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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A sound performance prediction method based on the artificial neural network (ANN) was proposed to meet the requirements of rapid prediction and optimization design of resonant sound-absorbing metamaterials. Firstly, a theoretical model was established for multilayer perforated resonant sound-absorbing metamaterials (MPRSMs) composed of microperforated panels and Helmholtz resonators, which was then verified through simulation and experiments; subsequently, a dataset was generated with the theoretical model, and in turn an ANN model was constructed by means of the back propagation (BP) neural network to build the mapping relationship between structural parameters and acoustic performances; afterwards, the trained ANN model was combined with the genetic algorithm to optimize the acoustic performance of the MPRSMs. The results show that, the trained ANN model can accurately predict the sound absorption performance of the MPRSMs, and the prediction efficiency improves by more than 50% compared to the theoretical model; the combination of the ANN model and the optimization algorithm can not only improve the optimization efficiency, but bring good low-frequency broadband sound absorption performance of the optimized structure. The ANN provides convenience for large-scale structural performance prediction calculations and has broad application prospects in structural design and optimization of metamaterials. [ABSTRACT FROM AUTHOR]

The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

To solve the flexible job shop dynamic scheduling problem with fuzzy quality control time, which is more realistic, this paper established two rescheduling models for machine breakdown and urgent order insertion with the objective of minimizing completion time. Two models were for three failure scenarios: emergency order insertion, machine failure during no-load operation, and machine failure during work-piece processing. To solve this model, this paper designed a novel genetic-neighborhood search algorithm that integrated cellular-automata-based neighborhood search and random restart hill-climbing. Aiming at the characteristics of dual sub-decisions involving order sequencing and machine selection, this paper designed a two-layer coding scheme including process code and machine code, and designed corresponding genetic operations such as crossover and mutation for them. After that, this paper applied the genetic operations into the framework of cellular-automata-based neighborhood search to enhance the algorithm's global search capability, and integrated the random restart hill-climbing algorithm based on key operations to improve the algorithms local development capability. Experiments tested 10 flexible job shop scheduling instances and one flexible job shop scheduling instance with fuzzy quality control time to verify the effectiveness of the proposed algorithm and models. In addition, the test results under different failure scenarios show the proposed method outperforms the backward-based scheduling strategy used in practice. [ABSTRACT FROM AUTHOR]

The combination processing of molds and electronic product joint inspection introduce the mandatory concurrent operations on the same machine (MCDSM), which breaks the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) To solve this type of job shop scheduling problem, this paper proposed an adaptive hybrid initialization genetic algorithm (AHIGA). Firstly, this paper defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, it designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of MCOSM. Meanwhile, it established a population hybrid initialization method to generate high-quality feasible solutions. This paper also designed novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, it constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithms global search capability. Finally, it constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results show that the proposed model and algorithm can effectively solve GJSPMCO problem, and the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of RMFS and reduce operating costs, this paper proposed a double-layer planning model for AGV task allocation considering load balancing, following an analysis of key factors causing congestion in RMFS. The upper layer sought to minimize the total cost, while the lower layer addressed the minimization of the system load standard deviation and AGV idle rate through a multi-objective function. It introduced a modified adaptive genetic algorithm(SAGA) to overcome the challenges of low efficiency and susceptibility to local optima in solving task allocation problems using traditional GA. It integrated the sigmoid function was to transform fitness values and participates in the adaptive adjustment of cross-mutation operators. Additionally, it incorporated catastrophic strategies to prevent premature convergence of the algorithm. Finally, it simulated the proposed algorithm on different scale cases, and the results demonstrate that, in comparison to the genetic algorithm, adaptive genetic algorithm, adaptive disaster genetic algorithm, and ant colony algorithm, the improved algorithm exhibits substantial optimization in the experimental results of various scale cases. This suggests that the enhanced algorithm effectively avoids premature convergence, enhances solution quality and convergence stability, actively balances road network load, and reduces total operating costs. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the efficiency of logistics distribution in urban environment, taking delivery time and delivery cost as optimization objectives, a mathematical model of “vehicle-drone”route optimization was established, and a third-order algorithm based on clustering-Floyd-genetic algorithm was proposed. The experimental results show that the algorithm can effectively reduce the computational load through multi-order data processing, and overcome the slow convergence rate of genetic algorithm, easy to fall into the local optimal problem. The sensitivity analysis of drone capacity shows that the delivery capability of drone increases significantly with the increase of load, and the simultaneous increase of load and maximum range can bring the delivery capability of drone into full play. Compared with the vehicle-only mode, the total cost and time of “ vehicle-drone” mode decrease by 36. 1% and 34. 5% respectively. It is proved that the algorithm has some practical value in city logistics distribution. [ABSTRACT FROM AUTHOR]

Traditional machine learning models for groundwater potential prediction do not consider the optimal combination of factors, which can adversely affect groundwater potential mapping. For this reason, a groundwater potential prediction method based on genetic algorithm optimization support vector machine was proposed. Taking Yiliang County of Yunnan Province as the study area, a total of 15 influencing factors were selected from topography, hydrology, soil, geology, etc. Considering the model performance and the roles of the influencing factors, the optimal factor combinations containing 11 influencing factors were screened using the genetic optimization algorithm. Then the groundwater potential prediction model was constructed using the support vector machine method. Finally, the model accuracies and areas under the curves of the receiver operating characteristic (AUCs) before and after the optimization of the factors were calculated, and the receiver operating characteristic (ROC) curves of the model and the groundwater potential prediction maps were plotted. The results show that the accuracy of the model before factor optimization is 0. 774, the AUC of the validation set is 0. 789, the accuracy of the model after factor optimization is 0. 777, and the AUC of the validation set is 0.806, which are increased by 0.003 and 0.017 respectively. It can be seen that the accuracy and reliability of the proposed method are superior to the traditional support vector machine method, and the results can provide scientific reference for regional hydrogeological survey and groundwater resource management and planning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

选取双向DC-DC变换器为研究对象，根据混合储能系统中储能元件和耗能元件的连接拓扑结构确定在不同能量流动方向上的功率需求指标，确定变换器主要元件的参数和合适的电路元件。搭建降压/升压（buck/boost）电路模型，并设计自适应快速终端滑模控制策略，提出改进遗传算法对控制器的参数进行优化。由于不同控制参数的控制效果不同，为了获得更好的电压输出特性，对自适应快速终端滑模控制策略进行参数优化，并通过仿真验证了该方法具有良好的控制性能。

The prediction of subway passengers' walking time during exit is an important basis for urban traffic operation and management. Accurate prediction can help alleviate subway congestion, optimize subway services, and improve passenger satisfaction. Firstly, video analysis software was used to extract the walking time and several characteristic variables of passengers during exit from surveillance videos. Secondly, in order to screen out the factors that have a significant impact on walking time, correlation analysis and optimal scaling regression model were used for factor analysis, and genetic algorithm was used to extract the optimal feature combination. Finally, the extracted features were used as input vectors and the Extreme Gradient Boosting model was used to predict walking time, with mean absolute error as the evaluation index. The experimental results show that the method proposed in this paper has good effect in predicting the behavior of subway passengers during exit, with a mean absolute error of 1. 55 s, lower than the unoptimized Extreme Gradient Boosting model (1. 87 s), support vector machine (2. 03 s) and random forest (1. 96 s) models. [ABSTRACT FROM AUTHOR]

Air-rail intermodal freight transportation offers a promising solution to address the increasing demand for high-value and time-sensitive goods. In the transformation of transport mode, the connecting link was affected by the transport cost and transport efficiency of combined transport goods to a certain extent. Based on the external transfer connection mode of “ all-cargo aircraft + highspeed rail freight train”, a model was constructed for optimizing the connection timetable and ferry scheduling scheme in air-rail intermodal freight transportation to further optimize the air-rail intermodality scheme. To solve this model, an approach based on genetic algorithms was developed. Connection cost and connection time were further analyzed. The model and approach were tested in a realworld case of air-rail freight transportation using the timetable data of Hongqiao Airport T2 Terminal and Shanghai Hongqiao High-speed Railway Station on a specific day. The simulation results show that three ferry cars are needed to transport the goods in the air-rail connection. In this scheme, the workload of each ferry car is reasonably distributed, and the cost of each batch of goods is the smallest. The connection cost accounts for about 20% of the total cost, and the cost advantage of air-rail intermodality in long-distance transportation is more obvious. The efficiency of loading and unloading is the key factor affecting the connection time of air-rail intermodality. It can provide a guidance for the future operation of long-distance air-rail intermodality. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Weaving and embroidering are the craft of weaving knitting or embroidering with cotton linen silk wool and other textile materials. China is the starting point of the Silk Road the birthplace of sericulture silk reeling and silk weaving and the exquisite skills of the weaving and embroidery industry enjoy a high reputation in the world. The patterns of woven and embroidered artifacts present historical cultural and artistic values highlight the formal beauty of plastic arts in traditional Chinese culture and provide rich material resources for cultural and creative design. However the traditional weaving and embroidering design mainly relies on manual pattern extraction which has problems such as high cost complicated steps long cycle and low efficiency. To this end this paper proposed an algorithm based on genetic algorithm to adaptively determine the optimal threshold value of Canny operator to automatically extract the patterns in the images of woven and embroidered artifacts improve the extraction efficiency and effect make the artifacts "live" and promote the inheritance and protection of traditional Chinese culture. To realize the automatic extraction of patterns from images of woven and embroidered artifacts and solve the problem that traditional Canny operators need to manually determine the threshold for edge detection of images of woven and embroidered artifacts and that the extraction efficiency is not high the article took the images of woven and embroidered artifacts as the research object proposing a genetic algorithm to optimize the Canny operator for the extraction of patterns from woven and embroidered artifacts. Firstly the paper analyzed the characteristics of the images of the woven and embroidered artifacts and used bilateral filtering algorithm and mean drift algorithm to smooth the noisy original image for the gray scale of the smoothed graph it selected the genetic algorithm to solve the optimal threshold of Canny operator and input the optimal threshold into Canny operator for the edge detection of the gray scale graph. Then the paper expanded the morphology of the edge image and automatically filtered out noise and incomplete pattern samples based on the area length and width of each connectivity domain. Finally the algorithm was evaluated for the effect of extracting patterns and the algorithm's running time. Experiments show that the algorithm can accurately detect the real edges of the pattern in the image of the woven and embroidered artifacts and the edges detected are clearer and more continuous than those detected by the other five traditional operators. In terms of pattern extraction precision and pixel accuracy PA it is better than the existing methods such as traditional Canny operators hybrid frog jump adaptive Canny operator Otsu maximum optimized Canny operator and so on. The algorithm is robust to the presence of folds that lead to pattern deformation in the woven and embroidered artifacts and all the patterns are successfully extracted. The algorithm can effectively detect the real edges of the patterns in the image of woven and embroidered artifacts and complete the pattern extraction. Woven and embroidered artifacts contain deep cultural heritage and artistic value and through pattern extraction designers can integrate traditional cultural elements into modern design to create unique cultural and creative products. In this paper the algorithm solves the problem that the traditional Canny operators need to determine the threshold value for the edge detection of woven and embroidered artifacts and that the extraction efficiency is not high. In the process of cultural and creative design the digitization of cultural and creative materials is the first step. In addition the grasp and application of the knowledge of history and culture artistic connotations unique stories and symbolic meanings embedded in patterns can more accurately capture the essence of the culture and the emotional expression it represents. For this reason it is of deeper significance and value to carry out research on the integration and mining of multimodal information such as patterns and knowledge in the future. [ABSTRACT FROM AUTHOR]

In recent years, with the development of artificial intelligence, deep learning has become one of the most important computing loads today. The next generation of artificial intelligence(AI)and high-performance computing applications have put unprecedented demands on the computing power and communication capabilities of computing platforms. Wafer-scale chips integrate ultra-high-density transistors and interconnect communication capabilities on the entire wafer, so it is expected to provide revolutionary computing power solutions for future AI and supercomputing platforms. Among them, the huge computing resources and unique new architecture of wafer-scale chips pose unprecedented challenges to task mapping algorithms. Related research has become a major focus of academic research in recent years. This paper focuses on studying the mapping methods of AI tasks on wafer-scale hardware resources. By expressing the AI algorithm as multiple convolutional kernels and considering the computational power characteristics of convolutional kernels, a mapping algorithm for wafer-scale chips is designed based on genetic algorithms. The simulation results under a series of mapping tasks verifies the effectiveness of the mapping algorithm and revealed the impact of parameters such as execution time and adapter cost on the cost function. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Micronanoelectronic Technology is the property of Micronanoelectronic Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of long time and low positioning accuracy of indoor positioning algorithm, an indoor positioning algorithm based on improved LightGBM (light gradient boosting machine) algorithm was proposed. The algorithm first preprocessed the data in the fingerprint database, and removed outliers and outliers through KNN (K-nearest neighborhood ) algorithm to reduce environmental noise interference and improve data reliability. Next, the algorithm divided the sample set into training set and test set, and used LightGBM algorithm to model. At the same time, genetic algorithm was used to adjust the parameters in LightGBM algorithm, and the optimal parameters were found according to the fitness function to obtain the LightGBM + GA (genetic algorithm) coordinate prediction model. Finally, a prediction model was established according to the optimized parameters to realize coordinate prediction. The experimental results show that the algorithm improves the accuracy of WiFi positioning by 0. 1 m compared with XGBoost (extreme gradient boosting,XGBoost) algorithm and 0. 19 m compared with GBDT algorithm. In terms of positioning time, LightGBM + GA algorithm is 5. 10 s faster than GBDT (gradient boosting decision tree) algorithm and 5. 97 s faster than XGBoost algorithm, which has good practicability. [ABSTRACT FROM AUTHOR]

In view of the lack of meteorological visibility data in Anhui Province, meteorological data from four automatic meteorological stations (Mount Huangshan Station, Lingbi Station, Shannan Xigu Station, and Baize Lake Station) under different terrain conditions in Anhui Province were selected from the years 2017 to 2019. The meteorological elements closely related to visibility were first identified using the gray correlation analysis method. Subsequently, a hybrid algorithm of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) was employed to optimize the prediction model of the back propagation (BP) neural network. The visibility of automatic weather stations under four different terrain conditions was predicted, and the prediction results were compared with the RF prediction model and XGBoost prediction model. The results indicate that, under any terrain condition, the GA-PSO-BP neural network prediction model exhibits smaller prediction errors and higher model accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Low-Carbon Chemistry & Chemical Engineering is the property of Low-Carbon Chemistry & Chemical Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the efficiency of domestic wastewater treatment process design, a wastewater treatment process database was established to save data samples abstracted from real domestic wastewater treatment processes, and the process was selected by the support vector machine classification algorithm based on boundary points(BP-SVM). Support vector machine classification algorithm based on boundary points(BP-SVM)was used to select the process. Multiple BP-SVMs were set up to solve the multi-classification problem. Parameter optimization of BP-SVM parameters was performed using genetic algorithm(GA). The results of the study show that the process design algorithm gives a suitable solution, verifies an accuracy of 94%, and consumes less compared to other algorithms. The feasibility and effectiveness of the algorithm is proved. [ABSTRACT FROM AUTHOR]

Slurry concentration has characteristics of hysteresis and time varying in conventional PID control, which is easily affected by the properties of tailings and particle size. Therefore, a multi objective optimization control strategy for slurry concentration is proposed. By analyzing the filling process flow, a multi objective control model for slurry concentration is established according to setting multi objective decision variables and establishing corresponding objective functions and constraints. The slurry concentration is optimized by underflow concentration, filling quality and filling cost, the model was solved by genetic algorithm and simulated in MATLAB. Through the simulation curve, the multi objective optimization algorithm meets the control requirements of qualified quality and low cost in terms of evaluation indicators* Compared with the traditional PID control algorithm, the multi objective optimization control algorithm has faster response speed and stronger robustness* Since the operation of the filling system, the slurry concentration has stabilized at 70%士1%, and the filling cost has decreased by 15%, which meets the requirements of industrial production. [ABSTRACT FROM AUTHOR]

In order to analyze the optimization problem of bus route departure time, two intersecting bus routes with transfers were taken as the research object. Taking into account passenger congestion, a bus timetable optimization model was constructed with the objective function of minimizing the total waiting time, transfer waiting time, and non transfer passenger waiting time. Two city bus routes in Hohhot City, namely Line 4 “JINYU Global Center to Affiliated Hospital of Medical University” section and Line 78 “Hohhot Bus to Saihan District Education Bureau” section, were studied, and the optimization model was solved using genetic algorithm. The results show that the proposed city bus timetable optimization scheme can shorten the transit waiting time and total waiting time by 18.1% and 10.7% respectively, thereby validating the proposed optimization model’s effectiveness in improving the operating efficiency of intersecting routes. [ABSTRACT FROM AUTHOR]

In order to improve the stability of power grid operation, it is necessary to build a stable and reliable power grid dispatching system, and an active-active dispatching system considering the characteristics of parallel systems was proposed. By building two data centers, active and standby. According to the application business volume, adjustments were made between the primary and backup adjustments, achieving full utilization of resources. In the event of a failure, another functioning center will take on all the business. The parallel system was regarded as the virtual operation system of the power grid, and the active-active dispatching system of the power grid was obtained after combining the real and virtual systems, with functions such as management and control module, experiment and evaluation module, and learning and training module. By initializing the data, calculating the power distribution results, establishing a two-layer task scheduling model, and using genetic algorithms to solve the model, the optimization scheduling of the dual active scheduling system can be achieved. The test results show that the method proposed has the advantages of short time, high efficiency, large total amount of grid information dispatching, strong dispatching ability and high security of dispatching information to reach the optimal distribution value. It shows that the designed active-active dispatching system is feasible and beneficial to the optimal dispatching of regional power grid. [ABSTRACT FROM AUTHOR]

The problem of computing first edge server deployment is a fundamental problem in computing first network. In the actual scenario, the computing first edge server is close to computing power resources and provides access services for them to join the computing first network. However, the structure of computing resources is often determined by the actual demand, and changes with the change of demand. Under the constraint of computing first edge server resources, how to reasonably deploy computing first edge servers to ensure the effective construction of computing networks has become a hot topic of concern for all sectors. Firstly, this paper analyzed the deployment problem of computing first edge servers and transformed it into a multi-objective optimization problem with constraints. It proposed an improved genetic algorithm to address this issue. The advantages of this algorithm were as follows. It found non repetitive feasible solutions as the initial population provided more room for selection operations. When selecting, it adopted an individual balanced selection strategy to ensure the diversity and decentralization of the iterative population. When crossing and mutating, it adopted different types of random two point crossing and rotating random single point mutation strategies, thereby ensuring the diversity and diversity of the newborn population. The experiments is verified by resources deviation rate, load error rate, convergence rate. And expectation solution error rate shows that the algorithm is very effective and reasonable. [ABSTRACT FROM AUTHOR]

To address the issue of resource wastage and increasing latencies in the placement process of edge servers, this paper analyzed and modeled the user density and average access time of edge server placement schemes as multi-objective optimization problems. It designed an edge server placement scheme based on user density and average access time, and proposed a multi-objective sea-horse genetic algorithm (MOSGA) to solve this problem. The MOSGA algorithm firstly used the idea of multi-objective optimization algorithm to improve the SHO algorithm, so that the SHO algorithm could be applied to multi-objective optimization problems. It used the genetic algorithm to improve the propagation operation of the SHO algorithm, so that the MOSGA could better jump out of the local optimal solution and accelerate the solution of the problem. The proposed algorithm was verified on the data set of Shanghai Telecom, and the simulation experiment results show that MOSGA is obviously better than RA, K-means, NSGA and LMM, which not only effectively solves the problem of server resource waste, but also greatly reduces the time of terminal equipment to access the server. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)