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Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

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IEEE ACCESS 2021年 9卷 116453-116474页

作者： Khamies, Mohamed Magdy, Gaber Kamel, Salah Khan, Baseem Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Aswan Univ Fac Energy Engn Dept Elect Engn Aswan 81528 Egypt Hawassa Univ Dept Elect & Comp Engn Hawassa 1530 Ethiopia

This work presents a new robust control technique which combines a model predictive control (MPC) and linear quadratic gaussian (LQG) approach to support the frequency stability of modern power systems. Moreover, the constraints of the proposed robust controller (MPC-LQG) are fine-tuned based on a new technique titled chimp optimization algorithm (ChOA). The effectiveness of the proposed robust controller is tested and verified through a multi-area power system (i.e., single-area and two-area power systems). Each area contains a thermal power plant as a conventional generation source considering physical constraints (i.e. generation rate constraint, and governor dead band) in addition to a wind power plant as a renewable resource. The superiority of the proposed robust controller is confirmed by contrasting its performance to that of other controllers which were used in load frequency control studies (e.g., conventional integral and MPC). Also, the ChOA's ingenuity is verified over several other powerful optimization techniques;particle swarm optimization, gray wolf optimization, and ant lion optimizer). The simulation outcomes reveal the effectiveness as well as the robustness of the proposed MPC-LQG controller based on the ChOA under different operating conditions considering different load disturbances and several penetration levels of the wind power.

关键词： Power system stability optimization Stability analysis Frequency control Photonic crystals Magnetic materials Power grids Frequency control (LFC) model predictive control (MPC) linear quadratic gaussian (LQG) wind energy chimp optimization algorithm

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An improved H infinity controller using hybrid optimization for PV integrated MMC-HVDC system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 3917-3943页

作者： Ali, Qazi Waqar Inst Appl Technol Abu Dhabi U Arab Emirates

Generally, circulating current is generated in a Modular Multilevel Converter (MMC) by fluctuations in the capacitor voltage of sub-modules. Therefore, this research work seeks to build the Photo Voltaic (PV) integrated MMC-High-Voltage Direct Current (HVDC) systems with optimal H-infinity controller which is appropriate for 3-phase system. To verify the unstable voltages of sub-modules, the reference current is initially set to zero, as well as the unstable currents in each phase are monitored with a current sensor as well as contrasted to the reference current, resulting in the generation of an incorrect circulating current. The main contribution is to reduce the error among the reference current as well as the actual circulating current. In the H-infinity controller, the gains are optimally tuned via a new hybrid algorithm referred as Jaya optimization Insisted Explored chimp (JOI-EC) algorithm, which is the hybridized version of the Jaya optimization algorithm (JOA) and the chimp optimization algorithm (ChOA). Moreover, the performance of the proposed model is examined through control analysis, converter performance analysis, as well as capacitor voltage analysis in addition to the circulating current. The result of simulation shows the robust performance of the proposed controller with respect to minimum circulating current, voltage, and stability analysis.

关键词： MMC H-infinity controller Jaya optimization insisted explored chimp Circulating current Jaya optimization algorithm chimp optimization algorithm

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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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An effective optimized deep learning for emotion classification from EEG signals

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第4期17卷 1631-1642页

作者： Lokesh, Sittiahgari Reddy, Tadipathri Sreenivasulu Sri Venkatewara Univ SV Univ Coll Engn Dept Elect & Commun Engn Tirupati 517502 Andhra Pradesh India Sri Venkatewara Univ Dept Elect & Commun Engn Tirupati 517502 Andhra Pradesh India

Electroencephalography (EEG) signals can be used for emotion recognition (ER), which is an effective method for determining someone's mental state. However, because an EEG signal is non-stationary, the ER is a fascinating challenge. Additionally, the categorization of the mood patterns in the EEG signal makes use of signal processing techniques to extract pertinent data from EEG signals. As a result, the fractional chimp optimization algorithm (FrChOA), which was developed for this work, is introduced as an improved deep learning technique for choosing the best channel and classifying emotions from EEG signals. By merging the chimp optimization algorithm (CA) with fractional calculus, the created FrChOA is modeled (ChOA). Pre-processing, optimal channel selection, feature extraction, and human emotion categorization are the processing stages carried out in this instance by the ER. First, the low pass filtering technique is used to pre-process the incoming EEG signal. The best channel is then chosen using a developed algorithm called FrChOA, which bases its choice on classification accuracy. In order to increase classification performance, the essential features are extracted at the end of the feature extraction procedure. Additionally, the deep neuro-fuzzy network, whose training process is created FrChOA, is used for emotion classification. The developed algorithm also produced the best results, as evidenced by its testing accuracy, sensitivity, and specificity of 0.8848, 0.8763, and 0.8946, respectively.

关键词： chimp optimization algorithm Fractional calculus Low pass filtering Deep neuro-fuzzy network Channel selection

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An efficient vector-borne disease prediction using SS optimization-based hybrid support vector random forest model

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 3943-3952页

作者： Munirathinam, Babu Aroulanandam, Vijay Vasanth Saravanan, Prabakeran Rajalakshmi Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol Fac Engn & Technol Dept Networking & Commun Kattankulathur 603203 Tamil Nadu India

The transmission of infections caused by infected species or arthropoda, such as ticks, blackflies, sandflies, mosquitoes, and triatomine bugs, is known as vector-borne diseases. Arthropod vector is responsible in transmitting the most harmful illnesses that affect people as well as animals. This results in a significant impact on human health, leading to increased mortality rates and various side effects, ultimately reducing the life expectancy of people. This article proposes a novel approach to predict vector-borne diseases using medical data. The approach combines the Sine Cosine as well as Spotted Hyena-based chimp optimization algorithm (SSC) as well as hybrid Support Vector Machine-based Random Forest (SVM-RF) approach. The SSC algorithm is designed by incorporating three different algorithms, namely the chimp optimization algorithm, the Spotted Hyena Optimizer algorithm, and the Sine Cosine algorithm (SCA). The proposed hybrid SVM-RF classifier approach accurately detects vector-borne diseases. Using the vector-borne dataset, the proposed SSC-optimized hybrid SVM-RF approach outperformed other approaches with values of 92, 93.25, 92.53, and 91.52%, respectively. Overall, the proposed approach has significant potential in predicting and diagnosing vector-borne diseases, which can ultimately lead to improved public health outcomes.

关键词： Vector-borne disease prediction Hyena optimization algorithm chimp optimization algorithm Random Forest Approach and Support Vector Machine

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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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