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A hybrid fuzzy cerebellar model articulation controller based autonomous controller

COMPUTERS & ELECTRICAL ENGINEERING 2002年第1期28卷 1-16页

作者： Guo, C Ye, Z Sun, ZQ Sarkar, P Jamshidi, M Dalian Maritime Univ Lab Simulat & Control Navigat Syst Dalian 116026 Peoples R China Tsing Hua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Univ New Mexico Ctr Autonomous Control Engn Albuquerque NM 87131 USA Univ New Mexico Dept Engn Mech Albuquerque NM 87131 USA Univ New Mexico Dept Comp & Elect Engn Albuquerque NM 87131 USA Univ New Mexico NASA Ctr Autonomous Control Engn Albuquerque NM 87131 USA

This paper proposes a hybrid control architecture and an algorithm which integrates cerebellar model articulation controller (CMAC) or fuzzified CMAC with fuzzy logic control and bang-bang control under an intelligent supervisor. The intelligent control strategy separately dealing vith dynamic and steady state responses of control system is realized. The neuro-fuzzy controller has self-learning abilities in real time and is applied to ship steering and industrial process plants. Simulation case study results demonstrate the effectiveness and robustness of the integrated intelligent control algorithms. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： cerebellar model articulation controller fuzzified CMAC intelligent control multimode control integrated algorithm neuro-fuzzy control autonomous control

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Diabetes prediction model based on deep belief network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第4期21卷 817-828页

作者： Lang, Li-Ying Gao, Zheng Wang, Xue-Guang Zhao, Hui Zhang, Yan-Ping Sun, Sheng-Juan Zhang, Yong-Jian Austria, Ramir S. Hebei Univ Engn Handan 056038 Hebei Peoples R China Hebei Univ Technol Tianjin 300401 Peoples R China Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Hebei Peoples R China Hebei Univ Engn Sch Math & Phys Handan 056038 Hebei Peoples R China Univ Cordilleras Coll Teacher Educ Baguio 2600 Philippines

Diabetes is a disease that seriously endangers human health. Early detection and early treatment can reduce the likelihood of complications and mortality. The predictive model can effectively solve the above problems and provide helpful information for the clinic. Based on this, it is proposed to apply the idea of integrated algorithm in DBN algorithm, collect the hospital data by investigating its related factors, clean and process the collected data, and sample and model the processed data multiple times. It is shown that a single DBN classifier is better than support vector machine and logistic regression algorithm. The model established by the integrated deep confidence network has a significant improvement in classification accuracy compared to a single DBN classifier, and solves the unstable classification effect of a single DBN classifier.

关键词： Diabetes mellitus deep learning integrated algorithm deep believe neural network prediction model

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Implementation of Graphic Vertex-Coloring Parallel Synthesis algorithm Based on Genetic algorithm and Compute Unified Device Architecture

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2017年第1期51卷 32-41页

作者： Shen, Fengxian Jian, Xu Xi, Xianjie Sanjiang Univ 310 Longxi Rd Nanjing Jiangsu Peoples R China Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Aba Teachers Univ Dept Comp Sci Shuimo Town 623002 Sichuan Peoples R China Taizhou Vocat & Tech Coll Dept Comp Taizhou 318000 Peoples R China

Graphic vertex-coloring has long been a classical problem for combinatorial optimization in the field of science and technology. No algorithm can give an optimal solution for graphic vertex-coloring in polynomial time so far, though it plays an important role in the field of mathematical science and technology. Hence the problem has always been a non-deterministic polynomial (NP) complete problem. The current computer parallel technology based on compute unified device architecture (CUDA) is a hot spot in the relevant field. This study put forward a method integrating parallel genetic algorithm and CUDA to solve the problem of graphic vertex-coloring. First, color sequences were coded and parallel genetic operators were designed, which was beneficial to the improvement of algorithm efficacy. Then parallelization reformation was performed on the above integrated algorithm using CUDA. Experimental results demonstrated that, the newly developed algorithm improved the calculation efficiency and reduced the computation time compared to traditional algorithms based on central processing unit (CPU). Thus plenty of cases can be effectively solved if the minimum coloring number of a known graphics is found.

关键词： genetic algorithm compute unified device architecture graph coloring integrated algorithm

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Prediction of Road Rutting Depth Based on PSO-CatBoost 9

Prediction of Road Rutting Depth Based on PSO-CatBoost

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Xing, Zhen-zhen Hao, Jun Pei, Li-li Weng, Yu-han Li, Hang-ying Changan Univ Sch Informat Engn Xian Peoples R China Changan Univ Sch Data Sci & Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

The rutting depth is one of the important indicators for evaluating the performance of road use. Accurately predicting the Rutting Depth Index (RDI) can timely detect and solve the problem of road damage, ensuring driving safety. This article constructs a multi influencing factor dataset for RDI prediction by integrating road detection, climate, and traffic flow data from the AB expressway in Shaanxi Province, China. By comparing the prediction results of four models, namely Random Forest(RF), XGBoost, CatBoost, and LightGBM, it was found that XGBoost and CatBoost has the highest accuracy on the test dataset. To further improve the predictive precision of above model, we utilized the Particle Swarm Optimization (PSO) technique to meticulously adjust the hyperparameters of both XGBoost and CatBoost. The experimental results show that using the PSO-XGBoost and PSO-CatBoost models to predict RDI, the R2 scores reached 0.8029 and 0.8135, respectively. Compared with the XGBoost and CatBoost models, the accuracy improved by 0.0655 and 0.022, respectively. This indicates that the application of PSO significantly enhances the predictive accuracy of both XGBoost and CatBoost models for road rutting prediction, with the PSO-CatBoost model demonstrating superior performance. Consequently, it provides valuable data for road maintenance strategies.

关键词： road rutting depth index integrated algorithm CatBoost PSO optimization algorithm

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A Unified Coordinate Frame Method for GNSS/MEMS-INS Navigation System 31

A Unified Coordinate Frame Method for GNSS/MEMS-INS Navigati...

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第31届中国控制与决策会议

作者： Hailu Wang Zhong Su Ning-Liu Beijing Key Laboratory of high Dynamic Navigation Technology University of Beijing Information Science & Technology

ISBN: (纸本)9781728101071;9781728101064

For GNSS/MEMS-INS integrated navigation system, the traditional geographic coordinates of longitude latitude altitude as navigation parameters involving the floating point operation of angle is improper to be implemented practically. In this paper a unified coordinate transformation algorithm is *** N-coordinate reference frame is served as the reference navigation system. And improved combination algorithms is designed on engineering scenes. Which will reduce the complexity of the combination algorithm, decrease the amount of calculation and reduce the cost of engineering. The method has been tested successfully practically, based on real data and simulation of MEMS sensor and verified the feasibility and navigation accuracy of the algorithm.

关键词： GNSS/MEMS-INS Kalman Filter Uniform Coordinate Frame integrated algorithm

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Comprehensive Review of Video Enhancement algorithms for Low Lighting Conditions 3rd

Comprehensive Review of Video Enhancement Algorithms for Low...

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3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Vishalakshi, G. R. Gopalakrishna, M. T. Hanumantharaju, M. C. Dayananda Sagar Coll Engn Dept Elect & Commun Engn Bangalore Karnataka India Kammavar Sangam Sch Engn & Management Dept Comp Sci Engn Bangalore Karnataka India BMS Inst Technol & Management Dept Elect & Commun Engn Bangalore Karnataka India

ISBN: (纸本)9788132227571;9788132227564

Video enhancement becomes a very challenging problem under low lighting conditions. Numerous techniques for enhancing visual quality of videos/images captured under different environmental situations are proposed by number of researchers especially in dark or night time, foggy situations, rainy and so on. This paper discusses brief review of existing algorithms related to video enhancement techniques under various lighting condition such as De-hazing based enhancement algorithm, a novel integrated algorithm, gradient based fusion algorithm and dark channel prior and in addition it also presents advantages and disadvantages of these algorithms.

关键词： Video enhancement De-hazing integrated algorithm Gradient fusion Dark channel prior Review

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Driving rule extraction based on cognitive behavior analysis

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Journal of Central South University 2020年第1期27卷 164-179页

作者： ZHAO Yu-cheng LIANG Jun CHEN Long CAI Ying-feng YAO Ming HUA Guo-dong ZHU Ning Automotive Engineering Research Institute Jiangsu UniversityZhenjiang 212013China School of Automotive and Transportation Engineering Jiangsu UniversityZhenjiang 212013China Jiangsu Zhixing Future Automobile Research Institute Nanjing 210000China Department of Mechanical Engineering Shizuoka University of Science and TechnologyShizuoka Bagu 437-0032Japan

In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.

关键词： cognitive driving behavior driving rule extraction cognitive theory integrated algorithm

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Obstacle Avoidance Control for Static and Dynamic Obstacles on Intelligent Mobile Platforms in Greenhouses 22

Obstacle Avoidance Control for Static and Dynamic Obstacles ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Kang Meng, Fei Department of System Science University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331527471

This work focuses on the utilization of Mecanum wheel mobile platforms in greenhouse. Its aim is to improve the search efficiency and optimize path planning of Mecanum wheel mobile platforms in intricate environments through the improved A* algorithm. Through the integration of the Dynamic Window Approach (DWA) with the improved A* algorithm and accounting for the kinematic constraints specific to Mecanum wheel mobile platforms, the objective is to achieve swift and precise path planning in dynamic environments. Experimental results demonstrate that the utilization of this integrated algorithm enables Mecanum wheel mobile platforms to surpass traditional algorithms in path planning time, path length, and adaptability to dynamic environments. Furthermore, combining the improved A* algorithm with DWA effectively avoids randomly occurring obstacles, ensuring smooth operation of the platform along the globally optimal path. This intelligent integration into smart agriculture boosts efficiency and intelligence in production operations. © 2024 IEEE.

关键词： integrated algorithm Mecanum Wheel Mobile Platform Path Planning algorithms Smart Greenhouses

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Surface Location Ambiguity Analysis and Suppression algorithm Research based on multilateration

Surface Location Ambiguity Analysis and Suppression Algorith...

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IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)

作者： Gong Fengxun Ma Yanqiu Civil Aviat Univ China Coll Elect Informat & Automat Tianjin Peoples R China

ISBN: (数字)9781728106496

ISBN: (纸本)9781728106496

Both of the TOA or TSOA location algorithm can be used for airport surface location respectively, but they all had location ambiguity in the interesting airspaces, and the GDOP values were very difficulty to improve effectively. In order to resolve these issues, the TDOA/TSOA integrated algorithm model was presented. On the basis of simulation analyzing of the TDOA and TSOA positioning algorithms for the 2D and 3D location on airport surface respectively, the ambiguous points distribution was calculated and summarized in details. And the distribution characteristics of ambiguity areas in positioning with the TDOA/TSOA integrated positioning algorithm were simulated. The simulation results and comparison shows that the integrated algorithm model has smaller ambiguous area and better RMSE value. And lastly, the performance of the TDOA/TSOA integrated algorithm model were compared and analyzed with nine base stations MLAT system in Innsbruck airport. Simulation results indicated that TDOA/TSOA integrated algorithm model was better than that of TDOA model.

关键词： Ambiguity points Positioning error TDOA TSOA integrated algorithm

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Multidimensional soil salinity data mining and evaluation from different satellites

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SCIENCE OF THE TOTAL ENVIRONMENT 2022年 846卷 157416页

作者： Cao, Xiaoyi Chen, Wenqian Ge, Xiangyu Chen, Xiangyue Wang, Jingzhe Ding, Jianli Xinjiang Univ Coll Geog & Remote Sensing Sci Urumqi 830017 Peoples R China Xinjiang Univ Xinjiang Key Lab Oasis Ecol Urumqi 830017 Peoples R China Xinjiang Univ Key Lab Smart City & Envirorm Modelling Higher Educ Inst Urumqi 830017 Peoples R China Lanzhou Univ Key Lab Semiarid Climate Change Minist Educ Coll Atmospher Sci Lanzhou 730000 Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Shenthen Polytech Sch Artificial Intelligence Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (FE) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well fora!! satellites (R-2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R-2 = 0.89, RMSE = 2.57 ***(-1), RPD = 2.05), followed by S2 (R-2 = 0.86, RMSF. = 2.71 ***(-1), RPD = 1.90) and 1.8 (R-2 = 0.85,

关键词： Soil salinity Remote sensing Multidimensionality Data mining integrated algorithm

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