Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Sharmila Devi, J. | Balasubramanian, P.

Article Type: Research Article

Abstract: Milling seems to be the most extensively utilized production technology in modern manufacturing industries, and it plays a significant role. Chatter is a type of disturbance in the form of vibration that has a negative impact on machining operation. Chatter recognition utilizing sensor outputs is a hot topic in academia. Although some progress has indeed been documented utilizing various featurization techniques and ml techniques, conventional approaches have a number of limitations, including manual preparation and a huge dataset need. Although, these are widely being used to evaluate milling operations in terms of production efficiency & work piece surface quality,.they are …not suited for real applications due to their computing duration and require large data for training process. Therefore, in this study, three well-performing deep learning approaches such as LSTM, DTW, and Bi-LSTM are used to provide an effective way for monitoring and managing chatter in the milling processes with the Duplex 2205 material. Here, some of the parameters like acceleration is measured while the milling operation is taking place, and the measured acceleration value is processed using selected three DL techniques for identifying the presence of chatter and are tested to see which one performs the best. The Bi-LSTM outperformed other approaches in detecting chatter present, according to the data. Show more

Keywords: Bi-directional long short term memory, long short term memory, dynamic time warping, deep learning, acceleration, milling chatter detection

DOI: 10.3233/JIFS-221091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3647-3666, 2023

Authors: Wang, Yongguo | Bi, Xuewen | Zhang, Xinxin

Article Type: Research Article

Abstract: The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day …ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE % =1.68 %. Show more

Keywords: Photovoltaic systems, battery energy storage system, control method, prediction method, RBF neural network, experimental dataset

DOI: 10.3233/JIFS-221123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3667-3680, 2023

Authors: Nuhoho, Raphael Elimeli | Wenyu, Chen | Baffour, Adu Asare

Article Type: Research Article

Abstract:  As digital image acquisition becomes ubiquitous in recent years, the need for indoor scene recognition becomes more pronounced. Existing methods leverage the features of composing objects in a scene and overlook the adverse impacts of the common objects reoccurring in other scenes. This drawback decreases the feature discrimination between scenes (e.g., living room, dining room, and bedroom) due to reoccurring objects (e.g., tables, chairs, and toys). We propose a method of training convolutional networks by punishing or discounting the local object representations’ predictive ability and encouraging the network to learn global scene layout representations. To retain more vital information for …the scene feature representation, we introduce an activation function (with unbounded above, bounded below, smooth, and non-monotonic properties) to allow more low-negative values to flow through the network, discarding high negative values. We evaluate the proposed methods on MIT Indoor 67 and Scene 15 datasets. The experiment findings show that the proposed methods capture global scene concepts and improve performance. Show more

Keywords: Indoor scene recognition, feature representation, activation functions, convolutional neural network

DOI: 10.3233/JIFS-221975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3681-3693, 2023

Authors: Meziani, Ahlem | Bourouis, Abdelhabib | Chebout, Mohamed Sedik

Article Type: Research Article

Abstract: Effective risk management reaction improves the absorption of critical impacts on supply chains. Supply chain risk (SCR) sources, like control, process, demand, and supply, need to be identified, assessed, and mitigated to make rational decisions immediately. Late detection of a disruptive event can cause delays in handling risk. Since SCRs consist of complex, uncertain, and incomplete information, most of the provided risk assessment mechanisms cannot handle it in real-time. Hence, in this paper, we introduce NeutroMAS4SCRM, a framework incorporating real-time Multi-Agent Systems (MAS) with Neutrosophic Data Analytic Hierarchy Processes to best deal with the complexity, uncertainty, and vagueness of SCR …management-related issues and which can hence help decision-makers adopt less risky decisions. In addition, the MAS technology contribution to SCR management is outlined through a comparative study among the most recent studies. In contrast, the proposed MAS for the supply chain is implemented under the JADE agent platform, where the FIPA-ACL-based message content is specified using a dedicated ontology. A simulation-based decision support system is used to assess the cost risk and its harmful effects and determine how well the proposed framework can help companies manage risks efficiently. The simulation has proven to reduce risk costs by about 85%. Show more

Keywords: Supply chain risk management, single-valued neutrosophic set, neutrosophic DAHP, multi agent system, simulation

DOI: 10.3233/JIFS-222305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3695-3716, 2023

Authors: Lakshmi Narayanan, S. | Ignatia, K. Majella Jenvi | Alfurhood, Badria Sulaiman | Bhat, Nagaraj

Article Type: Research Article

Abstract: A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to …other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques. Show more

Keywords: Feature extraction, feature selection, classification, texture analysis

DOI: 10.3233/JIFS-222481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3717-3731, 2023

Authors: Huong, Trieu Thu | Lan, Luong Thi Hong | Giang, Nguyen Long | Binh, NguyenThi My | Vo, Bay | Son, Le Hoang

Article Type: Research Article

Abstract:  Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer’s disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system’s increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment …from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. Show more

Keywords: Complex fuzzy set, mamdani complex fuzzy inference system, transfer learning, fuzzy transfer learning, complex fuzzy transfer learning

DOI: 10.3233/JIFS-222582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3733-3750, 2023

Authors: Wu, Guanghua | Li, Hongsheng | Li, Hongyu | Guo, Shiping | Ma, Wenjian | Dong, Jing

Article Type: Research Article

Abstract: The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation …is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value. Show more

Keywords: Grey correlation degree, fuzzy C-means clustering algorithm, business expansion newspaper installation, Optimal power supply parameters

DOI: 10.3233/JIFS-222926

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3751-3762, 2023

Authors: Zhang, Shasha | Liu, Xiaodi | Garg, Harish | Zhang, Shitao

Article Type: Research Article

Abstract: With the implementation and steady progress of the Belt and Road (B&R) initiative, China’s investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation …method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods. Show more

Keywords: Investment decision making, Distance, Correlation coefficient, Disappointment theory, Probabilistic hesitant fuzzy sets

DOI: 10.3233/JIFS-223059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3763-3786, 2023

Authors: Zhou, Tong | Zhang, Shuai | Zhang, Dongping | Chan, Verner | Yang, Sihan | Chen, Mengjiao

Article Type: Research Article

Abstract: With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to …ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model. Show more

Keywords: Electric vehicle routing problem, multi-objective optimization, collaborative multi-heterogeneous-depot, mixed time windows, nondominated sorting genetic algorithm-II

DOI: 10.3233/JIFS-223298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3787-3805, 2023

Authors: Li, Shiyong | Li, Wenzhe | Sun, Wei | Liu, Jia

Article Type: Research Article

Abstract: The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of …deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme. Show more

Keywords: Cloud deployment, inelastic applications, resource allocation, nonconvex optimization, PSO

DOI: 10.3233/JIFS-201239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 3807-3823, 2023