Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Xiao, Yanjun | Han, Furong | Ding, Yvheng | Liu, Weiling

Article Type: Research Article

Abstract: The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge …base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms. Show more

Keywords: Rapier loom, expert system, fault tree, fault diagnosis

DOI: 10.3233/JIFS-210741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3429-3441, 2021

Authors: Xu, Haiyan | Chang, Yuqing | Zhao, Yong | Wang, Fuli

Article Type: Research Article

Abstract: Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by …separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets. Show more

Keywords: Ensemble empirical mode decomposition, long short-term memory network, elman neural network, error correction

DOI: 10.3233/JIFS-210779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3443-3462, 2021

Authors: Luo, Jun | Tian, Qin | Xu, Meng

Article Type: Research Article

Abstract: Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in …fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness. Show more

Keywords: Butterfly optimization algorithm, benchmark function, information cross-sharing, neighborhood search weight factor, reverse guidance

DOI: 10.3233/JIFS-210815

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3463-3484, 2021

Authors: Zhang, Lijun | Duan, Lixiang | Hong, Xiaocui | Liu, Xiangyu | Zhang, Xinyun

Article Type: Research Article

Abstract: Machinery operates well under normal conditions in most cases; far fewer samples are collected in a fault state (minority samples) than in a normal state, resulting in an imbalance of samples. Common machine learning algorithms such as deep neural networks require a significant amount of data during training to avoid overfitting. These models often fail to detect minority samples when the input samples are imbalanced, which results in missed diagnoses of equipment faults. As an effective method to enhance minority samples, Deep Convolution Generative Adversarial Network (DCGAN) does not fundamentally address the problem of unstable Generative Adversarial Network (GAN) training. …This study proposes an improved DCGAN model with improved stability and sample balance for achieving greater classification accuracy over minority samples. First, spectral normalization is performed on each convolutional layer, improving stability in the DCGAN discriminator. Then, the improved DCGAN model is trained to generate new samples that are different from the original samples but with a similar distribution when the Nash equilibrium is reached. Four indices—Inception Score (IS), Fréchet Inception Distance Score (FID), Peak Signal to Noise Ratio (PSNR), and Structural Similarity (SSIM)—were used to quantitatively evaluate of the generated images. Finally, the Balance Degree of Samples (BDS) index was proposed, and the new samples are proportionally added to the original samples to improve sample balance, resulting in the formation of several groups of datasets with different balance degrees, and Convolutional Neural Network (CNN) models are used to classify these samples. With experimental analysis on the reciprocating compressor, the variance of lost data is found to be less than 1% of the original value, representing an increase in stabilityof the model to generate diverse and high-quality sample images, as compared with that of the unmodified model. The classification accuracy exceeds 95% and tends to remain stable when the balance degree of samples is greater than 80%. Show more

Keywords: Imbalanced, data enhancement, fault diagnosis, DCGAN, CNN

DOI: 10.3233/JIFS-210843

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3485-3498, 2021

Authors: Moussa, Mona M. | Shoitan, Rasha | Abdallah, Mohamed S.

Article Type: Research Article

Abstract: Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In …the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric. Show more

Keywords: Object localization, Siamese network, hierarchical clustering, and convolutional neural networks (CNNs)

DOI: 10.3233/JIFS-210854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3499-3508, 2021

Authors: Meixin, Huang | Caixia, Liu

Article Type: Research Article

Abstract: Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing …it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models. Show more

Keywords: Grey model, variable-order fractional accumulation, whale optimization algorithm, electricity consumption

DOI: 10.3233/JIFS-210871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3509-3522, 2021

Authors: Li, Shuhao | Sun, Qiang | Liu, Shupei

Article Type: Research Article

Abstract: In recent years, supply chain risk management has been followed with interest due to the short life cycle of products. How to identify risk indicators can help evaluate risks on supply chains. Commonly adopted methods such as Fuzzy to determine the level of risks have limitations. In this paper, a framework of supply chain risk evaluation is first proposed and risk indicators are identified by theoretical surveys from 35 keywords and empirical analysis from 448 questionnaires. Moreover, both linguistic risk assessment model and Cloud model are used to evaluate risks of supply chain. The Cloud model evaluation results are between …general risk and high risk but closer to high risk. In addition, Cloud expected value of risk is 6.54 which is within the high-risk range, and evaluation results are also high risk. It is shown that when the weights are the same, the cloud model can determine the priority of risk indicators, and reflect volatility and randomness comparing with other evaluation methods. Show more

Keywords: Cloud model, supply chain risk management, word frequency, risk identification, risk evaluation

DOI: 10.3233/JIFS-210883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3523-3540, 2021

Authors: Chen, Xinghao | Zhou, Bin

Article Type: Research Article

Abstract: Path planning is the basis and prerequisite for unmanned aerial vehicle (UAV) to perform tasks, and it is important to achieve precise location in path planning. This paper focuses on solving the UAV path planning problem under the constraint of system positioning error. Some nodes can re-initiate the accumulated flight error to zero and this type of scenario can be modeled as the resource-constrained shortest path problem with re-initialization (RCSPP-R). The additional re-initiation conditions expand the set of viable paths for the original constrained shortest path problem and increasing the search cost. To solve the problem, an effective preprocessing method …is proposed to reduce the network nodes. At the same time, a relaxed pruning strategy is introduced into the traditional Pulse algorithm to reduce the search space and avoid more redundant calculations on unfavorable scalable nodes by the proposed heuristic search strategy. To evaluate the accuracy and effectiveness of the proposed algorithm, some numerical experiments were carried out. The results indicate that the three strategies can reduce the search space by 99%, 97% and 80%, respectively, and in the case of a large network, the heuristic algorithm combining the three strategies can improve the efficiency by an average of 80% compared to some classical solution. Show more

Keywords: UAV path planing, constraints shortest paths, resource re-initialized, pulse algorithm, heuristics

DOI: 10.3233/JIFS-210901

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3541-3553, 2021

Authors: Farag, Hania H. | Said, Lamiaa A. A. | Rizk, Mohamed R. M. | Ahmed, Magdy Abd ElAzim

Article Type: Research Article

Abstract: COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal …models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19. Show more

Keywords: Convolutional neural network, hyperparameter, residual network, xception network, random search optimization

DOI: 10.3233/JIFS-210925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3555-3571, 2021

Authors: Song, Qinyu | Ni, Yaodong | Ralescu, Dan

Article Type: Research Article

Abstract: The customer demands of various products bring a challenge for manufacturers. They have to design customized products while maintaining economies of scale and low costs. In this paper, to address this challenge, four approaches are argued to help companies find out the optimal solutions of products’ performance and the maximum profit: (i) only platform modularity without component sharing (ii) only component sharing without platform modularity, (iii) using both platform modularity and component sharing to develop products, or iv) the products are developed individually from a given unshared components set. A theoretical model is proposed and the most profitable approach is …found to develop a whole new product family when uncertainty exists in the customer demand and economies of scale with pre-defined parameters. We find that, when consumers’ valuation is considered, the manufacturer may prefer to adopt platform or component sharing individually rather than combining them because the performance of high-end products using platform and component sharing strategies is worse than that using two strategies separately. If platform and component sharing are adopted, the high-end product is under designed, but the manufacturer can benefit from economies of scale. When economies of scale of the platform are greater than or equal to that of component sharing, the optimal performance level of low-end products under platform strategy is lower than that under component sharing strategy. Finally, the detailed numerical analysis provides support for the feasibility and effectiveness of the model. Show more

Keywords: Platform, component sharing, uncertainty theory

DOI: 10.3233/JIFS-210957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3573-3589, 2021