Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Jin, Feifei | Zhu, Yajun | Zhang, Yixiao | Guo, Shuyan | Liu, Jinpei | Zhou, Ligang

Article Type: Research Article

Abstract: Interval type-2 trapezoidal fuzzy (IT2TrF) number is a powerful tool to depict fuzzy information. Information measures methods have received more and more attention in recent years as they play an important role in decision-making theory. A new multi-attribute decision-making (MADM) method supported by IT2TrF information measures is investigated in this paper under the IT2TrF information environment. Firstly, three axiomatic definitions of IT2TrF information measures are introduced, which include information entropy, similarity measure and cross-entropy. Secondly, with the help of the exponential function, we formulate some information measure formulas, which are followed by the proofs that the exponential entropy, exponential similarity …measure and exponential cross-entropy fit the three axiomatic definitions. Subsequently, a novel IT2TrF MADM method is designed, in which the IT2TrF exponential entropy and cross-entropy are utilized to generate the attribute weights, the IT2TrF exponential similarity measure is employed to obtain the closeness degree of the ideal solution and derive the most satisfying solution. Lastly, we provide a numerical example of corporate investment to demonstrate the applicability and feasibility of the proposed MADM method. The robustness and merits of the developed MADM method are highlighted by the comparative analysis. Show more

Keywords: Multi-attribute decision-making, interval type-2 trapezoidal fuzzy numbers, information measures, exponential function

DOI: 10.3233/JIFS-230310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2319-2330, 2023

Authors: Yuan, Yuxia | Zhang, Yachao

Article Type: Research Article

Abstract: Background: Image semantic segmentation can be understood as the allocation of a predefined category label to each pixel in the image to achieve the region segmentation of the image. Different categories in the image are identified with different colors. While achieving pixel classification, the position information of pixel points of different categories in the image is retained. Purpose: Due to the influence of background and complex environment, the traditional semantic segmentation methods have low accuracy. To alleviate the above problems, this paper proposes a new real-time image semantic segmentation framework based on a lightweight deep convolutional encoder-decoder architecture …for robotic environment sensing. Methodology: This new framework is divided into three stages: encoding stage, decoding stage and dimension reduction stage. In the coding stage, a cross-layer feature map fusion (CLFMF) method is proposed to improve the effect of feature extraction. In the decoding stage, a new lightweight decoder (LD) structure is designed to reduce the number of convolutional layers to speed up model training and prediction. In the dimension reduction stage, the convolution dimension reduction method (CDR) is presented to connect the encoder and decoder layer by layer to enhance the decoder effect. Results: Compared with other state-of-the-art image semantic segmentation methods, we conduct comparison experiments on datasets Cityscapes, SUN RGB-D, CamVid, KITTI. The Category iIoU combined with the proposed method is more than 70%, and the Category IoU is as high as 89.7%. Conclusion: The results reflect that the new method can achieve the better semantic segmentation effect. Show more

Keywords: Image semantic segmentation, lightweight deep convolutional encoder-decoder architecture, cross-layer feature map fusion, convolution dimension reduction method, robotic environment sensing

DOI: 10.3233/JIFS-222221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2331-2345, 2023

Authors: Hu, Zhexian

Article Type: Research Article

Abstract: The motivation for this paper is to consider that in recent years, the concept of metaverse, as the latest and most popular concept in the world, has been widely applied and studied in various industries, including economic management, art design, education and teaching. However, the academic and scientific circles have not reached a consensus on whether to define the metaverse as a technology or an intelligent scene. We believe that the metaverse should be a key concept and emerging theory for constructing the future wisdom field. Therefore, in this study, our research objective is to focus on the visual art …evaluation in digital works, and propose a visual art quality evaluation method in future metaverse digital works. This method is based on the quality function deployment theory and fuzzy mathematics theory in marketing. The second core point of this study is to build a field framework for the visual art evaluation of future digital works based on the metaverse by combing the current international and domestic understanding of the concept of metaverse. In addition, taking visual art quality evaluation as the research object, we have constructed a visual art quality evaluation index system for digital works under the background of metaverse. The index system is composed of one first-class index, three second-class indexes and nine third-class indexes. At the same time, we proposed a new fuzzy mathematics evaluation method in the research, called G1 entropy method. This algorithm combines subjective weighting method: G1 method and objective weighting method: entropy method as an important method of quality evaluation, and carries out the final rating through the combination weight of G1 entropy method. This study makes up for the concept of the future metaverse, introduces the gaps in the theory of visual art evaluation of future digital works, innovates the analysis of new concepts and the improvement of old methods, builds a new scene of organic combination of new technology and traditional visual art, and provides new ideas for the improvement of art quality at home and abroad in the future. In general, we sorted out the contributions of this research, including the following three aspects: (1) we constructed the metaverse field structure of digital works. By analyzing the current international and domestic research literature on the application of metaverse technology, especially the concept of metaverse in art scenes, we proposed to construct the field structure of online visual art after introducing the concept of metaverse, including blockchain technology, artificial intelligence technology Interaction technology and Internet of things technology as the four characteristics; (2) Method theoretical contribution: we further take the visual art quality evaluation as the research object, construct the index system of visual art quality evaluation of digital works under the background of metaverse, and propose an evaluation method of G1 entropy method, which is actually a method of subjective weighting by experts; (3) We use the method proposed in (2) to complete the calculation and ranking of the importance of 9 indicators in a practical case, and give some countermeasures for the calculation results of the importance of indicators. In conclusion, this study has realized the construction of new application scenarios of concepts and the new improvement of methods, and can provide theoretical and practical case experience support for the quality improvement of international and domestic metaverse visual art. Show more

Keywords: Metaverse, visual art, field architecture, quality function deployment, G1 entropy method

DOI: 10.3233/JIFS-223376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2347-2365, 2023

Authors: Davvaz, Bijan | Chinram, Ronnason | Lekkoksung, Somsak | Lekkoksung, Nareupanat

Article Type: Research Article

Abstract: Ideals play an essential part in studying ordered semigroups. There are several generalizations of ideals that are used to investigate ordered semigroups. It is known that (m , n )-ideals are an abstraction of bi-ideals, and n -interior ideals are an abstraction of interior ideals. This paper introduces a generality of (m , n )-ideals and n -interior ideals, so-called (α, β)-fuzzy (m , n )-ideals and (α, β)-fuzzy n -interior ideals. Furthermore, we discuss our current notions with those that already exist. We examine connections between (m , n )- (resp., n -interior) ideals and (α, β)-fuzzy (m , …n )- (resp., n -interior) ideals. A characterization of (α, β)-fuzzy (m , n )- (resp., n -interior) ideals, by a particular product, in ordered semigroups is provided. We demonstrate that our results generalize the known results through specific settings. Show more

Keywords: Ordered semigroup, (α, β)-fuzzy (m, n)-ideal, (α, β)-fuzzy n-ideal, bi-ideal, interior-ideal

DOI: 10.3233/JIFS-224255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2367-2380, 2023

Authors: Zhou, Bin | Chen, Jieshi | Zhang, Yang | Yang, Shanglei | Lu, Hao

Article Type: Research Article

Abstract: In the laser spiral welding (LSW) process, the welding parameters have a significant impact on the weld quality. In this paper, experiments were conducted and experimental data were collected on galvanized steel sheets using the LSW process, and mathematical models were developed using response surface methodology (RSM) and genetic algorithm (GA) to verify the specific effects of each process parameter on the weld and to perform process optimization. Laser power, welding speed, gap and focal length were selected as the influencing factors, and melt depth, melt width and concave as the output results. In the RSM model we found that …the laser power was positively correlated with the weld depth and width, while the welding speed was inversely correlated with the weld depth and width, the gap was positively correlated with the amount of concave, and the focal length had no significant effect on the weld. In the GA model we use a large amount of experimental data for BP neural network training and iterative optimization using a genetic algorithm. Validation experiments were conducted on two models, and the results indicated that the two models had higher accuracy in predicting the welding depth and width compared to predicting the concave. The GA model had an 8% increase in tensile strength and a 25% increase in plasticity of the weld joint obtained from the optimal process compared to the RSM model. The GA model has higher accuracy in optimizing the LSW process. Show more

Keywords: Laser spiral welding, response surface methodology, genetic algorithm, process optimization, mechanical property

DOI: 10.3233/JIFS-224448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2381-2392, 2023

Authors: Deng, Guannan | Zhang, Mei | Meng, Xiangqi | Yuan, Jiaming

Article Type: Research Article

Abstract: In this paper, we establish the matching relation between implication operator and aggregation operator, which provides a new solution for the design and construction of multi-rule fuzzy inference system. Firstly, according to the definition and monotonicity of implication operator, a new classification method of implication operator is proposed, and then the fuzzy inference process using different implication operators is classified. Then, dynamic maximum aggregation operator and dynamic minimum aggregation operator are proposed. Based on the compositional rule of inference (CRI) method, a matching method and basis of implication operator and aggregation operator for fuzzy inference systems is given and illustrated …with examples. Finally, the applicability of the proposed method in this paper is further illustrated by comparing the method with existing methods in the literature and using the nearness degree as an evaluation index. Show more

Keywords: Multi-rule fuzzy inference systems, classification of fuzzy implication, aggregation operators, nearness degree

DOI: 10.3233/JIFS-230866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2393-2408, 2023

Authors: Shi, Bo | Lv, Bin

Article Type: Research Article

Abstract: To improve the recognition accuracy of badminton players’ swing movements, this study proposes a single inertial sensor based method for badminton swing movement recognition. This article proposes a badminton racket-mounted data gathering system with a single inertial sensor and proposes a real-time motion data flow-based window segmentation technique to capture motion data. On this basis, a two-layer classifier recognition model based on C4.5 Decision Tree (C4.5 T) algorithm and Random Forest (RF) method is constructed to recognize swing technical actions. Using the C4.5 T to identify the swing style of athletes; The RF method is used to recognize the swing …technical action. The final experiment showed that the method studied achieved a recognition accuracy of 95.36% for six common swing movements. The proposed model has good application prospects in the recognition of badminton swing movements. However, due to the limitations of the experimental conditions, the recognition effect of this method on more complex swing movements needs to be further verified. Show more

Keywords: Single inertial sensor, the swinging movement of badminton, action recognition, random forest

DOI: 10.3233/JIFS-231409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2409-2418, 2023

Authors: Huang, Jingcao | Guo, Bin | Dian, Songyi

Article Type: Research Article

Abstract: Hydropower station is vital for the stable growth of the national economy. How to timely warn the possible faults of hydropower stations has become an increasingly popular research topic. The traditional detection model is difficult to detect the small abnormal changes in the data, and these abnormal changes are often the precursor of faults. To improve the sensitivity of the traditional detection model, this study introduced a weight factor into the traditional LSTM detection model. By using the correction mechanism, the LSTM correction model makes the prediction model never deviate from the normal track following the appearance of abnormal data. …This ensures that the model can generate large residuals after abnormal data occur so that we can detect these abnormal data in time. Finally, this paper puts forward two factors related to equipment health and integrates these two factors to form a health index. The results show that the LSTM correction model based on the health index can not only detect small changes that cannot be detected by traditional detection models but also knows the wear and tear of equipment during operation based on the changes in health indicators. Show more

Keywords: Hydropower station, LSTM, correction mechanism, anomaly detection, health factors

DOI: 10.3233/JIFS-223461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2419-2436, 2023

Authors: Yu, Ping | Wang, Haotian | Cao, Jie

Article Type: Research Article

Abstract: In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep …features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks. Show more

Keywords: Time sequential resnet, temporal feature extraction layer, spatial attention module, deep feature extraction layer, remaining useful life Introduction

DOI: 10.3233/JIFS-223971

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2437-2448, 2023

Authors: Ramasamy, Uma | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data dataset. Autoimmune disease is widely spread all over the world. Some autoimmune arthritis diseases are Rheumatoid Arthritis, Psoriatic Arthritis, Juvenile Arthritis, etc. These diseases come under both categories autoimmune and inflammatory diseases. The proposed work is designed to suggest the best machine learning model with the highest observed accuracy for the Arthritis Profile Data. Many authors do …not compare newly created datasets with previously used datasets. This can lead to inaccurate results due to the lack of reliable comparison. Additionally, it can prevent researchers from detecting potential bias in the data. Comparing datasets can help to identify and address any potential issues and improve the accuracy of the results. It is important to review existing datasets before beginning a new project to ensure the accuracy of the results. This article is the first study on the topic that analysis the accuracy behavior of each machine learning model concerning the Arthritis Profile Data and various benchmark disease datasets with different hold-out and k-fold cross-validation methods. The study concludes with a glimpse of whether dataset and feature size affect model prediction accuracy and proffers a machine learning model for the Arthritis Profile Data. The proposed research explores base learning classification algorithms and ensemble methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting from machine learning. Our empirical evidence clearly states XGBoost ensemble technique shows the highest accuracy for the Arthritis Profile Data. Show more

Keywords: Bias, variance, hold-out, cross-validation, autoimmune arthritis disease, machine learning, ensemble method

DOI: 10.3233/JIFS-224115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2449-2463, 2023