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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Lei, Fan | Cai, Qiang | Wang, Hongjun | Wei, Guiwu | Mo, Zhiwen
Article Type: Research Article
Abstract: Urban fire accident is a common dangerous accident in urban sudden accidents, which threatens the safety of people’s lives and property. For this reason, in recent years, all cities have incorporated the prevention and emergency management of urban fire accidents into their urban development planning, and actively improved their fire accident emergency management capabilities. However, how to evaluate the urban fire accident emergency management capacity of each city to ensure that people’s lives and property are protected to the greatest extent is an urgent problem to be considered and solved. Therefore, this paper defines a class of probabilistic double hierarchy …linguistic Heronian mean (PDHLHM) operator, probabilistic double hierarchy linguistic Power Heronian mean (PDHLPHM) operators, and their dual operators that can reflect the relationship between two attributes during aggregation. Taking urban fire accident risk monitoring and early warning capability, fire infrastructure and communication system, fire-fighting and rescue capability, recovery and reconstruction capability as evaluation attributes, the probabilistic double hierarchy linguistic weight Power Heronian mean (PDHLWPHM) operator model and the probabilistic double hierarchy linguistic weight Power geometric Heronian mean (PDHLWPGHM) operator model are constructed for group decision-making. In addition, the idempotence, boundedness and monotonicity of these operators are studied, and the sensitivity of the parameters involved in the operator model is analyzed. Finally, the new model proposed in this paper is compared with the existing model to verify its scientificity. Show more
Keywords: Group decision-making, probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWPHM operator and PDHLWPGHM operator, urban fire emergency management capability
DOI: 10.3233/JIFS-230485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3713-3760, 2024
Authors: Zhao, Chen | Sun, Lijun | Li, Gang | Tang, Yiming
Article Type: Research Article
Abstract: Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.
Keywords: Relevancy transformation operators, Polynomial functions, Fuzzy systems, Monotonicity
DOI: 10.3233/JIFS-231017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3761-3771, 2024
Authors: Wang, Jiaguo | Li, Wenheng | Lei, Chao | Yang, Meng | Pei, Yang
Article Type: Research Article
Abstract: Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, …dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short. Show more
Keywords: Actor-critic, deep reinforcement learning, intelligent agent, iterative learning
DOI: 10.3233/JIFS-233747
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3773-3788, 2024
Authors: Mukesh Krishnan, M. | Thanga Ramya, S. | Ramar, K.
Article Type: Research Article
Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and …the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods. Show more
Keywords: Unusual event detection, crowd detection, crowd clustering, and unusual crowd activities model
DOI: 10.3233/JIFS-233833
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3789-3798, 2024
Authors: Li, Tao | Wang, Xiaolong | Li, Xinkun | Jia, Xinyu | Wu, Lijie | Yang, Weihong
Article Type: Research Article
Abstract: Tunnel stability is mainly concerned with the object of symmetric tunnels, shallow buried unsymmetric(SBU) tunnels should also be emphasized as the focus of the computational analysis of tunnel engineering. It is especially important to solve the expressions of ultimate support force and damage surface function for SBU tunnels. In this paper, considering the effect of unsymmetrical action, based on the Hoke-Brown(H-B) damage criterion, the optimal upper bound(UB) solution expression is derived by using the limit analysis method. The expression can be used to express the support force and collapse pattern of a SBU rectangular tunnel. The results show that q …1 and q 2 decrease with the increase of parameters A and σ c , and increase with the increase of parameters B , γ , and h . q 1 increases with the increase of α , and vice versa for q 2 . The range of damage surface decreases with increasing parameter A , σ c and increases with increasing parameter B , γ , d , h . After the feasibility study and results analysis, it is concluded that the results obtained in this study are consistent with common engineering knowledge. The training results using Feedforward neural network verify the feasibility of the method for SBU tunnels and can be generalized for shallow buried(SB) symmetrical tunnels. The proposed method can provide a theoretical basis for the support design of SBU tunnels. Show more
Keywords: Shallow buried unsymmetrical tunnels, Hoke-Brown criterion, collapsed rock mass, limit analysis method
DOI: 10.3233/JIFS-234766
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3799-3809, 2024
Authors: Arulkumar, V. | Sandana Karuppan, A. | Alex, Sini Anna | Lathamanju, R.
Article Type: Research Article
Abstract: In an era marked by the widespread adoption of cloud services, individuals and businesses face the daunting task of navigating a complex landscape to make informed choices. The inherent opacity of the cloud service environment underscores the need for methods that can effectively handle imprecise information. This research presents a novel and superior approach to aid customers in selecting the most suitable cloud services. Our work introduces a distinctive fuzzy decision-making paradigm, surpassing current methodologies. We leverage an innovative analytic hierarchy process technique to quantify the semantic similarity between concepts and employ a fuzzy ontology to elucidate the uncertain relationships …among database items, facilitating precise service matching. Furthermore, we present a multi-faceted evaluation framework for ranking cloud services. To substantiate the efficacy of our similarity matching based on the fuzzy ontology, we conduct comprehensive testing. The results of our experiments provide compelling evidence of the viability and effectiveness of the proposed method. This research offers a valuable contribution to the challenging realm of cloud service selection, empowering individuals and organizations to make well-informed decisions amidst the cloud service abundance. Show more
Keywords: Semantic information retrieval, fuzzy ontology, ontology, the Semantic Web
DOI: 10.3233/JIFS-235130
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3811-3826, 2024
Authors: Cheng, Long | Wang, Lei | Cai, Jingcao
Article Type: Research Article
Abstract: For solving the distributed assembly flow shop scheduling problem with fuzzy processing time (FDAPFSP), a regional biogeography-based optimization algorithm (RBBO) is proposed to minimize the maximum fuzzy completion time. The mathematical model is provided. In RBBO, all habitats are divided into regions based on the habitat suitability index, and the habitats of each region are subject to cross-regional migration and replacement procedures. A critical factory optimization strategy is developed to enhance local search capability. Taguchi method is used to determine the parameters of RBBO. In ten FDAPFSP instances, comparative testing of RBBO algorithm with various heuristic and swarm intelligence algorithms …are conducted. The computation results show that in ten FDAPFSP cases, the proposed RBBO outperforms other algorithms in nine out of ten FDAPFSP cases. Show more
Keywords: Fuzzy scheduling, distributed scheduling, permutation flow-shop, regional biogeography-based optimization algorithm
DOI: 10.3233/JIFS-235854
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3827-3841, 2024
Authors: Vidhya, K. | Krishnamoorthi, K.
Article Type: Research Article
Abstract: In this manuscript, a hybrid approach is proposed for multi-functional grid connected photovoltaic (PV) interleaved inverter using power quality(PQ) enhancement. The proposed method is the integration of Spherical Evolution Search Algorithm (LSE) and Wild Horse Optimizer (WHO), thus it is called LSE-WHO method. The key objective of the proposed method lessens the DC voltage fluctuation and enhances the PQ. At the grid side, the interleaved inverter is used and it consists of 4 legs and every leg has a power electronic switch and a diode. Because of the structure of interleaved inverter, the shoot-through effect overcomes. The system performance is …improved by the utilization of interleaved inverter. The operation of proposed method is divided into 2 parts, like harmonics reduction and power harvesting. The LSE method is used to improve the maximal power of photovoltaic and the WHO method is used to lessen the harmonics distortion and eliminated the DC-link voltage fluctuation by double band hysteresis current controller (DBHCC). The switching losses are low because the DBHCC gives lesser switching frequency. Then, the LSE-WHO method is done in MATLAB, and its performance is compared to the existing methods. From the simulation, it conclude that the LSE-WHO method provides the THD as 2.12% and improves the PQ. Show more
Keywords: Power quality, power harvesting, grid connected PV, interleaved inverter, DC voltage fluctuation, switching losses, harmonic
DOI: 10.3233/JIFS-221561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3843-3865, 2024
Authors: Al-Essa, Laila A. | Khan, Zahid | Alduais, Fuad S.
Article Type: Research Article
Abstract: The logistic distribution is frequently encountered to model engineering, industrial, healthcare and other wide range of scientific data. This work introduces a flexible neutrosophic logistic distribution (LDN ) constructed using the neutrosophic framework. The LDN is considered to be ideal for evaluating and quantifying the uncertainties included in processing data. The suggested distribution offers greater flexibility and superior fit to numerous commonly used metrics for assessing survival, such as the hazard function, reliability function, and survival function. The mode, skewness, kurtosis, hazard function, and moments of the new distribution are established to determine its properties. The theoretical findings are …experimentally proven by numerical studies on simulated data. It is observed that the suggested distribution provides a better fit than the conventional model for data involving imprecise, vague, and fuzzy information. The maximum likelihood technique is explored to estimate the parameters and evaluate the performance of the method for finite sample sizes under the neutrosophic context. Finally, a real dataset on childhood mortality rates is considered to demonstrate the implementation methodology of the proposed model. Show more
Keywords: Uncertain data, neutrosophic probability, neutrosophic distribution, uncertain estimators, Monte Carlo simulation
DOI: 10.3233/JIFS-233357
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3867-3880, 2024
Authors: Liu, Yicheng | Hu, Zewei | Nie, Haiwen
Article Type: Research Article
Abstract: With the rapid economic development and high concentration of urban population, people’s income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using …the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved. Show more
Keywords: Deep learning, residual networks, public places, crowd recognition, clustering
DOI: 10.3233/JIFS-236811
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3881-3893, 2024
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