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A novel grey multivariate model for forecasting landslide displacement

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 103卷 104297-104297页

作者： Li, S. H. Zhu, L. Wu, Y. Lei, X. Q. Southwest Jiaotong Univ Dept Geol Engn Chengdu 610031 Sichuan Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Surface Proc Chengdu 610041 Sichuan Peoples R China

Forecasting landslide displacement is an important issue in engineering geology. In this field, it is difficult to accurately forecast the displacement of the step point of a step-type landslide. In this study, a novel grey multivariate model is proposed for forecasting landslide displacement. The proposed model transforms the original sequence by using the Hausdorff derivative operator, determines the model parameters by using the particle swarm optimization algorithm, and uses the trapezoidal integral formula to calculate the predicted value. Two numerical examples show that the average absolute relative error and mean squared error of the proposed model are smaller than those of the recursive discrete multivariate grey model and the multivariable grey model with structure compatibility. The proposed model is used to forecast the displacement of the Bazimen landslide in the Three Gorges Reservoir area of China. The previous month's displacement, precipitation, and change in the reservoir water level are used as input variables. The results show that the performance of the proposed model is superior to that of the extreme learning machine model. This paper provides an effective method for forecasting displacement of the step point of a step-type landslide.

关键词： Landslide displacement Forecast Grey multivariate prediction model Hausdorff derivative particle swarm optimization algorithm

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optimization of visible transmission and NIR reflection in multilayer via PSO: Simulation and experimental validation

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Kaihua Chen, Zhiying Guo, Jinyang Gao, Shuai Yu, Kun Zhang, Xiansheng Liu, Yufang Wu, Xiaohu Henan Normal Univ Sch Phys Henan Key Lab Infrared Spectrum Measures & Applica Xinxiang 453007 Peoples R China Shandong Inst Adv Technol Jinan 250100 Shandong Peoples R China Qingdao Univ Qingdao 266071 Shandong Peoples R China

Transparent heat reflective windows (THRW) effectively manage solar energy by reflecting near-infrared (NIR) heat while maintaining high visible (VIS) light transmittance, thus improving building energy efficiency. Conventional THRW designs using dielectric/metal/dielectric (D/M/D) structures often increase metal thickness for better NIR reflectance, which compromises VIS transmittance. In this work, we present a dielectric/metal/ dielectric/metal/dielectric (D/M/D/M/D) multilayer structure, utilizing TiO2 as the dielectric and Ag as the metallic component. The film thicknesses were systematically optimized using the particle swarm optimization algorithm to achieve an optimal balance between high VIS transmittance ((TVIS = 87 %) and high NIR reflectance (RNIR = 95 %). Additionally, the structure shows only 15 % UV transmittance due to absorption by TiO2 and reflection by Ag. The multilayer films are fabricated using electron beam evaporator, with the experimental results show that the multilayers maintain good VIS transmittance as well as high NIR reflectance. This highperformance THRW design demonstrates significant potential for energy-saving applications in hot climates, offering a viable solution for enhancing building energy efficiency and occupant comfort in the future.

关键词： Transparent-heat reflective windows Building energy efficiency Spectrally selective regulation particle swarm optimization algorithm

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Modeling and Performance optimization of Wireless Sensor Network Based on Markov Chain

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IEEE SENSORS JOURNAL 2021年第22期21卷 25043-25050页

作者： Liu, Yutang Zhang, Qin Henan Inst Technol Sch Sci Xinxiang 453002 Henan Peoples R China Xinxiang Univ Sch Math & Stat Xinxiang 453003 Henan Peoples R China

Wireless sensor networks are usually deployed in areas with relatively harsh natural environments, and the collection node transmits data to the destination node through a multi-hop route. Therefore, how to effectively plan the transmission path is an important issue. This paper combines the unbiased gray model with the Markov chain model to establish an unbiased gray Markov chain model, and points out that the unbiased gray Markov chain model also has shortcomings in parameter selection. The particle swarm algorithm is used to improve it, and the mathematical model, calculation principle and various parameters of the particle swarm optimization algorithm are introduced, and the implementation flow chart of the particle swarm algorithm is given. Aiming at the shortcomings of the unbiased gray Markov chain model, the particle swarm algorithm and the unbiased gray Markov chain model are combined to form the particle swarm unbiased gray Markov chain model. The simulation environment and the training environment of the particle swarm unbiased grey Markov chain model were designed in the experiment. The node scheduling optimization experiment proves that the scheduling method based on the particle swarm unbiased grey Markov chain model has achieved better results in coverage and energy consumption balance than the random and shortest distance method. In the routing experiment, the experimental analysis of the node's Q value proved the convergence of the algorithm, and compared with other protocols, it proved that the routing algorithm can effectively extend the network life cycle and achieve load balancing.

关键词： Sensors Wireless sensor networks Markov processes Mathematical model Routing particle swarm optimization Monitoring Wireless sensor network Markov chain particle swarm optimization algorithm routing protocol unbiased gray model

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Reliability evaluation of concrete-filled steel tube arch bridge based on DBN-PSOSA hybrid algorithm

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STRUCTURAL CONCRETE 2021年第4期22卷 2309-2321页

作者： Lu, Pengzhen Xu, Zijie Hong, Tao Wu, Ying Li, Dengguo Zhejiang Univ Technol Coll Civil Engn Hangzhou 310014 Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing Peoples R China

When traditional response surface method is used to evaluate the reliability of concrete-filled steel tubular arch bridge, due to its complex structure and highly nonlinear implicit function, the response surface fitting accuracy is not high, and the reliability accuracy is difficult to meet the requirements of design specifications. In order to solve the above problems, this paper chooses dynamic Bayesian networks (DBN) which is suitable for solving the problem of multiple state unit or system uncertainty to build implicit function of the response surface function. And this paper combines DBN and particle swarm optimization algorithm based on simulated annealing algorithm (PSOSA) to improve efficiency of model parameter optimization. It can make the construction of implicit function simulate the real structure of the limit state function. Then this paper verifies the suitability for this kind of complex structure reliability assessment of DBN-PSOSA hybrid algorithm. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of a concrete filled steel tube arch bridge project is evaluated. The results show that this method improves the calculation accuracy and efficiency.

关键词： annealing algorithm bridge engineering dynamic Bayesian networks particle swarm optimization algorithm reliability analysis the response surface

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Frequency-based tension assessment of an inclined cable with complex boundary conditions using the PSO algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2021年第5期79卷 619-639页

作者： Zhang, Wen-Ming Wang, Zhi-Wei Feng, Dan-Dian Liu, Zhao Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China

The frequency-based method is the most commonly used method for measuring cable tension. However, the calculation formulas for the conventional frequency-based method are generally based on the ideally hinged or fixed boundary conditions without a comprehensive consideration of the inclination angle, sag-extensibility, and flexural stiffness of cables, leading to a significant error in cable tension identification. This study aimed to propose a frequency-based method of cable tension identification considering the complex boundary conditions at the two ends of cables using the particle swarm optimization (PSO) algorithm. First, the refined stay cable model was established considering the inclination angle, flexural stiffness, and sag-extensibility, as well as the rotational constraint stiffness and lateral support stiffness for the unknown boundaries of cables. The vibration mode equation of the stay cable model was discretized and solved using the finite difference method. Then, a multiparameter identification method based on the PSO algorithm was proposed. This method was able to identify the tension, flexural stiffness, axial stiffness, boundary rotational constraint stiffness, and boundary lateral support stiffness according to the measured multiorder frequencies in a synchronous manner. The feasibility and accuracy of this method were validated through numerical cases. Finally, the proposed approach was applied to the tension identification of the anchor span strands of a suspension bridge (Jindong Bridge) in China. The results of cable tension identification using the proposed method and the existing methods discussed in previous studies were compared with the on-site pressure ring measurement results. The comparison showed that the proposed approach had a high accuracy in cable tension identification. Moreover, the synchronous identification of the flexural stiffness, axial stiffness, boundary rotational constraint stiffness, and boundary lateral support stif

关键词： boundary constraint stiffness cable tension finite difference method frequency-based method multiparameter identification particle swarm optimization algorithm

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Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Wang, Hui Liao, Xu Ji, Xiu Gong, Shanggao Meng, Xiangping Wang, Jiarui Changchun Inst Technol Sch Elect Engn & Informat Technol Changchun Peoples R China Natl & Local Joint Engn Res Ctr Smart Distribut Ne Changchun Peoples R China State Grid JILIN Elect Power Res Inst Changchun Peoples R China

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strate

关键词： smart buildings peer-to-peer power sharing retail electricity provider market bargaining particle swarm optimization algorithm alternating direction multiplier method

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Design and analysis of energy efficient urban buildings based on BIM model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3785-3798页

作者： Wu, Yang Chongqing Business Vocat Coll Dept Publishing Media Chongqing 401331 Peoples R China

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other tec

关键词： BIM energy saving architecture particle swarm optimization algorithm multiple objectives simulation

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Multi-Stress Accelerated Degradation Testing Reliability Assessment of LED Lamp Beads Considering Generalized Coupling

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APPLIED SCIENCES-BASEL 2024年第19期14卷 8767页

作者： Dong, Yinglong Zhou, Zhen Liu, Kaixin Yang, Xu Harbin Univ Sci & Technol Sch Measurement & Commun Harbin 150080 Peoples R China Gen Machine Tool Res Inst Inst Performance & Test Technol Beijing 101318 Peoples R China

Due to the complex operating environment of LED lamp beads (hereinafter referred to as LED), the test method that only considers the action of multiple stresses alone ignores the mutual influence between stresses, making it difficult to accurately obtain the life indicators of LEDs, resulting in a large deviation in the theoretical life results. In this regard, this paper proposes a multi-stress accelerated degradation evaluation method considering generalized coupling and conducts an accelerated degradation test (ADT) to evaluate the life of LEDs. We identified three stress sources and designed five new high-gradient ADTs. Through experimental data, we found that the three stress sources are strongly coupled on this LED. Then, a generalized coupling maximum likelihood estimation method (MLE) for the entire sample was constructed, and the particle swarm algorithm was used to solve the parameters. Finally, the life of this LED was evaluated based on the experimental data. The results show that the life of the LED considering multiple-stress coupling is within 6.5% of the historical life scatter point, which is more in line with the actual working environment.

关键词： LED accelerated degradation test multi-stress coupling particle swarm optimization algorithm reliability

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Fast Search of Lightweight Block Cipher Primitives via swarm-like Metaheuristics for Cyber Security

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第4期21卷 1–15页

作者： Jin, Xin Duan, Yuwei Zhang, Ying Huang, Yating Li, Mengdong Mao, Ming Singh, Amit Kumar Li, Yujie Beijing Elect Sci & Technol Inst 7 Fufeng Rd Beijing Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China Natl Inst Technol Patna Patna Bihar India Kyushu Inst Technol Kitakyushu Fukuoka Japan

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Synunetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarmlike normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.

关键词： Lightweight block cipher SKINNY metaheuristic algorigthm particle swarm optimization algorithm

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A particle swarm optimization algorithm and its application in hydrodynamic equations

A particle swarm optimization algorithm and its application ...

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2nd International Conference on Machinery, Materials Science and Engineering Applications (MMSE 2012)

作者： Zhou, Jianhui Zhao, Shuzhong Yue, Lixi Lu, Yannan Si, Xinyi Hebei United Univ Coll Qianan Tangshan Peoples R China Hehai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

ISBN: (纸本)9783037854099

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. A particle swarm optimization algorithm combining with the direct search method (DSPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

关键词： particle swarm optimization algorithm Direct search method Ordinary differential equations Parameter estimation

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