Vol.131, No.3, 2022-Table of Contents
  • Analysis of Multi-AGVs Management System and Key Issues: A Review
  • Abstract Multiple Automatic Guided Vehicle (multi-AGVs) management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario, such as flexible manufacturing systems, warehouses, container terminals, etc. This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator. The multi-AGVs management problem is a multi-objective, multi-constraint combinatorial optimization problem, which depends on the types of application scenarios. This article classifies and compares the research papers on multi-AGVs management in detail. Firstly, according to… More
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  • Assessment of the Solid Waste Disposal Method during COVID-19 Period Using the ELECTRE III Method in an Interval-Valued q-Rung Orthopair Fuzzy Approach
  • Abstract As the quantity of garbage created every day rises, solid waste management has become the world’s most important issue. As a result, improper solid waste disposal and major sanitary issues develop, which are only detected after they have become dangerous. Due to the system’s lockdown during the COVID-19 pandemic, this scenario became much more uncertain. We are at the stage to develop and execute effective waste management procedures, as well as long-term policies and forward-thinking programmes that can work even in the most adverse of scenarios. We incorporate major solid waste (organic and inorganic solid wastes) approaches that actually perform… More
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  • Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning
  • Abstract Sentiment analysis task has widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data is initially cleaned to remove… More
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  • Nonlinear Response of Tunnel Portal under Earthquake Waves with Different Vibration Directions
  • Abstract Tunnel portal sections often suffer serious damage in strong earthquake events. Earthquake waves may propagate in different directions, producing various dynamic responses in the tunnel portal. Based on the Galongla tunnel, which is located in a seismic region of China, three-dimensional seismic analysis is conducted to investigate the dynamic response of a tunnel portal subjected to earthquake waves with different vibration directions. In order to simulate the mechanic behavior of slope rock effectively, an elastoplastic damage model is adopted and applied to ABAQUS software by a self-compiled user material (UMAT) subroutine. Moreover, the seismic wave input method for tunnel portal… More
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  • A Method Based on Knowledge Distillation for Fish School Stress State Recognition in Intensive Aquaculture
  • Abstract Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture. Recent advances have been made in fish behavior analysis based on deep learning. However, most existing methods with top performance rely on considerable memory and computational resources, which is impractical in the real-world scenario. In order to overcome the limitations of these methods, a new method based on knowledge distillation is proposed to identify the stress states of fish schools. The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function, and it forces a lightweight network (GhostNet) to mimic… More
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  • Detecting and Repairing Data-Flow Errors in WFD-net Systems
  • Abstract Workflow system has become a standard solution for managing a complex business process. How to guarantee its correctness is a key requirement. Many methods only focus on the control-flow verification, while they neglect the modeling and checking of data-flows. Although some studies are presented to repair the data-flow errors, they do not consider the effect of delete operations or weak circulation relations on the repairing results. What's more, repairing some data-flow errors may bring in new errors. In order to solve these problems, we use workflow net with data (WFD-net) systems to model and analyze a workflow system. Based on… More
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  • User Role Discovery and Optimization Method Based on K-means++ and Reinforcement Learning in Mobile Applications
  • Abstract With the widespread use of mobile phones, users can share their location and activity anytime, anywhere, as a form of check-in data. These data reflect user features. Long-term stability and a set of user-shared features can be abstracted as user roles. This role is closely related to the users’ social background, occupation, and living habits. This study makes four main contributions to the literature. First, user feature models from different views for each user are constructed from the analysis of the check-in data. Second, the K-means algorithm is used to discover user roles from user features. Third, a reinforcement learning… More
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  • Efficient Data Augmentation Techniques for Improved Classification in Limited Data Set of Oral Squamous Cell Carcinoma
  • Abstract Deep Learning (DL) techniques as a subfield of data science are getting overwhelming attention mainly because of their ability to understand the underlying pattern of data in making classifications. These techniques require a considerable amount of data to efficiently train the DL models. Generally, when the data size is larger, the DL models perform better. However, it is not possible to have a considerable amount of data in different domains such as healthcare. In healthcare, it is impossible to have a substantial amount of data to solve medical problems using Artificial Intelligence, mainly due to ethical issues and the privacy… More
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  • A Numerical Modelling Method of Fractured Reservoirs with Embedded Meshes and Topological Fracture Projection Configurations
  • Abstract Projection-based embedded discrete fracture model (pEDFM) is an effective numerical model to handle the flow in fractured reservoirs, with high efficiency and strong generalization of flow models. However, this paper points out that pEDFM fails to handle flow barriers in most cases, and identifies the physical projection configuration of fractures is a key step in pEDFM. This paper presents and proves the equivalence theorem, which explains the geometric nature of physical projection configurations of fractures, that is, the projection configuration of a fracture being physical is equivalent to it being topologically homeomorphic to the fracture, by analyzing the essence of… More
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  • Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam
  • Abstract Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best… More
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  • Fixed-Time Adaptive Time-Varying Matrix Projective Synchronization of Time-Delayed Chaotic Systems with Different Dimensions
  • Abstract This paper deals with the fixed-time adaptive time-varying matrix projective synchronization (ATVMPS) of different dimensional chaotic systems (DDCSs) with time delays and unknown parameters. Firstly, to estimate the unknown parameters, adaptive parameter updated laws are designed. Secondly, to realize the fixed-time ATVMPS of the time-delayed DDCSs, an adaptive delay-unrelated controller is designed, where time delays of chaotic systems are known or unknown. Thirdly, some simple fixed-time ATVMPS criteria are deduced, and the rigorous proof is provided by employing the inequality technique and Lyapunov theory. Furthermore, the settling time of fixed-time synchronization (Fix-TS) is obtained, which depends only on controller parameters… More
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  • Degenerate s-Extended Complete and Incomplete Lah-Bell Polynomials
  • Abstract Degenerate versions of special polynomials and numbers applied to social problems, physics, and applied mathematics have been studied variously in recent years. Moreover, the (s-)Lah numbers have many other interesting applications in analysis and combinatorics. In this paper, we divide two parts. We first introduce new types of both degenerate incomplete and complete s-Bell polynomials respectively and investigate some properties of them respectively. Second, we introduce the degenerate versions of complete and incomplete Lah-Bell polynomials as multivariate forms for a new type of degenerate s-extended Lah-Bell polynomials and numbers respectively. We investigate relations between these polynomials and degenerate incomplete and… More
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  • Unidirectional Identity-Based Proxy Re-Signature with Key Insulation in EHR Sharing System
  • Abstract The introduction of the electronic medical record (EHR) sharing system has made a great contribution to the management and sharing of healthcare data. Considering referral treatment for patients, the original signature needs to be converted into a re-signature that can be verified by the new organization. Proxy re-signature (PRS) can be applied to this scenario so that authenticity and nonrepudiation can still be insured for data. Unfortunately, the existing PRS schemes cannot realize forward and backward security. Therefore, this paper proposes the first PRS scheme that can provide key-insulated property, which can guarantee both the forward and backward security of… More
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  • Partitioning of Water Distribution Network into District Metered Areas Using Existing Valves
  • Abstract Water distribution network (WDN) leakage management has received increased attention in recent years. One of the most successful leakage-control strategies is to divide the network into District Metered Areas (DMAs). As a multi-staged technique, the generation of DMAs is a difficult task in design and implementation (i.e., clustering, sectorization, and performance evaluation). Previous studies on DMAs implementation did not consider the potential use of existing valves in achieving the objective. In this work, a methodology is proposed for detecting clusters and reducing the cost of additional valves and DMA sectorization by considering existing valves as much as possible. The procedure… More
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  • Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation
  • Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics pallet segmentation. To better predict… More
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  • A Triple Human-Digital Twin Architecture for Cyber-Physical Systems
  • Abstract With the development of information and communication technology and the advent of the Internet of Things (IoT) era, cyber-physical system (CPS) is becoming the trend of products or systems. The deep integration and real-time interaction between the physical world and the virtual world expand system functions. Although there are some CPS implementation guidelines, the virtual world is still relatively abstract compared to the concrete physical world that can be touched through the IoT. Besides that, human is a non-negligible CPS endogenous interactive intelligent component. In this paper, we propose a triple human-digital twin architecture, where the physical objects and the… More
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  • Deep-Learning-Based Production Decline Curve Analysis in the Gas Reservoir through Sequence Learning Models
  • Abstract Production performance prediction of tight gas reservoirs is crucial to the estimation of ultimate recovery, which has an important impact on gas field development planning and economic evaluation. Owing to the model’s simplicity, the decline curve analysis method has been widely used to predict production performance. The advancement of deep-learning methods provides an intelligent way of analyzing production performance in tight gas reservoirs. In this paper, a sequence learning method to improve the accuracy and efficiency of tight gas production forecasting is proposed. The sequence learning methods used in production performance analysis herein include the recurrent neural network (RNN), long… More
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  • Study on the Mechanical Properties of Ni-Ti-Cu Shape Memory Alloy Considering Different Cu Contents
  • Abstract By adding copper to increase the performance, the Ni-Ti-Cu Shape Memory Alloy (SMA), has been widely used in the field of engineering in recent years. A thermodynamic constitutive model for Ni-Ti-Cu SMA considering different copper contents is established in this work. Numerical results for two different copper contents, as examples, are compared with the experimental results to verify the accuracy of the theoretical work. Based on the verified constitutive model, the effects of different copper content on the mechanical properties of Ni-Ti-Cu SMA and the tensile and compressive asymmetric properties of Ni-Ti-Cu SMA are finally discussed, respectively. More
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  • A Cell-Based Linear Smoothed Finite Element Method for Polygonal Topology Optimization
  • Abstract The aim of this work is to employ a modified cell-based smoothed finite element method (S-FEM) for topology optimization with the domain discretized with arbitrary polygons. In the present work, the linear polynomial basis function is used as the weight function instead of the constant weight function used in the standard S-FEM. This improves the accuracy and yields an optimal convergence rate. The gradients are smoothed over each smoothing domain, then used to compute the stiffness matrix. Within the proposed scheme, an optimum topology procedure is conducted over the smoothing domains. Structural materials are distributed over each smoothing domain and… More
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  • Modeling and Experimental Study for Automotive Dry Clutch Sliding Noise
  • Abstract Automotive dry clutches have been found to produce a low frequency sliding noise in many applications, which challenges the ride comfort of vehicles. In order to study this clutch sliding noise, a detailed finite element model including both a pressure plate assembly and a driven plate assembly was developed. Based on this model, a complex eigenvalue analysis is performed in this research. The effect of several major factors on the clutch sliding noise, such as the coefficient of friction, the clamping force, the geometric imperfection of the friction plate, and the thermal deformation of the pressure plate, were investigated numerically.… More
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  • Numerical Simulation of Reiner–Rivlin Nanofluid Flow under the Influence of Thermal Radiation and Activation Energy over a Rotating Disk
  • Abstract In current study, the numerical computations of Reiner–Rivlin nanofluid flow through a rotational disk under the influence of thermal radiation and Arrhenius activation energy is considered. For innovative physical situations, the motile microorganisms are incorporated too. The multiple slip effects are considered in the boundary conditions. The bioconvection of motile microorganism is utilized alongside nanofluids to provide stability to enhanced thermal transportation. The Bioconvection pattern in various nanoparticles accredits novel applications of biotechnology like the synthesis of biological polymers, biosensors, fuel cells, petroleum engineering, and the natural environment. By deploying some suitable similarity transformation functions, the governing partial differential equations… More
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  • A Personalized Comprehensive Cloud-Based Method for Heterogeneous MAGDM and Application in COVID-19
  • Abstract This paper proposes a personalized comprehensive cloud-based method for heterogeneous multi-attribute group decision-making (MAGDM), in which the evaluations of alternatives on attributes are represented by LTs (linguistic terms), PLTSs (probabilistic linguistic term sets) and LHFSs (linguistic hesitant fuzzy sets). As an effective tool to describe LTs, cloud model is used to quantify the qualitative evaluations. Firstly, the regulation parameters of entropy and hyper entropy are defined, and they are further incorporated into the transformation process from LTs to clouds for reflecting the different personalities of decision-makers (DMs). To tackle the evaluation information in the form of PLTSs and LHFSs, PLTS… More
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