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A General and Automatic RMS Current Oriented Optimal Design Tool for LLC Resonant Converters

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第6期10卷 7318-7332页

作者： Wei, Yuqi Pereira, Thiago Pan, Yiwei Liserre, Marco Blaabjerg, Frede Mantooth, H. Alan Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA Univ Kiel Chair Power Elect D-24118 Kiel Germany Aalborg Univ AAU Energy Dept DK-9220 Aalborg Denmark

LLC resonant converters are used in renewable energy applications to achieve high power efficiency conversions between different energy sources, buses, and energy storage elements. The traditional design methods for LLC resonant converters are based on a simple frequency-domain analysis (FDA). However, the accuracy of FDA is not satisfactory, especially at wide voltage range applications. To overcome the drawbacks of the traditional FDA method in wide-range applications, in this article, the time-domain analysis (TDA) is adopted to achieve accurate analysis of LLC converters. Efficiency is normally selected as the optimization objective, where the circuit components and accurate power loss models are required beforehand. To achieve a more general optimal design method for LLC converters, a simple root-mean-square (rms) current and TDA-based optimal design method is proposed. The optimal design is achieved by minimizing the converter rms current and ensuring some key design considerations. Moreover, an automatic design tool for the proposed rms-current-based optimal design method is developed, and advanced optimization algorithms are introduced to improve the optimization speed. A 2.5-kW experimental prototype is built using the optimal circuit parameters. Experimental results under different operating conditions are demonstrated, and the results are consistent with the theoretical analysis. Efficiency comparisons between the proposed optimal design method and conventional design method are made. The proposed optimal design method can improve the converter efficiency by a maximum value of 2.14%.

关键词： Automatic design optimization algorithm resonant converter root-mean-square (rms) current

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Guideline on Initial Point Finding and Search Bounds Setting for Biomedical Magnetic Localization

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IEEE SENSORS JOURNAL 2022年第16期22卷 16492-16504页

作者： Fu, Yuming Guo, Yongxin Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Natl Univ Singapore Chongqing Res Inst Ctr Intelligent Sensing & Artificial Intelligence Chongqing 401123 Peoples R China

Permanent magnet tracking technology provides a feasible way to localize noninvasive in vivo biomedical devices such as wireless capsule endoscopes. However, current permanent magnet tracking technology that typically relies on an optimization algorithm suffers from a trade-off between the accuracy, consistency, and speed of the results. In particular, in practical applications such as the magnetically actuated capsule, poor localization results are undesirable. In other words, high consistency and accuracy must be achieved concurrently. In this study, we propose an initial point finding method for optimization algorithms based on a semi-soft classifier that adopts a fully connected network and a convolution neural network as base classifiers. Furthermore, we present search bound setting guidelines for the optimization algorithms based on the estimated initial point. By setting tight bounds, the consistency and speed of the process can be improved while maintaining high accuracy. The proposed method is validated via simulations and experiments, and outperforms the previous methods under the same system configuration. Within an effective working space of 42 cm x 30 cm x 24 cm, it achieves an update rate of approximately 9-Hz, higher localization consistency, zero occurrence rate of poor localization results, an average error of 1.3 mm for position estimation, and an average error of 2.7 degrees for orientation estimation.

关键词： Convolutional neural network fully connected neural network magnetic localization optimization algorithm permanent magnet tracking system wireless capsule endoscope

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Multi-Objective optimization-Based Recommendation for Massive Online Learning Resources

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IEEE SENSORS JOURNAL 2021年第22期21卷 25274-25281页

作者： Li, Hui Zhong, Zhaoman Shi, Jun Li, Haining Zhang, Yong Jiangsu Ocean Univ Sch Comp Engn Lianyungang 222000 Peoples R China Jiangsu Prov Res & Dev Inst Marine Resources Lianyungang 222000 Peoples R China Ningxia Med Univ Dept Neurol Gen Hosp Yinchuan 750004 Ningxia Peoples R China

As an important research topic in intelligent teaching systems, personalized recommendation services of learning resources can effectively solve the "information overload" problem and provide effective learning. However, the traditional learning resource recommendation technology mainly aims to improve recommendation accuracy and cannot effectively ensure the diversity and novelty of recommendation results. In this paper, the learning resource recommendation task is modelled with a multi-objective optimization problem. This paper proposes the Multi-Objective Evolutionary algorithm-based online learning Resource Recommendation Model to balance the system's accuracy, novelty, and diversity. The proposed model includes the following four steps: learning clustering, optimization goal setting individual representation, and genetic operator. According to the experimental results, this algorithm can improve the recommendation performance of online learning resources. Compared with the existing recommendation algorithms, more accurate, diverse, and novel learning resource recommendation results can be obtained with the proposed algorithm.

关键词： Sensors optimization Prediction algorithms Evolutionary computation Clustering algorithms Indexes Heating systems Multi-objective optimization optimization algorithm online learning resources learning resource recommendation

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Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm optimization in Wireless Sensor Networks: Forest Fire Detection

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WIRELESS PERSONAL COMMUNICATIONS 2021年第4期118卷 3501-3522页

作者： Vinodhini, R. Gomathy, C. SRM IST Elect & Commun Engn Vadapalani Campus Chennai Tamil Nadu India

Wireless sensor networks (WSNs) displays an encouraging outcome for forest fire (FF) identification. The most serious WSN investigation tasks is forest fire's early prediction, which is used to save the ecosystem. For conveying the detected information to the BS (base station), the SNs (sensor nodes) are placed in remote forest region in WSN based FF discovery scheme that is manageable by the forest sector. Various studies have been finished in this field but they studied only few amount of constraints and the encountered situations influence has not discussed after the system positioning. In this work, fuzzy based unequal clustering and context aware routing (CAR) procedure with GSO (glow-worm swarm optimization) is developed in RWP (random way point) based dynamic WSNs. Based on FL (fuzzy logic) the unequal clustering is formed and the optimal CH (cluster head) is nominated to convey the information from CM (cluster member) to BS to increase the system lifespan and to decrease the energy consumption. Further, the routing process is performed by the CAR procedure with GSO process to enhance the efficiency of network. Lastly, a case study of FF identification is offered as a justification of the suggested method. The suggested work is executed in MATLAB. The simulation outcomes proved that the proposed approach provide the better outcomes in average energy consumption (0.025 J), PDR (99.4%), jitter (4.01 s), delay (0.0304 s), BER (15%), throughput (144.6Kbps), network lifetime (38.7 s) as related to other current protocols.

关键词： Sensor networks optimization algorithm Routing protocol Context aware Fuzzy logic Fire detection in forest

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A Novel Optimal Layout Method of Airborne Distributed POS Based on MAC and GMC Hybrid optimization Criterion

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IEEE SENSORS JOURNAL 2021年第12期21卷 13638-13648页

作者： Gong, Xiaolin Sun, Lujin Peng, Zhiwei Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China

The layout of the airborne distributed position and orientation system (POS) directly affects the overall accuracy of the motion parameters of all load points obtained by the distributed POS. However, there are restrictions of space, weight and cost of the distributed POS, it is unrealistic to install an inertial measurement unit (IMU) at the location of each load. To solve this problem, based on modal analysis of wing structure, considering the two factors of the modal spatial intersection angle and the vibration amplitude, a novel fitness function model based on modal assurance criterion (MAC) and Gramian matrix criterion (GMC) is proposed to evaluate the population in the optimization algorithm. The hybrid adaptive particle swarm optimization and genetic algorithm (HAPSOGA) based on this proposed model is applied to the multiple system layout of airborne distributed POS to obtain the optimal layout scheme. The results of semi-physical simulation and ground verification experiment show that compared with the optimal method based on MAC, the optimal layout scheme determined by the proposed method can use less IMU and obtain higher overall accuracy of motion parameters for the remote sensing loads.

关键词： Layout optimization Strain Atmospheric modeling Vibrations Sensors Remote sensing Distributed position and orientation system SINS GPS integration layout optimization optimization algorithm optimization criterion

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Manufacturing Imperfections of Open Cross-Section Deployable Thin-Shell Composite Structures

Manufacturing Imperfections of Open Cross-Section Deployable...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Carmi, Meital O. Pellegrino, Sergio Graduate Aerospace Laboratories California Institute of Technology 1200 E California Blvd MC 105-50 PasadenaCA91125 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Characterizing the geometric imperfections of ultra-thin composite structures is important since imperfections create weak points where local buckling is likely to occur. This work develops a thorough method for measuring the geometric imperfections in thin-shell composite longerons. The mean geometrical parameters, the axial twist, and the lengths, amplitudes and locations of both local and periodic radial imperfections in experimental composite longerons are measured using this method. The results of this study identify the dominant imperfections commonly introduced during the manufacturing of these structures. This research was carried out with financial support from the National Science Foundation’s Graduate Research Fellowship Program and the Caltech Space Solar Power Project. The authors would like to acknowledge Professor Dan Meiron for his guidance on the wavelength decomposition used in this work. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Composite Structures Longerons Shape Functions Numerical Modeling Computing Autoclave Thin Shell Structure MATLAB optimization algorithm Structural Failure

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Reservoir automatic history matching: Methods, challenges, and future directions

ADVANCES IN GEO-ENERGY RESEARCH

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ADVANCES IN GEO-ENERGY RESEARCH 2023年第2期7卷 136-140页

作者： Liu, Piyang Zhang, Kai Yao, Jun Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China

Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.

关键词： History matching optimization algorithm surrogate model data-driven

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Wireless Multiplexing Control Based on Magnetic Coupling Resonance and Its Applications in Robot

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JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME 2022年第1期14卷 011009页

作者： Wang, Jue Chen, Genliang Zhang, Zhuang Suo, Jiaqi Wang, Hao Shanghai Jiao Tong Univ Shanghai Key Lab Digital Manufacture Thin Walled Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Gensler Baltimore MD 21202 USA

Nowadays, more and more researchers are pursuing miniaturized and lightweight structure of robots. However, robots with multiple actuators require large control systems if each actuator needs to be controlled independently. In addition, the cables and circuits for control and power supply are the obstacles in reducing size and weight. In this article, a wireless multiplexing control system based on magnetic coupling resonance (MCR) is proposed. The control system can realize wireless energy transmission and control simultaneously. By decomposing a composite signal, it can control multiple actuators with only one input signal. However, in previous researches, their applications are primary and simple due to the switch control without feedback and the lack of systematic design method for robot application. Thus, based on the discrete form of composite signal, the closed-loop of wireless multiplexing control is presented, which makes this promising method a step closer to the practical application. Besides, based on the theoretical model of load power and transmission efficiency, five parameters to be optimized are extracted in accordance with the actual design requirements. The optimization algorithm for load power is proposed using particle swarm optimization (PSO). As for its applications in robots, a Delta robot with flexible linkage and an untethered multidrive pipe robot for sampling operation are designed to demonstrate the proposed control method. The experiment results of the Delta robot show the reliability and accuracy of the system, while the results of the pipe robot prove its potential use in the untethered robot system.

关键词： magnetic coupling resonance multiplexing control optimization algorithm parallel robot pipe robot

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A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年第PartA期151卷 106214-106214页

作者： Jena, Bibekananda Naik, Manoj Kumar Panda, Rutuparna Abraham, Ajith Anil Neerukonda Inst Technol & Sci Dept Elect & Commun Engn Visakhapatnam 531162 Andhra Pradesh India Siksha O Anusandhan Fac Engn & Technol Bhubaneswar 751030 Orissa India Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Burla 768018 Orissa India Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Auburn WA 98071 USA

Background: One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image ***: To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal ***: The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based *** results: The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropybased thresholding methods.

关键词： Biomedical engineering Machine learning Multilevel thresholding Generalized cross -entropy optimization algorithm

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Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2022年第3期19卷 031004-031004页

作者： He, Ning Qian, Cheng He, Lile Xian Univ Architecture & Technol Sch Mech & Elect Engn Xian 710055 Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China

As an important energy storage device, lithium-ion batteries have vast applications in daily production and life. Therefore, the remaining useful life (RUL) prediction of such batteries is of great significance, which can maintain the efficacy and reliability of the system powered by lithium-ion batteries. For predicting remaining useful life of lithium-ion batteries accurately, an adaptive hybrid battery model and an improved particle filter (PF) are developed. First, the adaptive hybrid model is constructed, which is a combination of empirical model and long short-term memory (LSTM) neural network model such that it could characterize battery capacity degradation trend more effectively. In addition, the adaptive adjustment of the parameters for hybrid model is realized via optimization technique. Then, the beetle antennae search (BAS) based particle filter is applied to update the battery states offline constructed by the proposed adaptive hybrid model, which can improve the estimation accuracy. Finally, remaining useful life short-term prediction is realized online based on long short-term memory neural network rolling prediction combined historical capacity with online measurements and latest offline states and model parameters. The battery data set published by NASA is used to verify the effectiveness of proposed strategy. The experimental results indicate that the proposed adaptive hybrid model can well represent the battery degradation characteristics and have a higher accuracy compared with other models. The short-term remaining useful life prediction results have good performance with the errors of 1 cycle, 3 cycles, and 1 cycle, above results indicate proposed scheme has a good performance on short-term remaining useful life prediction.

关键词： lithium-ion battery adaptive hybrid model particle filter optimization algorithm long short-term memory neural network remaining useful life

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