Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Geng, Xiuli | Li, Yiqun | Zhang, Hongliu | He, Jianjia

Article Type: Research Article

Abstract: Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the …experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed. Show more

Keywords: Product-service system (PSS), design requirement, information axiom, hesitant fuzzy linguistic term set, non-linear programming

DOI: 10.3233/JIFS-231329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9007-9028, 2023

Authors: Kai, Wei

Article Type: Research Article

Abstract: In this study, we focus on the analysis of factors influencing the siting decision of coal emergency reserve centers. Specifically, we first draw on the quality function deployment theory in marketing to logically integrate the ideas of this study. On this basis, we adopted an interdisciplinary fuzzy decision-making method, namely the G1-entropy method, to quantitatively evaluate the research of this paper. Thereafter, we constructed a three-level index system based on the characteristics of the coal emergency reserve site selection, and used the G1-entropy value method to calculate the weights of the indicators in the coal emergency reserve center siting decision …index system and obtain the results. Our research findings have found that the three key indicators of coal conventional reserve, emergency coal transportation methods, and emergency response time play a crucial role in the decision-making of coal emergency reserve center location. Therefore, we propose specific countermeasures and suggestions for these three key indicators. Our study can provide support for the government to better select the location of emergency coal reserves, better improve the national energy layout, and provide support for relevant decision makers on how to better reserve coal. The location of the emergency coal reserve center can better play the role of strategic reserve to stabilize the market function, effectively respond to the impact of various events on the energy market, and can make corresponding suggestions to the construction of the national energy security reserve system. Show more

Keywords: Emergency reserve center, site selection decision, quality function deployment theory, G1 method, entropy value method

DOI: 10.3233/JIFS-232299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9029-9052, 2023

Authors: Lyu, Aobo | Jiang, Jingjing | Zhou, Liang

Article Type: Research Article

Abstract: Central Bank Digital Currency (CBDC) pledges to realize a vast array of new functionalities, such as frictionless consumer payment and money-transfer systems, as well as precise supervision of money circulation, thereby enabling a number of new financial instruments and monetary policy levers. This study proposes, from a system feedback loop and cybernetics perspective, a Dynamic Issuance Mechanism (DIM) for CBDC that can theoretically enhance the vitality of economic operations. In accordance with this mechanism, the central bank implements dynamic issuance by monitoring cash leakage in real-time, so as to maintain the stability of the amount of money circulating on the …market, thereby boosting the currency turnover rate and financial vitality. To demonstrate the efficacy of the DIM, we employ the Agent-Based Modeling (ABM) tool to develop a macroeconomic simulation model for qualitative analysis that includes four entities: Central Bank, households, firms, and commercial banks. The multi-cycle operation process of the model includes a variety of economic indicators demonstrating that DIM has the potential to boost economic vitality and social production efficiency without exerting an adverse effect on citizens’ incomes, commodity prices, or the stability of the macroeconomic system. Finally, the function principle and potential risks of DIM are explained from a systems perspective, which offers a novel perspective for the functional design of CBDC and highlights that the hierarchical structure is a meaningful domain as the developmental direction. Show more

Keywords: Central bank digital currency, agent-based modeling, dynamic issuance mechanism, system feedback, macroeconomic

DOI: 10.3233/JIFS-221244

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9053-9067, 2023

Authors: Fang, Jian | Lin, Xiaomei | Wu, Yue | An, Yi | Sun, Haoran

Article Type: Research Article

Abstract: As a deep learning network model, ResNet50 can effectively recognize facial expressions to a certain extent, but there are still problems such as insufficient extraction of local effective feature information and a large number of parameters. In this paper, we take ResNet50 as the basic framework to optimize and improve this network. Firstly, by analyzing the influence mechanism of the attention mechanism module on the network feature information circulation, the optimal embedding position of CBAM (Convolutional Block Attention Module) and SE modules in the ResNet50 network is thus determined to effectively extract local key information, and then the number of …model parameters is effectively reduced by embedding the depth separable module. To validate the performance of the improved ResNet50 model, the recognition accuracy reached 71.72% and 95.72% by ablation experiments using Fer2013 and CK+ datasets, respectively. We then used the trained model to classify the homemade dataset, and the recognition accuracy reached 92.86%. In addition, compared with the current more advanced methods, the improved ResNet50 network model proposed in this paper can maintain a balance between model complexity and recognition ability and can provide a technical reference for facial expression recognition research. Show more

Keywords: ResNet50, SE, CBAM, depth separability, lightweight

DOI: 10.3233/JIFS-230524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9069-9081, 2023

Authors: Tan, Simin | Zhang, Ling | Sheng, Yuhong

Article Type: Research Article

Abstract: This paper mainly discusses the extinction and persistent dynamic behavior of infectious diseases with temporary immunity. Considering that the transmission process of infectious diseases is affected by environmental fluctuations, stochastic SIRS models have been proposed, while the outbreak of diseases is sudden and the interference terms that affect disease transmission cannot be qualified as random variables. Liu process is introduced based on uncertainty theory, which is a new branch of mathematics for describing uncertainty phenomena, to describe uncertain disturbances in epidemic transmission. This paper first extends the classic SIRS model from a deterministic framework to an uncertain framework and constructs …an uncertain SIRS infectious disease model with constant input and bilinear incidence. Then, by means of Yao-Chen formula, α-path of uncertain SIRS model and the corresponding ordinary differential equations are obtained to introduce the uncertainty threshold function R 0 * as the basic reproduction number. Moreover, two equilibrium states are derived. A series of numerical examples show that the larger the value of R 0 * , the more difficult it is to control the disease. If R 0 * ≤ 1 , the infectious disease will gradually disappear, while if R 0 * > 1 , the infectious disease will develop into a local epidemic. Show more

Keywords: Uncertainty theory, SIRS epidemic model, basic reproduction number, asymptotic behavior

DOI: 10.3233/JIFS-223439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9083-9093, 2023

Authors: Yang, Chun | Sun, Wei | Li, Ningning

Article Type: Research Article

Abstract: In the past decade, people’s life is getting better and better, and the attention to sports competition is also increasing. In the current information age, sports and athletes’ data are very important, especially team football. In college, football coaches can use the data to analyze the situation of college football players and opposing players to better specify the corresponding tactics to win the game. However, at present, most of the data results need to be manually recorded and counted on the spot or after the game. In the process of statistics, Zhou Jing will inevitably have omissions and other problems. …For this problem, a method based on space-time graph convolution. In the process, machine vision and motion recognition methods are combined, and the joint movements of different football players are extracted through the pose estimation method to obtain motion recognition results. To ented the methods on the KTH dataset. The results showed that the football motion recognition using the research method reached 98% on the dataset, which significantly improved the accuracy of nearly 5% over the existing state-of-the-art methods. At the same time, the accuracy rate of football movements was less than 5%. This means that the research method can effectively identify football sports, and can be widely used in other fields, and promote the development of human movement recognition in human-computer interaction and smart city and other fields. Show more

Keywords: Space-time graph convolution, football teaching, motion recognition

DOI: 10.3233/JIFS-230890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9095-9108, 2023

Authors: Xie, Canrong | Wang, Jianjun | Wu, Zhiwen | Nie, Shaojun | Hu, Yichan | Huang, Sheng

Article Type: Research Article

Abstract: Machine learning (ML) has been applied in civil engineering to predict the compressive strength of concrete with high accuracy. In this paper, five boosting ensemble algorithms, i.e., XGBoost, AdaBoost, GBDT, LightGBM, and CatBoost, were used to predict the compressive strength of high-performance concrete (HPC). The models were evaluated using performance indicators such as R2 , root mean square error (RMSE), and mean absolute error (MAE). The results showed that the CatBoost model had the highest accuracy with a R2 (0.970) and a RMSE (2.916). The prediction accuracy of the model was increased through hyperparameter optimization, which got a higher …with a R2 (0.975) and a RMSE (2.863). Meanwhile, the SHapley Additive exPlanations (SHAP) method was used to explain the output results of the optimal model (CatBoost), which generated explainable insights that further revealed the complex relationship between the prediction model parameters. The results showed that AGE, W/B, and W/C had the most impact on high-performance concrete compressive strength (HPCCS) prediction, which was similar to the results of sensitivity analysis. This study provided a theoretical basis and technical guidance for developing the mix design of a new high-performance concrete (HPC) system. In the future, the interpretable results of the model output should be iteratively checked and validated in the actual laboratory in order to provide guidance for engineering practice. Show more

Keywords: High-Performance Concrete (HPC), compressive strength, machine learning, boosting algorithms, game theory

DOI: 10.3233/JIFS-231021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9109-9122, 2023

Authors: Du, Xianjun | Wu, Hailei

Article Type: Research Article

Abstract: Convolutional neural networks (CNNs) have made significant progress in the field of cloud detection in remote sensing images thanks to their powerful feature representation capabilities. Existing methods typically aggregate low-level features containing details and high-level features containing semantics to make full use of both features to accurately detect cloud regions. However, CNNs are still limited in their ability to reason about the relationships between features, while not being able to model context well. To overcome this problem, this paper designs a novel feature interaction graph convolutional network model that extends the feature fusion process of convolutional neural networks from Euclidean …space to non-Euclidean space. The algorithm consists of three main components: remote sensing image feature extraction, feature interaction graph reasoning, and high-resolution feature recovery. The algorithm constructs a feature interaction graph reasoning (FIGR) module to fully interact with low-level and high-level features and then uses a residual graph convolutional network to infer feature higher-order relationships. The network model effectively alleviates the problem of a semantic divide in the feature fusion process, allowing the aggregated features to fuse valuable details and semantic information. The algorithm is designed to better detect clouds with complex cloud layers in remote sensing images with complex cloud shape, size, thickness, and cloud-snow coexistence. Validated on publicly available 38-Cloud and SPARCS datasets and the paper’s own Landsat-8 cloud detection dataset with higher spatial resolution, the proposed method achieves competitive performance under different evaluation metrics. Code is available at https://github.com/HaiLei-Fly/CloudGraph . Show more

Keywords: Remote sensing image cloud detection, feature interaction, graph convolutional networks, image segmentation, interpretability

DOI: 10.3233/JIFS-223946

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9123-9139, 2023

Authors: Silva, Victor L. | de Menezes, José Maria P.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9141-9156, 2023

Authors: yang, Chen | Jinming, Liu | Jian, Mao

Article Type: Research Article

Abstract: The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order …to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method. Show more

Keywords: Blind source separation, Deep Focusing U-Net, Electromagnetic signals

DOI: 10.3233/JIFS-223568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 9157-9167, 2023