Journal of Intelligent & Fuzzy Systems - Volume 40, issue 6

Authors: Zhong, Xianyou | Gao, Xiang | Mei, Quan | Huang, Tianwei | Zhao, Xiao

Article Type: Research Article

Abstract: Gear fault vibration signals are commonly non-stationary, and useful fault information is often buried in heavy noise, which makes it difficult to extract gear fault features. How to select the suitable fault frequency bands is the key to gear fault diagnosis. To address the above problems, a method combining the improved minimum entropy deconvolution (MED) and accugram, named IMEDA, is proposed for extracting gear fault features. Firstly, a selection index based on permutation entropy (PE) and correlation coefficient is defined. Then, the optimal filter length can be effectively selected by the step-length searching method using the proposed index as objective …function, and the improved MED is employed to preprocess the gear vibration signals. Finally, the accugram analysis is performed for the preprocessed signals to obtain the optimal frequency band, and the fault characteristic frequencies are extracted from the square envelope spectrum of the signals in the optimal band. The method is validated by gear experimental data with gear wear-out failure. The analysis results demonstrate that the proposed method owns superior effect by comparing with the fast kurtogram (FK), MED combined with FK (MED-FK), accugram and infogram. Show more

Keywords: Minimum entropy deconvolution, accugram, frequency band selection, fault feature extraction

DOI: 10.3233/JIFS-210405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12265-12282, 2021

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12283-12300, 2021

Authors: Li, Huanhuan | Ji, Ying | Qu, Shaojian

Article Type: Research Article

Abstract: Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate …L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided. Show more

Keywords: Two-stage stochastic integrated adjustment deviations and consensus model, directional constraints, uncertain adjustment costs, uncertainty initial opinions, L-shaped algorithm

DOI: 10.3233/JIFS-210443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12301-12319, 2021

Authors: Al-Tarawneh, Ahmed | Al-Saraireh, Ja’afer

Article Type: Research Article

Abstract: Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This …work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks. Show more

Keywords: Tweets, hacking, prediction, twitter, social networks

DOI: 10.3233/JIFS-210458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12321-12337, 2021

Authors: Ling, Chunyan | Lu, Zhenzhou

Article Type: Research Article

Abstract: To measure the effects of the fuzzy inputs on structural safety degree, this paper establishes the failure credibility-based global sensitivity by the fuzzy expected value of the absolute difference between the unconditional failure credibility and conditional one. To establish the failure credibility-based global sensitivity, the conditional failure credibility is firstly defined according to the original definition of conditional event and the relationship among the possibility, necessity and credibility, in which no extra assumption is introduced. After that, the equivalent expression of the failure credibility is deduced, on which the Bayesian transformation of the conditional failure credibility is obtained in this …paper. Then, a single-loop method based on the sequential quadratic programming is applied to efficiently estimate the defined failure credibility-based global sensitivity. According to the result of the constructed failure credibility-based global sensitivity, designers can pay more attentions to the more important fuzzy inputs to have a better control of the structural safety degree. The presented examples demonstrate the feasibility of the constructed failure credibility-based global sensitivity and the efficiency of the proposed solution. Show more

Keywords: Fuzzy input, failure credibility, global sensitivity, fuzzy expected value, conditional failure credibility, sequential quadratic programming

DOI: 10.3233/JIFS-210461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12339-12359, 2021

Authors: Song, Xudong | Zhu, Dajie | Liang, Pan | An, Lu

Article Type: Research Article

Abstract: Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the …fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method. Show more

Keywords: Elastic net, fault diagnosis, LSTM, transfer learning

DOI: 10.3233/JIFS-210503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12361-12369, 2021

Authors: Zou, Yuan | Yang, Daoli | Pan, Yuchen

Article Type: Research Article

Abstract: Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error …correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality. Show more

Keywords: Gross domestic product, grey Markov chain, artificial neural network, residual correction, forecasting

DOI: 10.3233/JIFS-210509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12371-12381, 2021

Authors: Noon, Serosh Karim | Amjad, Muhammad | Ali Qureshi, Muhammad | Mannan, Abdul

Article Type: Research Article

Abstract: Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of …achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference. Show more

Keywords: Cotton leaf disease, efficientnet, mobilenet, deep leaning, agriculture

DOI: 10.3233/JIFS-210516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12383-12398, 2021

Authors: Abughazalah, Nabilah | Khan, Majid | Munir, Noor | Zafar, Amna

Article Type: Research Article

Abstract: In this article, we have designed a new scheme for the construction of the nonlinear confusion component. Our mechanism uses the notion of a semigroup, Inverse LA-semigroup, and various other loops. With the help of these mathematical structures, we can easily build our confusion component namely substitution boxes (S-boxes) without having specialized structures. We authenticate our proposed methodology by incorporating the available cryptographic benchmarks. Moreover, we have utilized the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best nonlinear confusion component. With the aid of this multi-criteria decision-making (MCDM), one can easily select the …best possible confusion component while selecting among various available nonlinear confusion components. Show more

Keywords: Nonlinear confusion component, semigroup, loop, TOPSIS, MCDM

DOI: 10.3233/JIFS-210524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12399-12410, 2021

Authors: Wang, H.Y. | Wang, J.S. | Zhu, L.F.

Article Type: Research Article

Abstract: Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed …clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness. Show more

Keywords: Fuzzy C-means clustering algorithm, clustering validity function, membership matrix, intra-class compactness, inter-class separation

DOI: 10.3233/JIFS-210555

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12411-12432, 2021