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On-chip micro-ring resonator array spectrum detection system based on convex optimization algorithm

NANOPHOTONICS 2023年第4期12卷 715-724页

作者： Chen, Xinyang Gan, Xuetao Zhu, Yong Zhang, Jie Northwestern Polytech Univ Sch Sci Key Lab Light Field Manipulat & Informat Acquisit Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Sci Shaanxi Key Lab Opt Informat Technol Xian 710072 Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Educ Minist China Chongqing 400044 Peoples R China

We propose an all on-chip micro-ring resonator array spectrum detection system (MRRAS). Micro-ring resonator array as the core is used to construct the transmission matrix of the system. The theoretical analysis of the spectrum detection system is completed with waveguide transmission theory and spectrum construction method based on convex optimization algorithm. In the experiment, we obtain the priori information of the transmission matrix of the system, then detect the output intensity of unknown spectrum through MRRAS, and construct the under-determined matrix equations when the number of micro-rings is much smaller than that of reconstructed wavelengths. convex optimization algorithm is employed to obtain the least norm solution of the under-determined matrix equations, which enables fast spectrum reconstruction. The experimental results show that the spectrum detection system is constructed using three micro-ring resonators with 4 mu m radius, enabling the compact footprint. In addition, the silicon nitride based photonic platform is fully compatible with standard complementary metal oxide semiconductor (CMOS) processes. The system operating bandwidth is more than 12 nm and the resolution is better than 0.17 nm.

关键词： convex optimization algorithm MRRAS spectrum reconstruction waveguide transmission theory

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optimization Strategy of the Electric Vehicle Power Battery Based on the convex optimization algorithm

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PROCESSES 2023年第5期11卷 1416页

作者： Wang, Xuanxuan Ji, Wujun Gao, Yun Henan Polytech Zhengzhou 450046 Peoples R China

With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The performance of the proposed convex optimization algorithm, dual loop DP optimization algorithm, and nonlinear optimization algorithm is compared. The results show that the hydrogen consumption of electric vehicles optimized by the convex optimization algorithm is 95.364 g. This consumption is lower than 98.165 g of the DCDP optimization algorithm and 105.236 g of the nonlinear optimization algorithm before optimization. It is also significantly better than the 125.59 g of electric vehicles before optimization. The calculation time of the convex optimization algorithm optimization is 4.9 s, which is lower than the DCDP optimization algorithm and nonlinear optimization algorithm. The above results indicate that convex optimization algorithms have better optimization performance. After optimizing the power battery using a convex optimization algorithm, the overall performance of electric vehicles is higher. Therefore, this method can effectively improve the performance of current electric vehicle power batteries, make new energy vehicles develop rapidly, and improve the increasingly serious environmental pollution and energy crisis in China.

关键词： convex optimization algorithm electric vehicle power battery energy management strategy motor model

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Bi-level convex optimization speed planning algorithm for pure electric buses under multi-factor urban road conditions

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年

作者： Li, Chenyang Lin, Qiang Chen, Longxiang Zhu, Qingyuan Shao, Guifang Xiamen Univ Pen Tung Sah Inst Micronano Sci & Technol Xiamen 361005 Peoples R China

Energy saving is crucial in eco-driving research of pure electric buses, and its effectiveness is highly dependent on the speed planning. The current speed planning methods use dynamic programming to find the optimal solution, but they become less suitable when multi-factor conditions introduce complex constraints, leading to more computation complexity and time. To reduce the computational complexity, we proposed a bi-level convex optimization speed planning algorithm (Bi-COA) for pure electric buses under multi-factor urban road conditions. This method includes the constraint layer and solution layer. In the constraint layer, we constructed a nonlinear constraint model that considers multiple factors such as load variation, road gradient, and traffic signals, and transformed them into linear constraints through convexification. We then employ the average speed to transform the energy-optimal cost function into a quadratic function, which can reduce the computational complexity. In the solution layer, the Multi-Objective Evolutionary Strategy Kernel (MOESK) is utilized to obtain the optimal driving speed of the pure electric bus under multiple constraints. The results of the experiments indicate that the proposed method can save 71.99% of energy consumption compared with human drivers. Compared with the dynamic programming method, its average solution efficiency is improved by 40%, which greatly reduces the calculation time.

关键词： Energy saving pure electric buses multi-factor conditions speed planning convex optimization algorithm

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Hybrid distributed online and alternating convex optimization-based initial value accelerated hybrid precoding for millimeter wave multiple-input-multiple-output

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第3期37卷 e5660-e5660页

作者： Kadiyala, Ragodaya Deepthi Kakarla, Deepti Chikyal, Neetu Vasavi Coll Engn A Dept Elect & Commun Engn Hyderabad Telangana India Vasavi Coll Engn A Dept Elect & Commun Engn Hyderabad 500089 Telangana India

The hybrid precoding problem is considered a Frobenius norm reduction problem for the narrowband channel in a millimeter wave (mmWave) with multiple inputs and outputs (mmWave MIMO). This work proposes a hybrid distributed online and alternating convex optimization (HDO-ACO) algorithm to improve hybrid precoding (HP), interpreted as a trace minimization problem. HDO-ACO alternately determines the digital and analog precoders to reduce the trace by keeping the other constant. Initially, HDO-ACO uses Lagrange's method to determine the digital precoding subproblem. Then, it uses the integrated distributed online convex optimization and alternating minimization algorithm in the analog precoder design. In the HP method, the digital and analog precoders are iteratively updated until the highest number of iterations or convergence is reached. But this hybrid precoding method requires an initial analog precoding matrix input to begin the iteration. The algorithms converge gradually and fall into a suboptimal solution when the initial analog precoding matrix is set randomly. Hence, an initial value acceleration-based heuristic approach is used in the HDO-ACO alternating minimization algorithm that calculates the initial feasible value of an alternating minimization method using channel conditions. The simulation results of the proposed HDO-ACO algorithm are presented under bit error rate (BER), spectral efficiency (SE), and convergence behavior by comparing it with modified block coordinate descent-HP (MBCD-HP), manifold optimization-alternating minimization (MO-AltMin), and semi-definite relaxation-based alternating optimization (SDR-AO). The proposed HDO-ACO attains maximum SE and less BER than other hyper-precoding designs. A new hybrid distributed online and alternating convex optimization (HDO-ACO) algorithm is proposed to solve the problem of hybrid precoding (HP) by interpreting it as a trace minimization problem. The digital and analog decoding problems are solved us

关键词： convex optimization algorithm Hermitian matrix initial value acceleration Lagrangian method semi-definite programming

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Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System

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SENSORS 2023年第2期23卷 723-723页

作者： Miao, Jiansong Chen, Haoqiang Li, Hairui Bai, Shanling Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.

关键词： unmanned aerial vehicle communication mobile edge computing secrecy energy efficiency convex optimization algorithm

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Secrecy Capacity Enhancement in Active IRS-Assisted UAV Communication System

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SENSORS 2023年第9期23卷 4377-4377页

作者： Miao, Jiansong Li, Tongjie Bai, Shanling Yan, Shi Zhao, Yan Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China

As a new technology for reconstructing communication environments, intelligent reflecting surfaces (IRSs) can be applied to UAV communication systems. However, some challenges exist in IRS-assisted UAV communication system design, such as physical layer security issues, IRS design, and power consumption issues owing to the limitation of the hardware. Therefore, a secrecy capacity optimization scheme for an active IRS-assisted unmanned aerial vehicle (UAV) communication system is proposed to solve multi-user security issues. In particular, controllable power amplifiers are integrated into reflecting units to solve the problem of blocked links, and the UAV can dynamically select the served user according to the channel quality. In order to maximize the system average achievable secrecy capacity and ensure the power constraints of the UAV and active IRS, user scheduling, UAV trajectory, beamforming vector, and reflection matrix are jointly optimized, and the block coordinate descent (BCD) algorithm is applied to solve this non-convex problem. Simulation results show that the active IRS-assisted UAV communication scheme can significantly weaken the "multiplicative fading" effect and enhance the system secrecy capacity by 55.4% and 11.9% compared with the schemes with passive IRS and without optimal trajectory, respectively.

关键词： intelligent reflecting surface unmanned aerial vehicle communication secrecy capacity maximization convex optimization algorithm

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New H∞ observer-based control for delayed LPV stochastic system

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IET CONTROL THEORY AND APPLICATIONS 2022年第3期16卷 353-365页

作者： Chang, Wen-Jer Ku, Cheung-Chieh Chen, Guan-Wen Natl Taiwan Ocean Univ Dept Marine Engn Keelung Taiwan Ship & Ocean Ind R&D Ctr New Taipei Taiwan

An H-infinity observer-based control problem of delayed Linear Parameter Varying (LPV) stochastic system is addressed in this paper. In the LPV stochastic system, stochastic behaviors and external disturbance in real environment are respectively described by stochastic difference function. Furthermore, the delay effects on state and control input are considered as time-varying case for wide application. For the control problem, a lemma dealing with the delay terms is proposed to increase some slack variables. Based on the proposed lemma and the chosen Lyapunov-Krasovskii function, some sufficient conditions are derived to relax the conservatism of stability criterion. Besides, disturbance effect on state and estimation error is constrained via H-infinity performance for improving transient response. Through using the extended projection lemma, the derived conditions are formulated as strict Linear Matrix Inequality (LMI) problem. To avoid the potential oscillation, another lemma is proposed to separately assign the performance level during solving the conditions. All feasible solutions can be directly obtained by convex optimization algorithm without using extra calculation. Therefore, an observer-based controller is established to guarantee the asymptotical stability and H-infinity performance in the mean square. Finally, some numerical simulations are provided to demonstrate the effectiveness and applicability of the proposed method.

关键词： stochastic systems linear matrix inequalities H Lyapunov-Krasovskii function stochastic difference function observers convex optimization algorithm asymptotical stability performance convex programming delayed LPV stochastic system delayed linear parameter varying stochastic system stability criterion numerical simulations asymptotic stability external disturbance stability criteria state error Lyapunov methods observer-based control control linear parameter varying systems estimation error delays transient response extended projection lemma stochastic behaviors

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Sparse optimization Model Based on Sparse Matrix and Singular Value Vector for Fault Diagnosis of Rolling Bearings

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Qiu, Tianxu Huang, Weiguo Liao, Yi Ding, Chuancang Wang, Jun Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China Intelligent Urban Rail Engn Res Ctr Jiangsu Prov Suzhou 215131 Peoples R China

The extraction and detection of weak faults of rolling bearings on rotating machinery are essential and crucial. However, traditional methods of sparse matrix optimization have shortcomings of amplitude underestimation. Therefore, we propose a sparse optimization method based on sparse matrix and singular value (SV) vector. Due to the intrinsic features of the faulty bearing signals, we extract the sparsity and low-rank property from the matrix of time-frequency diagram. To improve the performance of the detection, we utilize the minimax concave penalty (MCP) to both the elements and the SV vector, which, respectively, enhance the reconstruction accuracy of fault signal. Then, the convexity of the model is proved and the selection of the parameters is also discussed. To solve this convex optimization problem, an iterative algorithm based on alternating direction method of multipliers (ADMM) and forward and backward splitting (FBS) is adopted to attain the global optimal solution. Synthetic signals and experimental signals are utilized to verify the effectiveness and superiority of the proposed method.

关键词： Bearing fault diagnosis convex optimization algorithm minimax concave penalty (MCP) singular value (SV) vector sparse matrix

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MATLAB-based innovative 3D finite element method simulator for optimized real-time hyperthermia analysis

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 244卷 107976-107976页

作者： Ul Abdin, Zain Shah, Syed Ahson Ali Cho, Youngdae Yoo, Hyoungsuk Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Hanyang Univ Dept Biomed Engn Seoul 04763 South Korea Hanyang Univ Dept Elect Engn Seoul 04763 South Korea

Background and Objective: Owing to the significant role of hyperthermia in enhancing the efficacy of chemotherapy or radiotherapy for treating malignant tissues, this study introduces a real-time hyperthermia simulator (RTHS) based on the three-dimensional finite element method (FEM) developed using the MATLAB App ***: The simulator consisted of operator-defined homogeneous and heterogeneous phantom models surrounded by an annular phased array (APA) of eight dipole antennas designed at 915 MHz. Electromagnetic and thermal analyses were conducted using the RTHS. To locally raise the target temperature according to the tumor's location, a convex optimization algorithm (COA) was employed to excite the antennas using optimal values of the phases to maximize the electric field at the tumor and amplitudes to achieve the required temperature at the target position. The performance of the proposed RTHS was validated by comparing it with similar hyperthermia setups in the FEM-based COMSOL software and finite-difference time-domain (FDTD)based Sim4Life ***: The simulation results obtained using the RTHS were consistent, both for the homogeneous and heterogeneous models, with those obtained using commercially available tools, demonstrating the reliability of the proposed hyperthermia simulator. The effectiveness of the simulator was illustrated for target positions in five different regions for both homogeneous and heterogeneous phantom models. In addition, the RTHS was cost-effective and consumed less computational time than the available software. The proposed method achieved 94% and 96% accuracy for element sizes of ��/26 and ��/36, respectively, for the homogeneous model. For the heterogeneous model, the method demonstrated 93% and 95% accuracy for element sizes of ��/26 and ��/36, respectively. The accuracy can be further improved by using a more refined mesh at the cost of a higher computational ***: The proposed hyp

关键词： Finite element method Hyperthermia Hyperthermia treatment planning Annular phased array convex optimization algorithm Homogeneous model Heterogeneous model

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Sparse Low-Rank Matrix Estimation With Nonconvex Enhancement for Fault Diagnosis of Rolling Bearings

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Ma, Juntao Huang, Weiguo Liao, Yi Jiang, Xingxing Ding, Chuancang Wang, Jun Shi, Juanjuan Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China

The diagnosis of bearing early fault is significant and fundamental in machine condition monitoring. An accurate and effective diagnosis is of great importance to avoid further serious accidents. However, existing sparse low-rank (SLR) methods for bearing fault diagnosis suffer from underestimation of amplitude and inaccurate approximation of singular values (SVs). Therefore, in this article, a novel SLR matrix estimation method with nonconvex enhancement (SLRNE) is proposed, extracting the fault transients from observed noisy signal. Specifically, fault transients have both sparse and low-rank properties in time-frequency domain. Based on this, a SLR optimization model is proposed to simultaneously promote the above two properties via truncated nuclear norm (TNN) and generalized minimax concave (GMC) penalty function. The two nonconvex functions aim to promote low-rank property and sparsity, respectively. Then, based on derived convexity conditions of the optimization problem, convex optimization algorithm, alternating direction method of multipliers (ADMMs), and forward-and-backward splitting (FBS) algorithm are applied to obtain a global optimal solution. In the iterative algorithm, a weighting strategy is designed for the SV threshold operator to enhance the effect of fault feature extraction. Simulated and experimental signals verify the effectiveness of SLRNE and contrast experiments verify its superiority.

关键词： Estimation Sparse matrices Feature extraction Fault diagnosis optimization Transient analysis Time-frequency analysis Bearing fault diagnosis convex optimization algorithm sparse low-rank (SLR) matrix estimation truncated nuclear norm (TNN)

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