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

Authors: Zhang, Feng | Wu, Mingying | Hou, Xinting | Wang, Xinhe | Han, Cheng | Xu, Xiayu | Zhang, Leilei

Article Type: Research Article

Abstract: In order to improve the reliability and performance of landing gear retraction systems, this paper presents two importance analysis methods based on universal grey operation. According to the system principle and fault mechanisms, the fault tree of the retraction system was first established. The uncertainties of the bottom events were then described using the universal grey number to obtain the universal grey representation of the system failure probability. And compared with the traditional interval operation, the results show that universal grey operation can solve the problem of interval expansion with uncertainty. Importance analyses of the bottom events were then conducted …based on the probability importance and the key importance. By comparing the two important indices of the bottom events, the larger the value is, the higher the importance is. It was found that the occurrence of the bottom events “pipeline oil leakage,” “pump motor damage,” and “oil pollution” had the greatest impact on system failure probability, thus determining the key weak links affecting system failure and indicating the most effective targets for improvement. Show more

Keywords: Universal grey operation, interval operation, importance measure, fault tree, landing gear retraction system

DOI: 10.3233/JIFS-202248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2675-2685, 2021

Authors: Jingfei, Chang | Yang, Lu | Ping, Xue | Xing, Wei | Zhen, Wei

Article Type: Research Article

Abstract: Deep convolutional neural network (CNN) is difficult to deploy to mobile and portable devices due to its large number of parameters and floating-point operations (FLOPs). To tackle this problem, we propose a novel channel pruning method. We use the modified squeeze-and-excitation blocks (MSEB) to measure the importance of the channels in the convolutional layers. The unimportant channels, including convolutional kernels related to them, are pruned directly, which greatly reduces the storage cost and the number of calculations. For ResNet with basic blocks, we propose an approach to consistently prune all residual blocks in the same stage to ensure that the …compact network structure is dimensionally correct. After pruning we retrain the compact network from scratch to restore the accuracy. Finally, we verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012. The results indicate that the performance of the compact network is better than the original network when the pruning rate is small. Even when the pruning amplitude is large, the accuracy can also be maintained or decreased slightly. On the CIFAR-100, when reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy of VGG-19 even improve by 0.54% after retraining. The source code is available at https://github.com/JingfeiChang/UCP . Show more

Keywords: Deep learning, convolutional neural network, network pruning, image classification

DOI: 10.3233/JIFS-202290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2687-2699, 2021

Authors: Chakhrit, Ammar | Chennoufi, Mohammed

Article Type: Research Article

Abstract: Failure mode, effects, and criticality analysis (FMECA) is a proactive quality tool that allows the identification and prevention of the potential failure modes of a process or product. In a conventional FMECA, for each failure mode, three risk parameters, namely frequency, non-detection, and severity are evaluated and a risk priority number (RPN) is calculated by multiplying these parameters to assess one signal criticality. However, in many cases, it suffers from some shortcomings regarding the decision-making and the situation where the information provided is ambiguous or uncertain. This paper describes a new fuzzy multi-criticality approach for improving the use of FMECA …by treating FMECA as a fuzzy multi-criteria optimization model. The new approach bases on replacing the calculation of a single criticality with a fuzzy inference system for improving the criticality evaluations which offers five partial criticalities that efficiently and separately calculate the impact of a failure on the environment, personnel, production, equipment, and management. In addition, an analytical hierarchy method (AHP) is used to calculate the priorities weights for each partial criticality and construct a criticality matrix in order to improve the relevance of decision-making. Furthermore, a real case of LPG storage system for ZCINA Hassi Messaoud in Algeria is provided to illustrate the practical implementation of the suggested approach and extremely shows the pertinence of the suggested fuzzy model as decision-making tools in preventing industrial risks with providing encouraging results regarding the criticality estimation and improve decision-making by prioritizing “preventive –corrective actions” and determine the efficient action for each partial criticality to control the risk effectively. Show more

Keywords: Fuzzy logic, FMECA, criticality assessment, multi-criteria, AHP, decision-making

DOI: 10.3233/JIFS-202362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2701-2716, 2021

Authors: Amiri Shahmirani, Mohammad Reza | Akbarpour Nikghalb Rashti, Abbas | Adib Ramezani, Mohammad Reza | Golafshani, Emadaldin Mohammadi

Article Type: Research Article

Abstract: Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective …parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data. Show more

Keywords: Earthquake, damage degree, fuzzy model, membership function, rules of fuzzy model

DOI: 10.3233/JIFS-202424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2717-2730, 2021

Authors: Jerisha Liby, J. | Jaya, T

Article Type: Research Article

Abstract: This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R 1 , G 1 , B 1 ), (R 2 , G 2 , B 2 ) are converted to HSV (Hue, saturation, values) Coefficients (H 1 , S 1 , V 1 ) and (H 2 , S 2 , V 2 ). The ‘Value’ Coefficients V 1 and V …2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC). Show more

Keywords: Video watermarking, data hiding, RGB model, HSV model, embedding rate, PSNR, SSIM, normalized cross-correlation, attacks

DOI: 10.3233/JIFS-202468

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2731-2742, 2021

Authors: Kaliraman, Bhawna | Duhan, Manoj

Article Type: Research Article

Abstract: Electroencephalogram (EEG) signals are essential in brain-computer interface systems. Nowadays, these signals are employed in various medical applications. In the past few years, EEG signals gain more attention in security systems to identify users, as these signals are unique for each individual. The current study explores deep learning frameworks for EEG-based user identification. Data from 107 users were considered for the study, which is acquired using 64 channels. Several experimental tests are performed over both convolutional neural network (CNN) and recurrent neural networks (RNN) using a 10-fold cross-validation process to check system effectiveness. In CNN, 1-D Convolutional layer is employed …for the processing of EEG signals. In RNN, LSTM and GRU are used to check system accuracy. For performance measure various metrices were considered such as accuracy, precision, recall and kappa score. Acquired results suggest that gated recurrent unit (GRU) outperforms other models in terms of accuracy and complexity both. GRU model has 91.2% accuracy and has three layers only, which reduces the model’s complexity. The training cost is also decreasing due to the low complexity of the model. Show more

Keywords: Convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), feed forward neural networks (FFNN), rectified linear units (ReLU), gated recurrent unit (GRU)

DOI: 10.3233/JIFS-202490

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2743-2753, 2021

Authors: Zhang, Jing | Sheng, Yuhong | Wang, Xiaoli

Article Type: Research Article

Abstract: Parameter estimation of high-order uncertain differential equations is an inevitable problem in practice. In this paper, the equivalent equations of high-order uncertain differential equations are obtained by transformation, and the parameters of the first-order uncertain differential equation including Liu process are estimated. Based on the least squares estimation method, this paper proposes a means to minimize the residual sum of squares to obtain an estimate of the parameters in the drift term, and make the noise sum of squares equal to the residual sum of squares to obtain an estimate of the parameters in the diffusion term. In addition, some …numerical examples are given to illustrate the proposed method. Finally, applications of the high-order uncertain spring vibration equations verify the viability of our method. Show more

Keywords: Uncertainty theory, high-order uncertain differential equation, parameter estimation, least squares estimation

DOI: 10.3233/JIFS-202522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2755-2764, 2021

Authors: Yi, Kui | Mao, Xiulan | Cheng, Honglei | Zhang, Ligang | Zhang, Dian

Article Type: Research Article

Abstract: Evaluating the capability of smart information service and exploring the smart information service elements in scenic areas can accelerate the smart tourism industry progress to a highly effective administrative and high quality of smart tourism service. This study aims to exploring the connections of a variety of smart information service elements in scenic areas, analyzing and evaluating the capability of smart information service, mapping a positive resolution to improve the capability of information service in scenic spots. Based on a synthetic method that combining Structural Equation Modeling (SEM) and Analytic Network Process (ANP), explicitly using the SEM to extracting the …key factors and mapping each factors’ co-relations, and in further step, using the ANP to carry a fuzzy evaluation of weighing the information service capability and each element in the case of Jiangxi Province in China, the result shows that the method of “SEM-ANP” is better fit than single ”SEM” or ”ANP”, the evaluating system for smart information service in scenic areas is significantly innovative and scientific to supply effective suggestions for policy makers. Show more

Keywords: Smart scenic areas, information service construction, “SEM-ANP” synthetic evaluation

DOI: 10.3233/JIFS-202536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2765-2777, 2021

Authors: Gao, Ting | Ma, Zhengming | Gao, Wenxu | Liu, Shuyu

Article Type: Research Article

Abstract: There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be …determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals. Show more

Keywords: Tensor decompostion, local linear embedding, mode product, dimensionality reduction

DOI: 10.3233/JIFS-202588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2779-2796, 2021

Authors: Yang, Meng | Ni, Yaodong | Yang, Xiangfeng | Ralescu, Dan A.

Article Type: Research Article

Abstract: In the increasingly competitive logistics industry, much more emphasis has been put on customer satisfaction by firms to distinguish themselves from their competitors. Motivated by the practices, the consistent vehicle routing problem (ConVRP) incorporates service consistency into the vehicle routing problem to improve customer satisfaction. However, the majority of the existing research considers the ConVRP in a deterministic environment while the uncertainties are not fully studied. Therefore, this paper solves the consistent vehicle routing problem under uncertain environment (UnConVRP) taking into account uncertain customer demands, travel times, and service times. Over a multi-day planning horizon, customers may have multi-day or …single-day service requirements. The same driver is assigned to each customer almost at the same time each day when customers require service. The objective is to design the routes for vehicles over the planning horizon under uncertain environment while maintaining service consistency. Two uncertain programming models are established based on different decision criteria and the crisp equivalents are proposed using uncertainty theory. An efficient template-based solution framework is designed to solve the models where the artificial bee colony algorithm is embedded. Initially, the template route is constructed to guarantee the service consistency of frequent customers. Then the final daily routes can be derived from the template route. Finally, numerical experiments are performed to show the effectiveness of the proposed algorithm. Show more

Keywords: Vehicle routing, consistent service, uncertain programming, artificial bee colony

DOI: 10.3233/JIFS-202593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2797-2812, 2021