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RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第12期238卷 6007-6017页

作者： Yu, Jun Zhao, Kun Guo, Zhenyu Deng, Sier Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Harbin Peoples R China State Key Lab Proc Automat Min & Met Beijing Peoples R China Cixing Grp Co Ltd Ningbo Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol 52 Xuefu Rd Harbin 150080 Peoples R China

To accurately evaluate the remaining life (RUL) of rolling bearings under small sample conditions and strong noise interference, a RUL prediction scheme using adaptive variational mode decomposition (VMD) and double-discriminator conditional CycleGAN (DD-cCycleGAN) is put forward. Combining chimp optimization algorithm (ChOA) with VMD, an adaptive VMD algorithm based on ChOA is presented, which selects effective mode components for reconstruction and reduces interference from strong background noise. A DD-cCycleGAN is developed to generate new samples which not only retain sample information of source domain, but also resemble samples of target one. A LSTM network after training is utilized to predict the bearing RUL in test samples. The performance of this scheme was validated by using the XJTU-SY bearing test dataset. The comparison analyses demonstrate this scheme has strong noise resistance and high accuracy.

关键词： Rolling bearing RUL prediction adaptive variational mode decomposition double-discriminator conditional CycleGAN chimp optimization algorithm

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Deep anomaly detection: A linear one-class SVM approach for high-dimensional and large-scale data

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Suresh, K. Velmurugan, K. Jayasakthi Vidhya, R. Sudha, S. Rahini Kavitha, V SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Comp Sci & Engn Chennai Tamil Nadu India Panimalar Engn Coll Comp Sci & Business Syst Chennai Tamil Nadu India

Anomaly detection plays a crucial role in various fields including cyber security, finance, healthcare, and industrial monitoring. Traditional methods in anomaly detection often face several challenges such as scalability, adaptability, and difficulty in handling high-dimensional data. So a novel Recurrent Extreme Learning based -Boosted chimp (REL-BC) algorithm is proposed for anomaly detection. The REL-BC model involves a data preprocessing phase and an anomaly detection phase. The data pre-processing phase involves three stages namely one-hot-encoding, outlier disposal, and min-max normalization. In this study, a Recurrent Neural Network is utilized to seize the temporal dependencies and traffic data in the network. Also, the Extreme Learning Machine (ELM) is employed in distinguishing normal as well as anomalous patterns. Further chimp optimization is employed for optimizing hyperparametersto improve the efficiency of the REL-BC approach. The outcome of the experimentation revealed that it demonstrated the improvement of performance for the REL-BC method in detecting anomalies based on various measures.

关键词： Anomaly detection Cyber security Recurrent neural network Extreme learning machine chimp optimization algorithm

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Low-cost and flexible generation expansion planning with localization of power plants in a test bus system

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2024年第2期47卷 226-242页

作者： Thangam, E. Manikandan, B., V Kannan, S. Raja, S. P. Ramco Inst Technol Dept Elect & Elect Engn Rajapalayam 626117 Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India

Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a chimp optimization algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.

关键词： Kuo, Cheng-Chien Power generation planning chimp optimization algorithm power system simulation power system interconnection load flow

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A novel three-factor authentication and optimal mapreduce frameworks for secure medical big data transmission over the cloud with shaxecc

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 68363-68391页

作者： Rajeshkumar, K. Dhanasekaran, S. Vasudevan, V. Kalasalingam Acad Res & Educ Krishnankoil 626126 India Koneru Lakshmaiah Educ Fdn Green Fileds Vaddeswaram 522302 Andhra Pradesh India

Big Data (BD) is a concept that deals with enormous amounts of data storage, processing, and analysis. With the exponential advancement in the evolution of cloud computing domains in healthcare (HC), the security and confidentiality of medical records have evolved into a primary consideration for HC services and applications. There needs to be more than the present-day cryptosystems to address these troubles. Therefore, this paper introduces a novel Three-Factor Authentication (3FA) and optimal Map-Reduce (MR) framework for secure BD transmission over the cloud with Secure Hashing Authentication XOR-ed Elliptical Curve Cryptography (SHAXECC). The authentication procedure is initially carried out with the SHA-512 algorithm, which protects the network from unauthorized access. Next, data deduplication is done using the SHA-512 algorithm to eliminate duplicate files. After that, an optimal MR design is introduced to handle a large amount of BD. In an optimal MR, the mapper uses the Modified Fuzzy C-means (MFCM) clustering approach to initially form the BD clusters. Then, the reducer uses the Levy Flight and Scoring Mutation-based chimp optimization algorithm (LSCOA) to form final BD clusters. Finally, the SHAXECC is used to transmit the data securely. Experiments are performed to compare the superiority of the proposed technique with the existing techniques in terms of some performance measures. The proposed approach outperformed other existing models concerning clustering and security measures. So, the proposed model is the best for data protection and privacy in cloud-enabled HC data.

关键词： Medical big data Big data security Trusted center Map reduce Data deduplication Hadoop distributed file system Fuzzy C-Means chimp optimization algorithm

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Enhancing precision in table tennis officiating through an optimized autonomous sensor node system

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ENGINEERING COMPUTATIONS 2024年第8/9期41卷 2134-2168页

作者： Sun, Chenyang Khishe, Mohammad Dalian Maritime Univ Phys Educ Dept Dalian Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

PurposeThe purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor ***/methodology/approachThe study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the *** optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which

关键词： optimization Reinforcement learning Table tennis chimp optimization algorithm Sensors

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Optimized clustering routing framework to maintain the optimal energy status in the wsn mobile cloud environment

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第3期83卷 7919-7938页

作者： Vellela, Sai Srinivas Balamanigandan, R. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 600124 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Artific Intelligence Chennai 600124 Tamil Nadu India

Nowadays, energy-efficient data transmission is one of the biggest challenges in Wireless Sensor Networks (WSNs). Therefore, various routing protocols were developed to minimize energy consumption to find the shortest path in the WSN. However, they face issues due to delays and energy consumption. Thus, this article has developed a novel hybrid chimp-based Clustering Flat Routing Protocol (CbCFRP) to overcome these demerits. The chimp fitness function in the designed model finds the optimal route for data transmission. Thus the energy consumption and delay are minimized. The clustering protocol helps to find the shortest route by determining the possible routes. The presented model is designed and validated in the MATLAB environment. Furthermore, the results of the developed model are estimated in terms of throughput, delay, packet drop, and delivery rate. Moreover, the robustness of the technique is verified with a comparative analysis. The comparative analysis confirms that the developed model earned better results than the existing approaches.

关键词： chimp optimization algorithm Shortest path estimation Clustering route protocol Optimal routing Wireless sensor networks

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A Deep Belief Network Model for Automatic Atrial Fibrillation Detection

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IETE JOURNAL OF RESEARCH 2024年第2期70卷 1303-1315页

作者： Suresh, P. Reka, S. Sofana Vellore Inst Technol Sch Elect Engn Chennai India Vellore Inst Technol Ctr Smart Grid Technol Sch Elect Engn Chennai India

Atrial fibrillation is an abnormal heart rhythm categorized by the rapid and irregular pulse of the atrial chambers in the heart. The Electrocardiogram is normally used for the analysis of Atrial Fibrillation which is used to check a patient's rhythm and electrical activity. The diagnosing method involved in this process is used to classify the electrocardiogram signal as normal or atrial fibrillation. In this analysis, Adaptive Dual Threshold Filter and Discrete Wavelet Transform methods are utilized to remove noises present in ECG signals representing Electromyogram, Power line interference, Baseline wandering, White Gaussian Noise, electrode motion artifact, and muscle contraction noises. In this process, a combination of heart rate variability and atrial activity is used to do the feature extraction. This work proposes a chimp optimization algorithm-based parameter-optimized Deep Belief classifier for effective Atrial Fibrillation detection. To validate the results comparison analysis is done with the three methods, Random Forest Classifier, Logistic Regression, and Decision Tree Classifier, to the check the outcomes of the proposed model. The simulation outcomes demonstrate that the accuracy of the proposed method achieves 10.81%, 10.81%, and 1.86% better than the existing methods.

关键词： Atrial activity Atrial fibrillation chimp optimization algorithm Deep belief classifier Electrocardiogram Heart rate variability

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Leveraging quantum-inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems

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EXPERT SYSTEMS 2024年第8期41卷 e13563-e13563页

作者： Zhang, Lin Alsubai, Shtwai Alqahtani, Abdullah Alanazi, Abed Abualigah, Laith Wuhu Inst Technol Coll Network Engn Wuhu Anhui Peoples R China Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci Al Kharj Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Software Engn Dept Al Kharj Saudi Arabia Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Middle East Univ MEU Res Unit Amman Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Isra Univ Fac Informat Technol Amman Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya Malaysia Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Yuan Ze Univ Coll Engn Taoyuan Taiwan

Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum-behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN-based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best-modified versions of CHOA, the quantum-behaved whale optimization algorithm (QWOA), and the quantum-behaved grey wolf optimizer (QGWO) as the two best quantum-behaved optimizers as well as classic CHOA. The most effective deep learning-based predictors for forecasting the profit, ranked from highest to lowest, are DNN-QCHOA, DNN-NCHOA, DNN-QWOA, DNN-QGWO, DNN-ULCHOA, DNN-CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN-CHOA is shown to be the most accurate model.

关键词： chimp optimization algorithm deep learning financial profit prediction quantum-behaved

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Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

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SOLAR ENERGY 2021年 222卷 1-17页

作者： Zayed, Mohamed E. Zhao, Jun Li, Wenjia Elsheikh, Ammar H. Abd Elaziz, Mohamed Yousri, Dalia Zhong, Shengyuan Mingxi, Zhu Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ene MOE Tianjin 300350 Peoples R China Tanta Univ Dept Power Mech Engn Fac Engn Tanta 31521 Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Fayoum Univ Dept Elect Engn Fac Engn Al Fayyum Egypt STATE GRID Int Dev Co Ltd West Changan St Beijing 100031 Peoples R China

Hybrid artificial intelligence models have become promising tools for soft computing and computational intelligence, as they can deal with complicated sustainable systems such as the prediction modeling of concentrated power systems. In these models, one or two artificial intelligence techniques are integrated with an optimization algorithm to develop a fine-tuned prediction modeling. In this paper, we develop a novel hybrid prediction model using an improved version of the Random Vector Functional Link (RVFL) network to predict the instantaneous output power and the monthly power production of a solar dish/Stirling power plant (SDSPP). A new metaheuristic algorithm called chimp optimization algorithm (CHOA) has been combined with the RVFL network to effectively determine the optimal values of RVFL parameters. More so, the proposed RVFL-CHOA model is compared with four artificial-based models include the original RVFL, and three hybrid modified versions of the RVFL model using the Particle Swarm optimization (PSO), Spherical Search optimization (SSO), and Whale optimization algorithm (WOA). The prediction performance of the five models was compared using various statistical evaluation metrics. The statistical results prove the superiority and effectiveness of the proposed RFVL-CHOA method among the other investigated optimized models for performance prediction of the SDSPP. Based on the test data, the REVL-CHOA predicts the instantaneous output power and the monthly power production of the SDSPP with determination coefficient values of 0.9992, and 0.9108, and root mean square error values of about 0.00047, and 0.05995, respectively.

关键词： Solar dish Stirling power plant Random Vector Functional Link chimp optimization algorithm Spherical search optimization Whale optimization algorithm Power production prediction

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Vibration Modeling And Simulation Analysis Of Wind Turbine Flexible Tower Based On ChOA algorithm optimization

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第1期28卷 109-118页

作者： Yin, Xiao Ju Mu, Qi Zheng Zhou, Li Li, Bo Shao, Guo Ce Du, Zhi Liang Shenyang Inst Engn Dept Renewable Energy Shenyang 110136 Peoples R China CGN New Energy Investment Shenzhen Co Ltd Liaoning Branch Shenyang 110623 Peoples R China Shenyang Inst Engn Coll Informat Shenyang 110136 Peoples R China

The vibration of flexible towers of wind turbines can cause accidents or even the shutdown of wind turbines from time to time. Establishing a tower vibration prediction model can effectively predict the occurrence of accidents. Aiming at the problems of low prediction accuracy of the BP neural network and easy to fall into the local optimal solution, the BP neural network is optimized using the chimp optimization algorithm (ChOA). To confirm the algorithm's feasibility, the 120m flexible tower data of a 2 MW wind turbine in a wind farm is simulated and analyzed, and the tower vibration prediction model is used to establish by optimizing the heterogeneous data from multiple sources through correlation analysis under different operating conditions of the wind turbine to find out the correlation variables affecting the vibration of the flexible tower. The results show that the ChOABP neural network has the best prediction effect under the rated wind speed, the root mean square error (RMSE) decreases by 12.1267, and the mean absolute error (MAE) decreases by 9.688, and the error-index decreases by more than the rated wind speed, which proves that the algorithm is better than the optimized BP neural network in rated wind speed.

关键词： Wind turbine Tower chimp optimization algorithm BP neural network Predictive modeling

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