Journal of Intelligent & Fuzzy Systems - Volume 44, issue 2

Authors: Yao, Linjie | Zhang, Guidong | Sheng, Yuhong

Article Type: Research Article

Abstract: Multi-dimensional uncertain differential equations (MUDEs) are often used to describe complex systems that vary with time. In this paper, the generalized moment estimation method is employed to estimate the MUDEs’ unknown parameters. A method to optimize parameters with multiple estimation results is proposed. The hypothesis test and α-path are proposed to verify the feasibility of the parameter results. Several examples of parameter estimation for MUDEs are given, as well as two numerical examples to verify the feasibility of the method.

Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, generalized moment estimation, parameter estimation

DOI: 10.3233/JIFS-213503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2427-2439, 2023

Authors: Sathish, S. | Kavitha, K. | Poongodi, J.

Article Type: Research Article

Abstract: The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing …algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. Show more

Keywords: Software defined networking, Dantzig Wolfe Decomposition algorithm, industrial internet of things networks, multi-constrained shortest path problem, centralized route optimization

DOI: 10.3233/JIFS-221776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2441-2454, 2023

Authors: Akalya devi, C. | Karthika Renuka, D. | Pooventhiran, G. | Harish, D. | Yadav, Shweta | Thirunarayan, Krishnaprasad

Article Type: Research Article

Abstract: Emotional AI is the next era of AI to play a major role in various fields such as entertainment, health care, self-paced online education, etc., considering clues from multiple sources. In this work, we propose a multimodal emotion recognition system extracting information from speech, motion capture, and text data. The main aim of this research is to improve the unimodal architectures to outperform the state-of-the-arts and combine them together to build a robust multi-modal fusion architecture. We developed 1D and 2D CNN-LSTM time-distributed models for speech, a hybrid CNN-LSTM model for motion capture data, and a BERT-based model for text …data to achieve state-of-the-art results, and attempted both concatenation-based decision-level fusion and Deep CCA-based feature-level fusion schemes. The proposed speech and mocap models achieve emotion recognition accuracies of 65.08% and 67.51%, respectively, and the BERT-based text model achieves an accuracy of 72.60%. The decision-level fusion approach significantly improves the accuracy of detecting emotions on the IEMOCAP and MELD datasets. This approach achieves 80.20% accuracy on IEMOCAP which is 8.61% higher than the state-of-the-art methods, and 63.52% and 61.65% in 5-class and 7-class classification on the MELD dataset which are higher than the state-of-the-arts. Show more

Keywords: Emotion recognition, time-distributed models, CNN-LSTM, BERT, DCCA

DOI: 10.3233/JIFS-220280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2455-2470, 2023

Authors: Han, Meng | Li, Ang | Gao, Zhihui | Mu, Dongliang | Liu, Shujuan

Article Type: Research Article

Abstract: In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the …performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given. Show more

Keywords: Classification, multi-class imbalance data, data preprocessing method, algorithm-level classification method

DOI: 10.3233/JIFS-221902

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2471-2501, 2023

Authors: Xiao, Yanjun | Zhao, Churui | Qi, Hao | Liu, Weiling | Meng, Zhaozong | Peng, Kai

Article Type: Research Article

Abstract: In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. …In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system. Show more

Keywords: Pole piece rolling mill, deviation correction system, fuzzy PID, genetic algorithm, algorithm fusion, extended kalman filter

DOI: 10.3233/JIFS-221028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2503-2523, 2023

Authors: Vo, Tham

Article Type: Research Article

Abstract: The wind power is considered as a potential renewable energy resource which requires less management cost and effort than the others like as tidal, geothermal, etc. However, the natural randomization and volatility aspects of wind in different regions have brought several challenges for efficiently as well as reliably operating the wind-based power supply grid. Thus, it is necessary to have centralized monitoring centers for managing as well as optimizing the performance of wind power farms. Among different management task, wind speed prediction is considered as an important task which directly support for further wind-based power supply resource planning/optimization, hence towards …power shortage risk and operating cost reductions. Normally, considering as traditional time-series based prediction problem, most of previous deep learning-based models have demonstrated significant improvement in accuracy performance of wind speed prediction problem. However, most of recurrent neural network (RNN) as well as sequential auto-encoding (AE) based architectures still suffered several limitations related to the capability of sufficient preserving the spatiotemporal and long-range time dependent information of complex time-series based wind datasets. Moreover, previous RNN-based wind speed predictive models also perform poor prediction results within high-complex/noised time-series based wind speed datasets. Thus, in order to overcome these limitations, in this paper we proposed a novel integrated convolutional neural network (CNN)-based spatiotemporal randomization mechanism with transformer-based architecture for wind speed prediction problem, called as: RTrans-WP. Within our RTrans-WP model, we integrated the deep neural encoding component with a randomized CNN learning mechanism to softy align temporal feature within the long-range time-dependent learning context. The utilization of randomized CNN component at the data encoding part also enables to reduce noises and time-series based observation uncertainties which are occurred during the data representation learning and wind speed prediction-driven fine-tuning processes. Show more

Keywords: Wind speed prediction, deep learning, transformer, randomization, nomenclatures

DOI: 10.3233/JIFS-222446

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2525-2541, 2023

Authors: Yu, Wenmei | Xia, Lina | Cao, Qiang

Article Type: Research Article

Abstract: With the development of big data, Internet finance, the digital economy is developing rapidly and has become an important force to drive the continuous transformation of the global economy and society. China has put forward plans for the development of digital economy from 2021 to 2025, requiring the number of core industries of digital economy to reach 10% of GDP by 2025, while continuously improving China’s digital economy to achieve high-quality development of China’s digital economy. Aiming at China’s digital economy, we use the adaptive lasso method and select feature variables based on quantitative and qualitative perspectives, so as to …predict the development trend of China’s digital economy from 2021 to 2025 based on the TDGM (1, 1, r) grey model optimized by the particle swarm algorithm. Meanwhile, we have added the comparative analyses with TDGM(1,1), Grey Verhulst, GM(1,1) models and evaluate the prediction results both Ex-ante and Ex-post, demonstrating the feasibility of the proposed model and the accuracy. Finally, we find that the future of China’s digital economy will meet the planned objectives in terms of quantity and quality, but the trend of digital economy development in quantity is faster, thanks to the development of digital technology application industry. Show more

Keywords: Digital economy development, adaptive lasso grey model, TDGM(1, 1, r) model, quantity and quality

DOI: 10.3233/JIFS-222520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2543-2560, 2023

Authors: Muthumanickam, Arunkumar | Balasubramanian, Gomathy | Chakrapani, Venkatesh

Article Type: Research Article

Abstract: The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable …weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems. Show more

Keywords: Accidents-free driving, autonomous vehicles, deep learning, fleet management, internet of things, microcontroller, sensors

DOI: 10.3233/JIFS-222719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2561-2576, 2023

Authors: Little Flower, X. | Poonguzhali, S.

Article Type: Research Article

Abstract: For real-time applications, the performance in classifying the movements should be as high as possible, and the computational complexity should be low. This paper focuses on the classification of five upper arm movements which can be provided as a control for human-machine interface (HMI) based applications. The conventional machine learning algorithms are used for classification with both time and frequency domain features, and k-nearest neighbor (KNN) outplay others. To further improve the classification accuracy, pretrained CNN architectures are employed which leads to computational complexity and memory requirements. To overcome this, the deep convolutional neural network (CNN) model is introduced with …three convolutional layers. To further improve the performance which is the key idea behind real-time applications, a hybrid CNN-KNN model is proposed. Even though the performance is high, the computation costs of the hybrid method are more. Minimum redundancy maximum relevance (mRMR), a feature selection method makes an effort to reduce feature dimensions. As a result, better performance is achieved by our proposed method CNN-KNN with mRMR which reduces computational complexity and memory requirement with a mean prediction accuracy of about 99.05±0.25% with 100 features. Show more

Keywords: Empirical mode decomposition, minimum redundancy maximum relevance, spectrogram representation, k-nearest neighbor, deep learning

DOI: 10.3233/JIFS-220811

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2577-2591, 2023

Authors: Annapandi, P. | Ramya, R. | Kotaiah, N.C. | Rajesh, P. | Subramanian, Arun

Article Type: Research Article

Abstract: This manuscript proposes an efficient hybrid strategy to obtain the optimal solution of operational cost reduction, size reduction of hybrid renewable energy sources and optimal power flow control for off-grid system. Here, off-grid is incorporated with photovoltaic array, wind turbine, Diesel generator, and battery energy storage system. The hybrid method is joint execution of Giza Pyramids Construction (GPC) and Billiards-inspired optimization algorithm (BOA) hence it is named GPC-BOA technique. The major purpose of proposed method is minimizing the operational cost as well as size of hybrid renewable energy sources and improves the power flow of system. In this energy management …system of off-grid provides cost reduction which includes the generation, replacement, operating and maintenance, cost of fuel consumption, cost of exchanged power with grid, and the penalty for emissions. Here, the GPC method is employed for forecasting the load requirement of system. The BOA technique optimizes the off-grid system through the deliberation of forecasted load requirement. At last, the proposed approach is performed on MATLAB platform and the performance is assessed using existing techniques. Show more

Keywords: Energy management system, cost, power flow, photovoltaic array, wind turbine, Diesel generator, battery energy storage system

DOI: 10.3233/JIFS-221176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2593-2614, 2023