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2021 International Conference on Signal Processing and Machine Learning, CONF-SPML 2021

作者： Qinyang,Lv Jincheng No.2 Middle School

ISBN: (纸本)9781665417341

Obstacle avoidance path planning is a key technology for autonomous vehicles in identifying obstacles and avoiding obstacles,which is of great significance to the development of autonomous driving *** article gives an overview of traditional algorithms and intelligent algorithms related to obstacle avoidance path planning technology for autonomous vehicles,analyzes,compares and summarizes the advantages and disadvantages of each algorithm,and introduces their combined *** considering the advantages and disadvantages of using a single algorithm to plan obstacle avoidance paths in practical applications,it is found that a single algorithm shows drawbacks in a dynamic environment,such as poor computing ***,it is concluded that the use of multiple algorithms can make up for the shortcomings of a single algorithm,which has many advantages and will be focus of automatic obstacle avoidance research in the future.

关键词： autonomous vehicle obstacle avoidance path planning algorithm traditional obstacle avoidance algorithm intelligent optimization algorithm

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Big Data Feature Mining Method for Energy Storage System of Photovoltaic Power Station in Typical Scenarios 4

Big Data Feature Mining Method for Energy Storage System of ...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Sheng, Xinxin Yang, Qingbo Ye, Ruili Xie, Lin Feng, Chaohan Tao, Lei Zhang, Zhoujie China Electric Power Research Institute Beijing Key Lab of Research and System Evaluation of Power Dispatching Automation Technology Beijing China

ISBN: (纸本)9798350363586

The energy storage system can effectively solve the challenges brought by the high proportion of renewable energy access to the power grid. In this paper, a big data feature mining method based on double-layer intelligent optimization algorithm is proposed. Considering the differences in operating conditions of energy storage systems in photovoltaic power stations caused by different seasonal scenarios and different weather scenarios, the improved bird swarm algorithm is proposed to mine the big data features of energy storage systems in photovoltaic power stations under typical weather scenarios. The inherent operation law of the energy storage system in the actual photovoltaic power station is revealed, and the charging and discharging characteristics of the actual energy storage system are reflected. © 2024 IEEE.

关键词： big data energy storage system feature mining intelligent optimization algorithm photovoltaic power station the dispatching and control cloud

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A Survey on Route Planning Methods of AUV Considering Influence of Ocean Current

A Survey on Route Planning Methods of AUV Considering Influe...

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IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Sheng Liang Qiu Zhi-ming Li Heng Naval Univ Engn Coll Weaponry Engn Wuhan Hubei Peoples R China Naval Aviat Univ Coll Aviat Fdn Yantai Peoples R China Naval Res Acad Beijing Peoples R China

ISBN: (纸本)9781538678879

When AUV travel in the ocean, it will unavoidably be affected by the current. Different from the ground and air environment, the influence of ocean current on AUV route planning is much greater than that of wind to ground robot and air UAV path planning. Firstly, we describe the research status of intelligent optimization algorithm combined with ocean current in the field of AUV route planning and optimization in detail and analyze the planning performance and adaptive scenario of different algorithms in the flow environment. Secondly, the application of classical optimization and planning algorithm under the influence of ocean current in AUV route planning is reviewed. The analysis shows that the A* algorithm and the level set algorithm combined with the ocean current get more attraction. Thirdly, the difference between the path planning of the robot and route planning of the AUV is compared and the total research distribution of the AUV route planning algorithm considering the influence of the sea current after 2000 is given. Finally, we propose that the extension research based on A* algorithm and level set method should be the future development of AUV route planning in the real ocean environment.

关键词： AUV route planning ocean current intelligent optimization algorithm classical optimization and planning algorithm A* algorithm level set method

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A Clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations

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系统仿真学报 2024年第12期36卷 2782-2796页

作者： Zhou Yaqiong Chen Junqi Li Weishi Qiu Sihang Ju Rusheng College of System Engineering National University of Defense TechnologyChangsha 410073China

To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic *** establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal *** on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun *** results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.

关键词： location problem clustering algorithm intelligent optimization algorithm Pareto front

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Optimized Robust Controller Design Based on CPSOGSA optimization algorithm and H₂/H_∞ Weights Distribution Method for Load Frequency Control of Micro-Grid

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IEEE ACCESS 2021年 9卷 162093-162107页

作者： Zou, Yidong Qian, Jing Zeng, Yun Ismail, Shamseldeen Dao, Fang Feng, Zhaozhan Nie, Cong Mei, Hong Kunming Univ Sci & Technol Faulty Met & Energy Engn Kunming 650000 Yunnan Peoples R China Kunming Univ Sci & Technol City Coll Kunming 650000 Yunnan Peoples R China

In this paper, a mixed H-2/H-1 robust control strategy that considers the weights of the H-2 and H-1 norm in the optimization process is proposed, and it is used to solve the load frequency control (LFC) problem of the micro-grid (MG). The MG load frequency model established in this paper includes battery energy storage system (BESS), fuel cell (FC), wind turbine (WT), photo-voltaic (PV), and diesel engine generator (DEG). The optimal mixed H-2/H-1 robust controller takes the minimum square integral of the system's frequency fluctuation as the goal of control optimization by integrating the robust performance expressed by the H-2/H-1 two norms. The hybrid particle swarm optimization and gravitational search algorithm with chaotic map algorithm (CPSOGSA) is used to optimize the weight value reflecting the H-2 and H-1 performance of the system and the evaluation function's weighting matrix of the output performance so that the controller can reach the optimum under the constraints. Simulation experiments show that the robust controller designed by the proposed method has better dynamic performance when compared with H-1 robust controller, H-2 robust controller, and traditional H-2/H-1 robust controller, and the results are very satisfactory.

关键词： Load-frequency control islanded micro-grid intelligent H-2/H-infinity robust control intelligent optimization algorithm

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Multi-objective optimization of multi-energy heating systems based on solar, natural gas, and air- energy

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷 101394-101394页

作者： Guo, Fuqiang Li, Yan Xu, Zheng Qin, Jinghan Long, Liming Ocean Univ China Mech Engn Coll Engn Qingdao 266100 Peoples R China Qingdao Energy Design & Res Inst Co Ltd Qingdao 266071 Peoples R China

This study focuses on multi-objective optimization of multi-energy heating systems. By evaluating the heating load of the project, a mathematical model combining solar energy, natural gas, and air-energy is established to determine the comprehensive index that includes economic, environmental, and energy indicators. The heating load and the ratio of energy to temperature control are analyzed to optimize the design and operation of the project. Three types of algorithms are evaluated to solve the proposed model, and the chemical reaction optimization algorithm is chosen because of its stability and efficiency. The multi-energy heating systems is optimized via transient simulation using TRNSYS, and the energy consumption of the optimized system is 2.57% less than that of a conventional system. Moreover, the economic cost and CO2 emissions of the optimized system are reduced by 13.94% and 20.67%, respectively, compared with those of the conventional system.

关键词： Multi-objective optimization Multi-energy complementary intelligent optimization algorithm Cluster analysis Transient numerical simulation

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Multi-modal and multi-objective hyperspectral unmixing model based on multi-source data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 227卷

作者： Lin, Jiewen Chen, Jian China Agr Univ Coll Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Yangtze River Delta Nanjing 210044 Peoples R China

Mixed pixels in hyperspectral images are a common imagination in the process of hyperspectral image processing, usually processed using mixed pixel decomposition. To explore the effect of multi-source data fusion on endmember bundle extraction, a multi-modal and multi-objective optimization endmember bundle extraction method based on data fusion (MMO-CDPSO-RSADRDSM) is proposed. Firstly, the initialization particles are randomly assigned in the region by DSM interference;Secondly, according to the characteristics of hyperspectral image space and DSM space, the crowding distance of decision space is improved to improve the diversity of decision space;Finally, the DSM derived weights are integrated into the optimization model to calculate the objective function, and the endmember bundle is extracted iteratively by the above method. The effective endmember bundles extracted by the MMO-CDPSO-RSADRDSM algorithm have mean RMSE (mRMSE) of 0.1853 and 0.1548 on MUUFL and Houston data, respectively, and recombinant minimum mSAD (re-min mSAD) of 0.0325 and 0.0341. The number of endmemebers extracted on the MUUFL dataset is 35, and in Houston it is 36. The experimental results show that this method has good recognition ability for endmembers with high differences and similar hyperspectral bands, and extracts more effective endmember bundles on this basis. This provides a new method for extracting endmember bundles for multi-source data fusion.

关键词： Hyperspectral image Endmember extraction Data fusion intelligent optimization algorithm Multi-modal and Multi-objective

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Structural parameters identification for industrial robot using a hybrid algorithm

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2022年第2期19卷

作者： Liu, Kejin Xia, Junyong Zhong, Fei Zhang, Li Hubei Univ Technol Sch Mech Engn 28 Nanli Rd Wuhan 430068 Hubei Peoples R China

To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot.

关键词： Industrial robot single point repeatability parameters identification intelligent optimization algorithm

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Forecasting carbon price in China using a novel hybrid model based on secondary decomposition, multi-complexity and error correction

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JOURNAL OF CLEANER PRODUCTION 2023年第1期401卷

作者： Yang, Hong Yang, Xiaodie Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important prac-tical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algo-rithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary dec

关键词： Carbon price Forecasting Hybrid framework Mode decomposition intelligent optimization algorithm Error correction

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An optimized deployment strategy of smart smoke sensors in a large space

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第11期16卷 3544-3564页

作者： Liu, Pingshan Fang, Junli Huang, Hongjun Guilin Univ Elect Technol Business Sch Guilin Peoples R China

With the development of the NB-IoT (Narrow band Internet of Things) and smart cities, coupled with the emergence of smart smoke sensors, new requirements and issues have been introduced to study on the deployment of sensors in large spaces. Previous research mainly focuses on the optimization of wireless sensors in some monitoring environments, including three-dimensional terrain or underwater space. There are relatively few studies on the optimization deployment problem of smart smoke sensors, and leaving large spaces with obstacles such as libraries out of consideration. This paper mainly studies the deployment issue of smart smoke sensors in large spaces by considering the fire probability of fire areas and the obstacles in a monitoring area. To cope with the problems of coverage blind areas and coverage redundancy when sensors are deployed randomly in large spaces, we proposed an optimized deployment strategy of smart smoke sensors based on the PSO (Particle Swarm optimization) algorithm. The deployment problem is transformed into a multi-objective optimization problem with many constraints of fire probability and barriers, while minimizing the deployment cost and maximizing the coverage accuracy. In this regard, we describe the structure model in large space and a coverage model firstly, then a mathematical model containing two objective functions is established. Finally, a deployment strategy based on PSO algorithm is designed, and the performance of the deployment strategy is verified by a number of simulation experiments. The obtained experimental and numerical results demonstrates that our proposed strategy can obtain better performance than uniform deployment strategies in terms of all the objectives concerned, further demonstrates the effectiveness of our strategy. Additionally, the strategy we proposed also provides theoretical guidance and a practical basis for fire emergency management and other departments to better deploy smart smoke sensors in a lar

关键词： NB-IoT optimized deployment strategy smart smoke sensors large space intelligent optimization algorithm coverage accuracy deployment cost

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