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A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure

ISA TRANSACTIONS 2024年 147卷 304-327页

作者： Chen, Rongxing Xiao, Xinping Gao, Mingyun Ding, Qi Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Cent China Normal Univ Sch Informat Management Wuhan 430079 Peoples R China Nanjing Univ Sch Business Nanjing 210008 Peoples R China

The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure o

关键词： Mixed frequency sampling grey model Mixed data sampling chimp optimization algorithm Hard disk drive failure forecasting

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Deep learning-based hybrid sentiment analysis with feature selection using optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第28期82卷 43273-43296页

作者： Daniel, D. Anand Joseph Meena, M. Janaki VIT Chennai Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

In the past few years, sentiment analysis (SA) of online content has gained more attention in the research area due to the enormous increase of online content from various sources like websites, social blogs, etc. Many organizations use SA techniques to determine the opinion of users and to ensure their satisfaction. Numerous techniques are suggested by many researchers to identify the sentiments of online content. Among them, hybrid of deep learning and lexicon-based SA techniques are gaining more attention due to their outstanding performance than other approaches. Though the lexicon-based SA approaches integrated with deep learning SA approaches possess more advantages they suffer from lack of accuracy and scalability issues due to the high-dimensional features. To eliminate this issue, a hybrid SA approach is proposed in this paper with a bio-inspired feature selection technique. The Valence Aware Dictionary for Sentiment Reasoning (VADER) approach is integrated with the hybrid deep learning approach of attention-based bidirectional long short-term memory and variable pooling convolutional neural network (VPCNN-ABiLSTM) for SA. The optimal features are selected to minimize the scalability issue by integrating the chimp optimization algorithm with the opposition-based learning technique. The performance of the proposed approach is evaluated for four types of benchmark datasets in terms of precision, accuracy, recall, and F1 score. The proposed approach with OBL-CHOA based feature selection technique achieved higher accuracy of 97.1% with the reduction of 13.6% features. The accuracy of the proposed approach with the feature selection technique is 6.9% higher than the existing BiLSTM-CNN based SA approach.

关键词： Sentiment analysis VADER Convolutional neural network Deep learning Long short-term memory chimp optimization algorithm

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Micro-Calcification Classification Analysis in Mammogram Images with Aid of Hybrid Technique Analysis

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期128卷 1287-1307页

作者： Shanker, M. C. Vadivel, M. Sathyabama Inst Sci & Technol Dept Elect Engn Chennai Tamil Nadu India Vidya Jyothi Inst Technol Dept Elect & Commun Engn Hydrabad Andra Pradesh India

Breast cancer is the leading cause of death in women. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and chimp optimization algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).

关键词： Breast cancer classification Deep belief neural network Pre-processing Feature extraction Deep learning method chimp optimization algorithm

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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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