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The High-Efficiency optimization Design Method for Two-Stage Miller Compensated Operational Amplifier

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2029-2033页

作者： Yang, Yunqi Yin, Xiaoyu Chen, Dongdong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In this brief, a high-efficiency optimization design method is proposed for a two-stage Miller-compensated operational amplifier (TSMCOA). In the proposed method, the parameters and performance metrics of TSMCOA are simulated by Cadence software. Then, the neural network (NN) models are utilized to describe the relationship between its parameters and performance metrics, which can greatly improve simulation efficiency. Based on the performance metrics of TSMCOA, a multi-objective function is established. Then, according to the NN models and multi-objective function, the parameters of TSMCOA are optimized by particle swarm optimization algorithm with linearly decreasing inertia weight (PSO-LDIW). The optimized area is 0.1371 mu m2 72.09 mu 20803

关键词： Multi-objective optimization neural network particle swarm optimization algorithm two-stage Miller-compensated operational amplifier

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APPLICATION OF NONLINEAR BIG DATA ANALYSIS TECHNIQUES IN COMPUTER SOFTWARE RELIABILITY PREDICTION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第1期25卷 55-64页

作者： Gao, Li Wang, Hai Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Nanning Univ Civil & Architectural Engn Coll Nanning 530000 Peoples R China

This research addresses the difficulties of underfitting, overfitting, and convergence to local minima in artificial neural networks for software dependability prediction. The work specifically focuses on enhancing the performance of the con-ventional PSO-SVM model for software reliability prediction. The analysis of the conventional PSO-SVM model and the special features of software reliability prediction serve as the foundation. An improved PSO-SVM software reliability prediction model is developed and the PSO-SVM model and a Backpropagation (BP) prediction model are compared experimentally. The critical metrics assessed include training error, and efficiency. The experimental results reveal that the training error of the enhanced PSO-LSSVM prediction model diminishes rapidly, levelling off after approximately 200 training generations. The BP prediction model requires 1,733 generations to meet training requirements. Furthermore, the improved PSO-LSSVM prediction model demon-strates significantly higher training efficiency than the BP prediction model. The optimized prediction model exhibits superior adaptability to small sample sizes, swift training, and high prediction accuracy, making it a more suitable choice for software reliability prediction applications.

关键词： particle swarm optimization algorithm Support vector machine Software reliability Prediction Training error Efficiency

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Multi-UAV pursuit-evasion gaming based on PSO-M3DDPG schemes

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COMPLEX & INTELLIGENT SYSTEMS 2024年第5期10卷 6867-6883页

作者： Zhang, Yaozhong Ding, Meiyan Zhang, Jiandong Yang, Qiming Shi, Guoqing Lu, Meiqu Jiang, Frank Northern Polytech Univ Xian Peoples R China Guangxi Minzu Univ Nanning Peoples R China UTS Univ Technol Sydney Ultimo NSW Australia

The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm is a multi-agent reinforcement learning algorithm, which introduces the minimax theorem into Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It also has unstable convergence caused by sparse sample data and randomization. However, the particle swarm Optimisation (PSO) algorithm, unlike traditional reinforcement learning methods, involves the construction of independent populations of policy networks to generate sample data, followed by training the reinforcement learning algorithm. PSO optimizes and updates the policy population based on a fitness function, aiming to enhance the efficiency and convergence speed of the algorithm in learning from the sample data. In order to address the multi-agent pursuit-evasion problem, we propose the PSO-M3DDPG algorithm, which combines the PSO algorithm with the M3DDPG algorithm. Through experimental simulations, the improved algorithm demonstrates superior training results and faster convergence speeds, thus validating its effectiveness.

关键词： Pursuit-evasion game particle swarm optimization algorithm Reinforcement learning M3DDPG (mini-max-multi-agent deep deterministic policy gradient)

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Estimation method of layered ground thermal conductivity for U-tube BHE based on the quasi-3D model

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RENEWABLE ENERGY 2023年第1期213卷 121-133页

作者： Deng, Zhenpeng Nian, Yongle Cheng, Wen-long Univ Sci & Technol China Dept Thermal Sci & Energy Engn Hefei 230027 Anhui Peoples R China

The distribution of ground thermal conductivity (As) is a significant consideration for the design of ground source heat pump systems. This paper proposed a new method to estimate the layered As for U-tube borehole heat exchanger (BHE) using particle swarm optimization (PSO) and distributed thermal response test (DTRT) data. Firstly, a quasi-3D heat transfer model considering geothermal gradient for U-tube BHE had been built to simulate fluid temperature profiles. And the experiment in DTRT was performed using a 45 m sandbox. Then, the layered As were estimated through PSO and DTRT data based on the established model. The results showed that the convergent As distribution could be reliably obtained by short-term DTRT. Furthermore, the estimation results are independent of the number of layers and data points. The mean difference in As distribution between 9 layers and 45 layers is 0.007 W/(m & BULL;K) and the layered As also could be obtained when the number of layers is more than the number of temperature data points, and the root mean square error between 30 and 220 points is less than 0.3 W/(m & BULL;K).

关键词： Ground source heat pump Layered ground thermal conductivity U-tube borehole heat exchanger Distributed thermal response test particle swarm optimization algorithm

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Investigating the Impact of Demand Management on the Microgrid with the Presence of Renewable Resources Uncertainty and Reliability

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2024年第3期8卷 889-903页

作者： Hong, Wei Tao, Zheng hua Yang, Bo Changzhou Univ Changzhou 213164 Peoples R China NARI Technol Dev Co Ltd Nanjing 211106 Peoples R China

The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable loads. In the distribution system, microgrids can use renewable energy sources to be operated in far-away regions at lower investment costs. The energy industry faces numerous problems, including problems with energy efficiency, ensuring system confidence, and reducing the destructive environmental effects. Load management programs can help address the challenges confronting the energy industry. This essay proposes a method for evaluating the load responsiveness in microgrids. A combined algorithm, including a gravitational search algorithm as well as particle swarm optimization, to address a multi-objective optimization function. The Latin hypercube sampling method is adopted to create diverse scenarios, which are reduced by the K-means method. The objective function includes grid losses, generation costs, confidence index, and voltage stability. The suggested method is implemented on a modified 69-bus system consisting of wind turbines, solar power stations, and energy storage systems using the combined optimization algorithm in Matlab. The results show that increasing renewable energy production reduces power losses, power consumption, and cost per kilo and improves voltage deviation. Adding renewables and energy storage also reduces power fluctuations and grid losses. Adding renewable sources brings benefits such as reduced production costs, losses, and improved voltage profiles. From the simulation outcomes, it can be seen that the average voltage profile has been improved by about 4%. Also, increasing the responsive load has improved the mains voltage profile and increased by about 10%.

关键词： Energy Photovoltaic particle swarm optimization algorithm Wind energy Gravitational search optimization algorithm

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Energy-saving diagnosis of public buildings based on multi-objective optimization algorithm

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INTELLIGENT BUILDINGS INTERNATIONAL 2024年第2期16卷 59-72页

作者： Yuan, Yousheng Bai, Chaoqin Henan Univ Sci & Technol Sch Civil Engn & Architecture Luoyang 471000 Peoples R China

This study focuses on existing ventilation, heating, and air conditioning systems in public buildings and mainly considers three objectives: system energy consumption, indoor thermal comfort, and efficiency. A multi-objective diagnosis system is established, which correlates evaluation indicators with key equipment, forming a clear hierarchical diagnosis system. Meanwhile, particle swarm optimization algorithm is combined with linear weighting method to optimize the operating parameters of key equipment based on the diagnosis results, obtaining the optimal parameters of various operating scenes. For winter conditions, the average system energy consumption is 34.2 W/m2, the average system energy efficiency ratio is 1.5, and the average indicator for indoor thermal environment is 1.06. For summer conditions, the average energy consumption of the systems is 28.9 W/m2, the average energy efficiency ratio is 2.2, and the average indicator for indoor thermal environment is - 0.82. Compared with the measured results, most of the optimized indicators are better, but the system energy efficiency ratio is slightly lower than the measured results for winter conditions. Through the established diagnosis system and optimization method, this research evaluates and optimizes the existing the systems in public buildings. Demonstrating the effectiveness of the established diagnosis system and optimization method.

关键词： Energy-saving diagnosis public buildings multi-objective optimization particle swarm optimization algorithm HVAC systems

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Displacement of mining vibrating screen obtained from acceleration based on improved S-G filter

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Xiao, Linjing Lu, Hao Yan, Fangping Shandong Univ Sci & Technol Dept Elect & Informat Jinan 250031 Shandong Peoples R China Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266590 Peoples R China

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky-Golay (S-G) filter is proposed. The particle swarm optimization (PSO) algorithm is used to optimize the window length of the S-G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

关键词： Vibration displacement Acceleration signals Improved Savitzky-Golay filter particle swarm optimization algorithm Frequency-domain integration

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Cross-social-network user alignment research based on multi-dimensional user features

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Zhao, Tao Gao, Heng Wang, Zecheng Bi, Dianjie Chen, Xuemin Anhui Univ Finance & Econ Dept Comp Sci & Technol Bengbu Peoples R China Texas Southern Univ Dept Engn Houston TX USA

Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particle swarm optimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.

关键词： Social networks user alignment block matrix particle swarm optimization algorithm

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Designing a Squared-Off Cascade for Separation of the ⁹²Mo and ¹⁰⁰Mo Isotopes to High Purity

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JOURNAL OF ENGINEERING PHYSICS AND THERMOPHYSICS 2024年第4期97卷 830-843页

作者： Mirmohammadi, S. L. Safdari, J. Mallah, M. H. Ezazi, F. Atom Energy Org Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch POB 11365-8486 Tehran Iran

The separation of the Mo-92 and Mo-100 isotopes from the natural composition to the 99.99% enrichment level in a three-section squared-off cascade with 200 centrifuges was investigated. The parameters of this cascade were optimized using the 3SQC-PSO and 3SQC-GOA codes and the PSO and GO algorithms. The objective function of the indicated optimization codes is to simultaneously maximize the cascade capacity, the recovery factor, and the D-function. The results obtained show that the separation of the Mo-92 and Mo-100 isotopes should be done in two and one separation steps, respectively. A single cascade of most suitable configuration for the separation of both the Mo-92 and Mo-100 isotopes is proposed. The recovery separations of the Mo-92 and Mo-100 isotopes in this cascade comprise 92 and 95%, respectively.

关键词： lightest and heaviest molybdenum isotopes Mo-92 and Mo-100 three-section squared-off cascade particle swarm optimization algorithm Grasshopper optimization algorithm

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Research on particle swarm Compensation Method for Subdivision Error optimization of Photoelectric Encoder Based on Parallel Iteration

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SENSORS 2022年第12期22卷 4456页

作者： Hou, Han Cao, Guohua Ding, Hongchang Li, Kun Changchun Univ Sci & Technol Mech Engn Fac Changchun 130022 Peoples R China Changchun Univ Sci & Technol Chongqing Res Inst 618 Liangjiang Ave Chongqing 401135 Peoples R China

Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moire grating signals, a particle swarm optimization compensation model for grating the subdivision error of a photoelectric encoder based on parallel iteration is proposed. In the paper, an adaptive subdivision method of a particle swarm search domain based on the honeycomb structure is proposed, and a raster signal subdivision error compensation model based on the multi-swarm particle swarm optimization algorithm based on parallel iteration is established. The optimization algorithm can effectively improve the convergence speed and system accuracy of traditional particle swarm optimization. Finally, according to the subdivision error compensation algorithm, the subdivision error of the grating system caused by the sinusoidal error in the system is quickly corrected by taking advantage of the high-speed parallel processing of the FPGA pipeline architecture. The design experiment uses a 25-bit photoelectric encoder to verify the subdivision error algorithm. The experimental results show that the actual dynamic subdivision error can be reduced to 1/2 before compensation, and the static subdivision error can be reduced from 1.264 '' to 0.487 '' before detection.

关键词： interpolation error compensation grating moire fringe FPGA particle swarm optimization algorithm

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