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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Fang Xianbao Yong, Lin Su Yian Zhong Letian Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

In view of the low precision of DV-Hop positioning algorithm in wireless sensor network, an improved DV-Hop localization algorithm that fuses chaotic sequences and Grey wolf optimization (GWO) algorithms is proposed. Firstly, in order to reduce the average jump distance error, the algorithm introduces the correction factor, and secondly, for the cumulative calculation error brought about by the least squares method, the improved gray wolf optimization algorithm is used as an alternative, in order to further improve the positioning accuracy and convergence speed, the chaotic sequence is introduced to initialize the gray wolf population, adaptive adjustment strategy is applied to control parameter a, and the algorithm global search ability is improved by using random walking strategy. The simulation results show that compared with the traditional DV-Hop algorithm, GA-DVhop algorithm and PSO-DVhop algorithm, the average positioning accuracy is improved by 90%, 86% and 78%.

关键词： wireless sensor networks DV-Hop algorithm gray wolf optimization algorithm chaotic sequence adaptive policies

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Evolution Model of Open-Source Software Projects in GitHub 2

Evolution Model of Open-Source Software Projects in GitHub

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2nd IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) / 7th International Workshop on Pattern Recognition (IWPR)

作者： Wang, Hongbing Ji, Haoran Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (数字)9781665482233

ISBN: (纸本)9781665482233

In the past decade, open-source software (OSS) has become a very popular research topic in the field of software engineering. Because its code is open to the public, it has been pursued by programming teams all over the world, including those in universities, government agencies and enterprises. In addition to analyzing some famous OSS projects, researchers also studied OSS projects and their functions in GitHub. However, the evolution process and the rules of OSS projects in GitHub have not been widely investigated so we conducted in-depth research on this issue. We use the cellular automata (CA) in the field of system dynamics to model the OSS project, construct the evolution rules of the gray wolf optimization (GWO) algorithm, define the objective vector, objective function and key optimization operators of GWO for OSS cellular evolution, and realize the intelligent acquisition of OSS project evolution rules in GitHub. In addition, we also selected the data of some OSS projects in GitHub from 2015 to 2020 for the simulation experiment. The experiment results show that the total accuracy of the simulation is 91.4%, which is consistent with real data.

关键词： Open-source software project GitHub cellular automata gray wolf optimization algorithm evolution rules

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3D Path Planning algorithm for Unmanned Underwater Vehicles Based on Improved Grey wolf optimization algorithm 14

3D Path Planning Algorithm for Unmanned Underwater Vehicles ...

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14th Asian Control Conference (ASCC)

作者： Chang, Peng Wang, Yintao Yao, Yao Han, Zhengqing Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710000 Peoples R China Jiangsu Automat Res Inst Lianyungang 222000 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

To enhance the route planning capabilities of underwater unmanned vehicles in complex three-dimensional ocean environments and to overcome the limitations of traditional planning methods, such as simplistic handling of environmental factors and optimization indicators, this paper introduces a new three-dimensional route planning algorithm from the perspectives of navigation safety and stealth to achieve optimal route planning. Firstly, addressing the issues of insufficient global search capability, low optimization accuracy, and slow convergence speed in traditional grey wolf optimization algorithm, this paper proposes an improved grey wolf optimization algorithm. This algorithm implements a nonlinear adjustment of the convergence factor, thereby better balancing the global and local search capabilities of the algorithm. Additionally, it introudces a speed evolution factor into the algorithm, enabling adaptive mutation within the grey wolf population and enhancing the search capability of the algorithm. Secondly, this paper employs a grid model to describe the three-dimensional ocean space environment, taking into account the performance of underwater platforms, terrain, ocean environment, and mission constraints. A three-dimensional route planning strategy based on the improved grey wolf optimization algorithm is constructed, and multi-constraint heuristic functions and multi-indicator evaluation functions are designed. The simulation results demonstrate that the algorithm fully considers the impact of ocean environmental factors and is capable of realizing three-dimensional planning under different performance indicators and mission constraints, effectively meeting the practical navigation needs of underwater platforms.

关键词： gray wolf optimization algorithm Underwater unmanned vehicles Path planning Three-dimensional underwater environment

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Fault Location of Active Distribution Network Based on Improved gray wolf algorithm

Fault Location of Active Distribution Network Based on Impro...

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Chinese Automation Congress (CAC)

作者： Li, Zheng Zhang, Xiao Song, Qiang Wu, Na Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China State Grid Shandong Elect Power Co Jining Power Supply Co Jining Peoples R China

ISBN: (纸本)9781728176871

As the scale of the power grid continues to expand and a large number of distributed power sources are connected, the difficulty of locating faults is increasing. The gray wolf optimization algorithm is simple in principle, ease to implement and has good global performance, so it is widely used, but the algorithm also has the defects of low optimization accuracy and easy to fall into the local optimal solution. Based on the gray wolf algorithm based on dynamic update weights, this paper proposes an improved gray wolf optimization algorithm. The algorithm first improved the C strategy to balance the global exploration and local development capabilities of the algorithm;then the sine and cosine global optimization strategy was used to guide and update the position again to reduce the premature probability and improve the algorithm optimization accuracy;finally, the adaptive local optimization strategy was used to select the optimal solution through neighborhood comparison to avoid the algorithm falling into the local optimal solution. The gray wolf optimization algorithm before and after the improvement is used to simulate and analyze the fault section location, which further verifies the correctness and superiority of the algorithm.

关键词： Active distribution network fault location gray wolf optimization algorithm sine and cosine self-adaption

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A New OMVDR Adaptive Anti-inference Beamforming algorithm 13

A New OMVDR Adaptive Anti-inference Beamforming Algorithm

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13th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Chen, Baiju Li, Bo Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Shaanxi Peoples R China

ISBN: (纸本)9781728157337

Beamforming is a key technology in millimeter-wave communication. By controlling the transmitter and receiver of a millimeter-wave antenna array, the signal-to-noise ratio (SNR) can be effectively improved, and interference and noise can be suppressed. In order to further enhance the anti-interference ability of beamforming, under the condition of minimum variance distortionless response (MVDR) linear constraint, this paper uses the gray wolf optimization (GWO) algorithm numerical search capability to optimize the weights. An optimized minimum variance distortionless response (OMVDR) adaptive beamforming algorithm is presented. In the selection of the fitness function, the idea of separating amplitude and phase is adopted, and the fitness function is set to the weighted sum of the mean square error of the output signal and the input signal, thereby realizing the simultaneous optimization of the weight amplitude and phase. Simulation results show that OMVDR has lower sidelobe level and null and better anti-interference ability than traditional MVDR algorithms and PSO-MVDR.

关键词： beamforming gray wolf optimization algorithm minimum variance distortionless response anti-interference

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A Fault Diagnosis Method Based on GWO-DELM for Aeroengine Gas Path 34

A Fault Diagnosis Method Based on GWO-DELM for Aeroengine Ga...

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34th Chinese Control and Decision Conference (CCDC)

作者： Cui, Jianguo Yu, Qian Cui, Xiao Jiang, Liying Yu, Mingyue Du, Wenyou Liu, Dong Wang, Jinglin Shenyang Aerosp Univ Sch Automat Shenyang 110136 Peoples R China AVIC Aerodynam Res Inst Model Balance & Wind Tunnel Equipment Dept 5 Shenyang 110034 Peoples R China Aviat Key Lab Sci & Technol Fault Diag & Hlth Man Shanghai 201601 Peoples R China

ISBN: (纸本)9781665478960

Aiming at the problem of complex working mechanism of aeroengine gas path system and difficulty in effective fault diagnosis in actual work, a new fault diagnosis method of aeroengine gas path based on gray wolf optimization Deep Extreme Learning Machine (GWO-DELM) is proposed. Firstly, analyze a large amount of monitoring data of a certain type of aeroengine gas path components, and sort out the health data and fault sample data sets. Secondly, create the DELM fault diagnosis model by the health data and fault sample data set of the aeroengine gas circuit system. To reduce the influence of artificially setting network parameters on the diagnosis results, the gray wolf optimization algorithm (GWO) is used to optimize the DELM network parameters, and the optimal DELM fault diagnosis model GWO-DELM is created. Finally, the GWO-DELM fault diagnosis model is used to study the fault diagnosis verification technology of the aeroengine air circuit system, and the diagnosis results of the ELM, DELM and Multilayer Kernel Extreme Learning Machine (ML-KELM) fault diagnosis models are compared. The result shows that the fault diagnosis accuracy of the proposed GWO-DELM fault diagnosis model is 96.0%, which is significantly higher than that of the ELM model of 88.0%, the DELM model of 92.0% and the ML-KELM model of 94.0%, the effectiveness of the proposed method is verified, and it has a good application prospect.

关键词： Deep Extreme Learning Machine gray wolf optimization algorithm Fault Diagnosis Aeroengine

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Three-dimensional DV-Hop Localization Based on Improved Lion Swarm optimization algorithm

Three-dimensional DV-Hop Localization Based on Improved Lion...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Ji, Falei Jiang, Mingyan Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

ISBN: (纸本)9781728173276

With the development of wireless sensor networks, research on three-dimensional(3D) node localization algorithms is becoming more and more important. 3D Distance Vector Hop(DV-Hop) is a non-ranging-based 3D positioning algorithm with low positioning accuracy and large errors. Aiming at above problems, 3D DV-Hop localization based on improved lion swarm optimization(ILSO) algorithm is proposed. The wolf swarm hunting idea of gray wolf optimization algorithm and the herd interaction idea of sheep optimization algorithm are used to improve the lion swarm optimization algorithm. The ILSO algorithm is compared with several algorithms and performs well. Then it is applied to the optimization of unknown node coordinates. Simulation results show that the proposed algorithm has higher positioning accuracy than classic 3D DV-Hop algorithm and the 3D DV-Hop algorithm based on the original lion swarm optimization algorithm.

关键词： Wireless sensor network 3D DV-Hop localization Improved lion swarm optimization algorithm Sheep optimization algorithm gray wolf optimization algorithm

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A Data-Driven Knowledge System for Anomaly Detection in the Oil & Gas Industry

A Data-Driven Knowledge System for Anomaly Detection in the ...

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2023 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2023

作者： Mazzuto, G. Carbonari, S. Bevilacqua, M. Ciarapica, F.E. Università Politecnica Delle Marche Department of Industrial Engineering and Mathematical Sciences Ancona Italy

ISBN: (纸本)9798350323153

Following the technological and digital developments introduced by Industry 4.0, the vast amount of information generated by an industrial plant increasingly requires more efficient and accurate management mechanisms for its real-time management. The proposed approach combines the Fuzzy Cognitive Maps (FCMs) methodology and the gray wolf optimization (GWO) algorithm for the anomaly detection of an industrial plant with reference to the oil & gas sector. The power of FCMs mathematics and the flexibility and accuracy of the GWO algorithm allow real-time identification of the plant status and, if an anomaly is detected, discriminate potential causes. Moreover, although the FCM methodology is comparable to a neural network, it requires far fewer parameters to train the model, resulting in less computational time. © 2023 IEEE.

关键词： Fuzzy Cognitive Maps gray wolf optimization algorithm Predictive Maintenance Prognostics and Diagnostics System

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Compaction property prediction of mixed gangue backfill materials using hybrid intelligence models: A new approach

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CONSTRUCTION AND BUILDING MATERIALS 2020年 247卷 118633-000页

作者： Li, Baiyi Yan, Hao Zhang, Jixiong Zhou, Nan China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China

Solid backfill coal mining has become one of the main means of green mining in coal mines because of its ability to control surface subsidence and disposal of surface gangue. Mixed gangue backfill material (MGBM) is the key factor for stratum control in solid backfill mining, and its compact mechanical performance directly affects the efficiency of backfill mining. In order to better carry out backfill mining design and backfill effect evaluation, a new hybrid artificial intelligence model integrating support vector machines (SVM), differential evolution algorithm (DE) and gray wolf optimization algorithm (GWO) is proposed to predict the compaction property of MGBM. The cement, lime and fly ash materials are selected to be mixed with gangue backfill materials and a large number of compaction tests using a self-made circular cylinder barrel are carried out to provide the dataset for the DGWO-SVM hybrid model. The input variables of this model include cement content, lime content, fly ash content and overburden stress, and the output variable of the model is the compaction property of MGBM. The performance of the DGWO-SVM model is evaluated by R-2, MAE and RMSE. The predictive results indicate that the DGWO-SVM hybrid model can accurately predict the compaction property of MGBM, and the R-2 of the training set and the testing set are 0.9518, 0.9137. Meanwhile, the relative importance of each input variable is implemented using the MIV method, and the importance scores of cement content, lime content, fly ash content and overburden stress are 0.3266, 0.0738, 0.2448, 0.3548, respectively. The research results can provide guidance for the optimization design of solid backfill mining. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Mixed gangue backfill materials Compaction property Support vector machines Differential evolution algorithm gray wolf optimization algorithm Predictive model

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Optimized preparation of gangue waste-based geopolymer adsorbent based on improved response surface methodology for Cd(II) removal from wastewater

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ENVIRONMENTAL RESEARCH 2023年第1期221卷 115246页

作者： Dong, Chaowei Zhou, Nan Zhang, Jixiong Lai, Wanan Xu, Jianfei Chen, Junlin Yu, Runhua Che, Yepeng China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Peoples R China Hebei Univ Technol Arizona Coll Technol Tianjin 300401 Peoples R China Jiangsu Prov Acad Environm Sci Nanjing 210036 Peoples R China China Coal Energy Xinjiang Tianshan Coal Power Co Beijing 831200 Xinjiang Peoples R China

Resource utilization of gangue solid waste has become an essential research direction for green development. This study prepared a novel gangue based geopolymer adsorbent (GPA) for the removal of Cd(II) from waste-water using pretreatment gangue (PG) as the main raw material. The ANOVA indicated that the obtained quadratic model of fitness function (R2 > 0.99, P-value <0.0001) was significant and adequate, and the contribution of the three preparation conditions to the removal of Cd(II) was: calcination temperature > Na2CO3: PG ratio > water-glass solid content. The hybrid response surface method and gray wolf optimization (RSM-GWO) algorithm were adopted to acquire the optimum conditions: Na2CO3:PG ratio = 1.05, calcination tem-perature of 701 degrees C, solid content of water glass of 22.42%, and the removal efficiency of Cd(II) by GPA obtained under the optimized conditions (GPAC) was 97.84%. Adsorption kinetics, adsorption isotherms and character-ization by XRD, FTIR, Zeta potential, FSEM-EDS and BET were utilized to investigate the adsorption mechanism of GPAC on Cd(II). The results showed that the adsorption of Cd(II) from GPAC was consistent with the pseudo -second-order model (R2 = 0.9936) and the Langmuir model (R2 = 0.9988), the adsorption was a monolayer adsorption process and the computed maximum Cd(II) adsorption (50.76 mg g-1) was approximate to experi-mental results (51.47 mg g-1). Moreover, the surface morphology of GPAC was rough and porous with a specific surface area (SSA) of 18.54 m2 g-1, which provided abundant active sites, and the internal kaolinite was destroyed to produce a zeolite-like structure where surface complexation and ion exchange with Cd(II) through hydroxyl (-OH) and oxygen-containing groups (-SiOH and-AlOH) were the main adsorption mechanisms. Thus, GPAC is a lucrative adsorbent material for effective Cd(II) wastewater treatment, complying with the "high value-added" usage of solid wastes and "waste to cure poison" green sustaina

关键词： Adsorption mechanism Gangue waste utilization Geopolymer adsorbents gray wolf optimization algorithm Response surface method

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