebeen employed for intricate tasks including object recognition, image generation, and image processing, leveragingtheir advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have foundutility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactiveplayer experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providinginvaluable insights and support to healthcare professionals in the realmof disease detection. The principal objectiveof this paper is to offer a comprehensive overview of OpenAI, OpenAI Gym, ChatGPT, DALL E, stable diffusion,the pre-trained clip model, and other pertinent models in various domains, encompassing CLIP Text-to-Image,education, medical imaging, computer vision, social influence, natural language processing, software development,coding assistance, and Chatbot, among others. Particular emphasis will be placed on comparative analysis andexamination of popular text-to-image and text-to-video models under diverse stimuli, shedding light on thecurrent research landscape, emerging trends, and existing challenges within the domains of OpenAI and *** a rigorous literature review, this paper aims to deliver a professional and insightful overview of theadvancements, potentials, and limitations of these pioneering language models.

关键词： OpenAI ChatGPT DALL E stable diffusion OpenAI Gym text-to-image text-to-video

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A resonant high-pressure microsensor based on a composite pressure-sensitive mechanism of diaphragm bending and volume compression

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Microsystems & Nanoengineering 2024年第2期10卷 57-66页

作者： Pan Qian Zongze Yu Jie Yu Yulan Lu Bo Xie Jian Chen Deyong Chen Junbo Wang The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing100190China The School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing100049China

In this paper,a composite pressure-sensitive mechanism combining diaphragm bending and volume compression was developed for resonant pressure microsensors to achieve high-pressure measurements with excellent *** composite mechanism was explained,and the sensor structure was designed based on theoretical analysis and finite element *** all-silicon resonant high-pressure microsensor with multiple miniaturized cavities and dual resonators was developed,where dual resonators positioned in two resonant cavities with suitably different widths are used to perform opposite characteristics in pressure and the same characteristics at different temperatures,which can improve pressure sensitivities and realize temperature self-compensation by differential frequency *** microsensor was fabricated by microfabrication,and the experimental results showed that the sensor had an accuracy of±0.015%full scale(FS)in a pressure range of 0.1~100 MPa and a temperature range of−10~50℃.The pressure sensitivity of the differential frequency was 261.10 Hz/MPa(~2523 ppm/MPa)at a temperature of 20℃,and the temperature sensitivities of the dual resonators were−1.54 Hz/℃(~−14.5 ppm/℃)and−1.57 Hz/℃(~−15.6 ppm/℃)at a pressure of 2 *** differential output had an outstanding stability within±0.02 Hz under constant temperature and ***,this research provides a convenient solution for high-pressure measurements because of its advantages,namely,large range,excellent accuracy and stability.

关键词： stability resonant bending

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Low rank optimization for efficient deep learning:making a balance between compact architecture and fast training

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Journal of Systems engineering and Electronics 2024年第3期35卷 509-531,F0002页

作者： OU Xinwei CHEN Zhangxin ZHU Ce LIU Yipeng School of information and communication engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Deep neural networks(DNNs)have achieved great success in many data processing ***,high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices,and it is not environmental-friendly with much power *** this paper,we focus on low-rank optimization for efficient deep learning *** the space domain,DNNs are compressed by low rank approximation of the network parameters,which directly reduces the storage requirement with a smaller number of network *** the time domain,the network parameters can be trained in a few subspaces,which enables efficient training for fast *** model compression in the spatial domain is summarized into three categories as pre-train,pre-set,and compression-aware methods,*** a series of integrable techniques discussed,such as sparse pruning,quantization,and entropy coding,we can ensemble them in an integration framework with lower computational complexity and *** addition to summary of recent technical advances,we have two findings for motivating future *** is that the effective rank,derived from the Shannon entropy of the normalized singular values,outperforms other conventional sparse measures such as the?_1 norm for network *** other is a spatial and temporal balance for tensorized neural *** accelerating the training of tensorized neural networks,it is crucial to leverage redundancy for both model compression and subspace training.

关键词： model compression subspace training effective rank low rank tensor optimization efficient deep learning

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