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Monitoring method for gasification process instability using BEE-RBFNN pattern recognition

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2021年第29期46卷 16202-16216页

作者： Zhang, Jinchun Zhang, Zichuan Hou, Jinxiu Henan Polytech Univ Sch Energy Sci & Engn Jiaozuo 454000 Henan Peoples R China Henan Polytech Univ Sch Safety Sci & Engn State Key Lab Cultivat Base Gas Geol & Gas Contro Jiaozuo 454000 Henan Peoples R China

To identify large fluctuations of parameters in the process of gasification, a control chart pattern recognition method based on optimized radial basis function neural network (RBFNN) is proposed in this paper to conduct pattern recognition of the parameters of gasification process. The proposed method is described in four aspects, feature description, feature extraction, classifier and algorithm learning and training. The initial data is described from shape features and statistical features to reduce the dimension of the data. The optimal characteristic set is selected by the association rule mining algorithm to reduce the complexity of the model. The performance of neural network is affected by the learning method. With RBFNN as a classifier, a learning method based on bees algorithm is therefore put forward and then training recognition is compared between the proposed method and the traditional method. The results show that the proposed method has higher recognition rate and simpler structure than the traditional method, and it is very effective in monitoring and identifying abnormal fluctuations of gasification process parameters. (C) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Gasification process Instability monitoring Pattern recognition Radial basis neural network bees algorithm

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Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第6期11卷 180-192页

作者： Liang, Yuchen Wang, Yuqi Gu, Chengyi Tang, Jie Pang, Xianjuan Jiangsu Univ Sch Mech Engn 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn 1040 Heping Ave Wuhan 430070 Hubei Peoples R China Jiangsu Haiyu Machinery Co Ltd 8 Yingbin Rd Taizhou 225714 Jiangsu Peoples R China Henan Univ Sci & Technol Natl United Engn Lab Adv Bearing Tribol 263 Kaiyuan Ave Luoyang 471000 Henan Peoples R China

Predicting cutting tool remaining life is important to sustainable machining. Accurate wear assessment improves efficiency, reduces waste , and lowers costs by minimizing tool failure . Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilizing multi-source data to address these challenges effectively. The key innovations include the following: (1) the proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardized data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life;best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite element analysis is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimization mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64%, and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in improving the operational efficiency and reliability of manufacturing systems is validated through the integration of advanced data analytics and deep learning techniques.

关键词： fog computing adaptive prognosis cutting tool life prediction multi-source data fusion bees algorithm

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Optimization of the geometrical parameters for elevated temperature hydro-mechanical deep drawing process of 2024 aluminum alloy

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2021年第2期235卷 151-161页

作者： Yaghoubi, S. Fereshteh-Saniee, F. Bu Ali Sina Univ Fac Engn Dept Mech Engn Hamadan 65178 Hamadan Iran

This research is concerned with the effects of the geometrical parameters of the die in elevated temperature Hydro-Mechanical Deep Drawing (HMDD) process of 2024 aluminum alloy. A Group Method of Data Handling (GMDH) process was used to train a neural network in order to study the process behavior. Based on the maximum reduction in sheet thickness and the uniformity of the final product, an objective function was constructed. The bees algorithm (BA) was used to achieve the optimal values for process variables. To verify the simulation results, they were compared with the experimental findings gained via this research and an appropriate correlation was observed between these results. This comparison showed that, by optimization of the geometrical parameters of the process, the value of the combined objective function was the best one compared with all of the cases tried in the present investigation.

关键词： Hydro-mechanical deep drawing (HMDD) maximum reduction in thickness thickness variation GMDH neural network bees algorithm

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Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 7323-7336页

作者： Bilgic, Hasan Huseyin Sen, Muhammed Arif Yapici, Ahmet Yavuz, Hakan Kalyoncu, Mete Iskenderun Tech Univ Fac Engn & Nat Sci Dept Mech Engn TR-31200 Iskenderun Hatay Turkey Konya Tech Univ Dept Mech Engn Konya Turkey Cukurova Univ Dept Mech Engn Adana Turkey

In this paper, meta-heuristic tuning of LQR weighting matrices using various objective functions on the experimental flexible arm under the effects of disturbance is investigated. The use of flexible and lightweight systems provides certain advantages such as high operating speeds, low electricity consumption, and low initial investment costs. However, such systems are more prone to vibration-related problems than their rigid and heavyweight counterparts. One of the closed-loop control methods used to suppress these vibrations is LQR control, but the success of the controller depends on the choice of the gain and regulator matrices. In this study, the bees algorithm, a meta-heuristic search algorithm, is used to determine LQR matrices. The system performance is compared with similar studies in the literature. The proposed objective function reveals an improvement of 3.1% in rotor maximum overshoot compared to studies in the literature. Flexible link maximum overshoot has been reduced from 17.8 to 7.1%. In order to validate the applicability and repeatability of the proposed method under the noise and disturbance in real systems, experimental verification tests were conducted using the Quanser Flexible Link system. As a result, the proposed method has been experimentally validated and it is estimated that it may well be a very useful controller design approach in various engineering systems and related control system developments.

关键词： bees algorithm LQR Optimization Noise rejection Disturbance rejection Flexible link

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Metaheuristic-Based Hyperparameter Tuning for Pretrained Deep Learning Model: Application to the Skin Cancer Identification

Metaheuristic-Based Hyperparameter Tuning for Pretrained Dee...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedaa Almansour Dheeb Albashish Shahnorbanun Sahran Azizi Abdullah Mohammad Faidzul Nasruddin Xu Xinlan Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Computer Science Department Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Computer-aided automated diagnosis is necessary due to the multifaceted nature of skin problems, the scarcity and uneven distribution of qualified dermatologists, and the requirements for timely and accurate diagnosis. Numerous pretrained deep learning models have been introduced to deal with such tasks, but most of them are suffering from hyperparameter tuning. This study aims to utilize metaheuristic algorithms (i.e., Genetic algorithm (GA) and bees algorithm) to adjust the hyperparameters of the pretrained (i.e., MobileNetV2) deep learning model to solve the identification task in skin medical images. The proposed models have been evaluated on the publicly available Kaggle dataset for eight skin diseases. The proposed MobileNetV2+bees algorithm achieved around 92% accuracy, while the MobileNetV2+GA algorithm achieved 93% accuracy. The obtained results outperformed most of the recent studies on the same dataset. The obtained MobileNetV2+GA model reveals the efficiency of identification and discrimination between eight different classes of skin cancer, which aids pathologists in accurately diagnosing skin diseases.

关键词： Deep learning bees algorithm Accuracy Computational modeling Metaheuristics Skin Tuning Diseases Skin cancer Genetic algorithms

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Optimization and scheduling of electric vehicle microgrids based on improved bee colony algorithm

Optimization and scheduling of electric vehicle microgrids b...

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International Conference on Power Electronics and Motion Control (IPEMC)

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

The large-scale integration of electric vehicles (EVs) and renewable energy generation systems into the power grid poses significant challenges to the security and stability of existing power systems. This paper explores the optimal scheduling problem of microgrids incorporating EVs and distributed energy sources, including wind, photovoltaic, and coal-fired units. The study also considers the grid load regulation function of electric vehicles based on Vehicle-to-Grid (V2G) technology. A scheduled strategy based on State of Charge (SOC) detection of electric vehicles is proposed, taking into account the operating costs of generating units, the costs associated with wind and solar energy curtailment, and the charging and discharging costs of electric vehicles. A microgrid optimization scheduling model is established with the objective of minimizing the total operating costs within the scheduling period. An improved bee algorithm, combining the bee algorithm with the artificial bee colony algorithm, is employed to solve the model. Comparisons with results from unscheduled charging demonstrate the effectiveness and superiority of the proposed scheduling model.

关键词： bees algorithm Wind energy generation Vehicle-to-grid Costs Wind energy Optimal scheduling Microgrids

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Drug Recommendation based on drug details and Optimization using Bee algorithm

Drug Recommendation based on drug details and Optimization u...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Kornpong Ariyachaipong Pattayapon Senaluang Pokpong Songmuang Rachada Kongkachandra Data Science and Innovation College of Interdisciplinary Studies Thammasat Universtiy Thailand Department of Computer Science Thammasat Universtiy Bangkok Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat Universtiy Bangkok Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aims to address the challenge of medication selection in resource-constrained settings by providing physicians with informed recommendations tailored to patient *** proposed system utilizes drug data sourced from ***, encompassing approximately 34,284 drug entries. To ensure high-quality recommendations, the data undergoes a rigorous preprocessing phase involving null value imputation and feature extraction using techniques like TF-IDF. Following preprocessing, a combination of Drug Recommendation with cosine similarity and the Bee algorithm is employed. Cosine similarity establishes a baseline for drug similarity, while the Bee algorithm optimizes the selection process by considering additional factors beyond just similarity, such as potential side effects and cost-effectiveness.

关键词： Drugs bees algorithm Technological innovation Null value Medical services Machine learning Feature extraction

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Feature Selection in Phishing Attack Detection Using Bee algorithm and Logistic Regression Method

Feature Selection in Phishing Attack Detection Using Bee Alg...

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Technology and Energy Management (ICTEM), International Conference on

作者： Nazila Razzaghi-Asl Jafar Tanha Soodabeh Imanzadeh Negin Samadi Sahar Hassanzadeh Mostafaei Sayyad Nojavan Computer and Electrical Engineering Department University of Tabriz Tabriz Iran Department of Electrical Engineering University of Bonab Bonab Iran

ISBN: (数字)9798350329797

ISBN: (纸本)9798350329803

Phishing attacks are one of the most damaging attacks for internet users. Detecting these attacks is one of the main challenges in the internet security due to their lack of unpredictable nature. Machine learning techniques are suitable methods to detect these attacks. The accuracy of these methods is highly dependent on the features of the data. This paper proposes a hybrid feature selection approach based on Bee algorithm and Logistic Regression method, which detects phishing attacks accurately by selecting an appropriate feature subset. The results show that the proposed approach has been able to increase the detection accuracy of phishing attacks by 1.23%, using a random forest algorithm, in comparison to previous works with 6.66% less features.

关键词： bees algorithm Logistic regression Accuracy Phishing Internet security Forestry Feature extraction

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Two-Stage Multi-Robot Task Allocation algorithms in Local Communication Scenarios

Two-Stage Multi-Robot Task Allocation Algorithms in Local Co...

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International Conference on Control and Automation (ICCA)

作者： Shilei Shan ZhiHong Peng XianLin Zeng School of Automation Beijing Institute of Technology Beijing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In robot emergency rescue scenarios, it is common for communication between robots to be restricted, allowing interaction only within localized communication ranges. However, commonly proposed task allocation algorithms with weak communication models often focus on communication quality while neglecting communication distance. Consequently, this paper introduces a Bernoulli communication model incorporating distance information as the communication model. Subsequently, a two-stage distributed task allocation algorithm is proposed based on this communication model. In the convergence stage, each robot utilizes the K-means algorithm to determine the target task group and employs a distributed bee algorithm to select the target task. In the dispersion stage, robots use an improved distributed genetic algorithm to allocate remaining tasks, exchanging optimal solutions with other robots, thereby achieving conflict-free autonomous task allocation. Finally, real-time simulations are conducted to validate that this algorithm effectively resolves the multi-robot task allocation problem within the localized communication model.

关键词： bees algorithm Automation Real-time systems Resource management Task analysis Robots Dispersion

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Anomaly Detection in Wireless Sensor Networks: A Comparative Analysis of Performance Metrics and Model Accuracy

Anomaly Detection in Wireless Sensor Networks: A Comparative...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Tripti Sharma Priya Dalal IT Department MSIT Janakpuri Delhi India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This research paper explores hybrid anomaly detection frameworks combining Particle Swarm Optimization (PSO), Bee algorithm (ABC), and Genetic algorithm (GA) using the CICIDS2017 dataset for Wireless Sensor Networks (WSNs). It evaluates their effectiveness across diverse network conditions, focusing on anomaly detection accuracy, false positives, and computational efficiency. The study aims to advance anomaly detection methodologies in WSNs by contributing insights into bio-inspired algorithms' impact on anomaly detection. It targets the research community, industry professionals, and scholars, emphasizing the documentation of methodologies and suggesting future research on hybridization techniques, dynamic adaptation, edge computing integration, and machine learning fusion for enhanced anomaly detection in WSNs, fostering improved network security and adaptability.

关键词： bees algorithm Wireless sensor networks Machine learning algorithms Accuracy Heuristic algorithms Machine learning Computational efficiency Anomaly detection Genetic algorithms Edge computing

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