Chengwan Zhang¹, Kai Long^1,*, Zhuo Chen^1,2, Xiaoyu Yang¹, Feiyu Lu¹, Jinhua Zhang³, Zunyi Duan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 369-390, 2024, DOI:10.32604/cmes.2023.029876

Abstract This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials. The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass, as well as the local volume fraction of all phases. The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function, avoiding the parameter dependence in the conventional aggregation process. Furthermore, the local volume percentage can be precisely satisfied. The effects including the global mass bound, the influence radius and local volume percentage… More >

Xiaofang Kang^1,*, Jian Wu¹, Xinqi Wang¹, Shancheng Lei²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 551-593, 2024, DOI:10.32604/cmes.2023.029044

Abstract In order to improve the seismic performance of adjacent buildings, two types of tuned inerter damper (TID) damping systems for adjacent buildings are proposed, which are composed of springs, inerter devices and dampers in serial or in parallel. The dynamic equations of TID adjacent building damping systems were derived, and the H₂ norm criterion was used to optimize and adjust them, so that the system had the optimum damping performance under white noise random excitation. Taking TID frequency ratio and damping ratio as optimization parameters, the optimum analytical solutions of the displacement frequency response of the undamped structure under white… More > Graphic Abstract

Gagan Deep^*, Jyoti Verma

Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329

Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information on the integration of AI… More >

Juanjuan Wang^*, Jiangping Nan, Yanan Wang

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.11, pp. 2761-2775, 2023, DOI:10.32604/fdmp.2023.021175

Abstract The main objective of this study is the technical optimization of a Shell-and-Tube Heat Exchanger (STHE). In order to do so, a simulation model is introduced that takes into account the related gas-phase circulation. Then, simulation verification experiments are designed in order to validate the model. The results show that the temperature field undergoes strong variations in time when an inlet wind speed of 6 m/s is considered, while the heat transfer error reaches a minimum of 5.1%. For an inlet velocity of 9 m/s, the heat transfer drops to the lowest point, while the heat transfer error reaches a… More >

Tianshu Wang¹, Guoqing Sun¹, Shaojun Zhang¹, Mingyu Wang¹, Jixin Wang², Yunwu Han^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.11, pp. 2923-2932, 2023, DOI:10.32604/fdmp.2023.028740

Abstract In typical metal foundry applications, sand casting is still the most used technology. The related binder plays a very important role as its performances can directly influence the quality of castings. Among many binders, glues of animal origin have attracted much attention in recent years due to their reduced environmental impact. However, they display some drawbacks such as the tendency to coagulate easily at room temperature and a relatively low strength. In this study, a novel gas-hardening casting binder was prepared using an animal glue and anhydrous potassium carbonate as a hydrolyzing agent to avoid undesired agglomeration. Moreover, sodium pyrophosphate… More >

Yaofei Zhang¹, Guoxiang Li¹, Shuzhan Bai¹, Ke Sun^1,*, Guihua Wang^1,*, Yujie Jia², Zhengxian Fang²

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.11, pp. 2933-2951, 2023, DOI:10.32604/fdmp.2023.028804

Abstract Multiphase simulations based on the VOF (Volume of Fluid) approach, used in synergy with the cavitation Schnerr-Sauer method and the K-Epsilon turbulence model, have been conducted to study the behavior of an injector nozzle as a function of relevant structural parameters (such as the spray hole diameter and length). The related performances have been optimized in the framework of orthogonal experimental design and range analysis methods. As made evident by the results, as the spray hole diameter increases from 0.10 to 0.20 mm, the outlet mass flow rate grows by 243.23%. A small diameter of the spray hole, however, has… More >

Trong-The Nguyen^1,2,3, Thi-Kien Dao^1,2,3,*, Trinh-Dong Nguyen^2,3

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3213-3234, 2023, DOI:10.32604/iasc.2023.041356

Abstract Wireless sensor networks (WSNs) are widely used for various practical applications due to their simplicity and versatility. The quality of service in WSNs is greatly influenced by the coverage, which directly affects the monitoring capacity of the target region. However, low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges. This study proposes an optimal node planning strategy for network coverage based on an adjusted single candidate optimizer (ASCO) to address these issues. The single candidate optimizer (SCO) is a metaheuristic algorithm with stable implementation procedures. However, it has limitations in avoiding local optimum traps in… More >

Nitin Mittal¹, Rohit Salgotra^2,3, Abhishek Sharma⁴, Sandeep Kaur⁵, S. S. Askar⁶, Mohamed Abouhawwash^7,8,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3159-3177, 2023, DOI:10.32604/iasc.2023.041059

Abstract The optimization of cognitive radio (CR) system using an enhanced firefly algorithm (EFA) is presented in this work. The Firefly algorithm (FA) is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies. It has already proved its competence in various optimization problems, but it suffers from slow convergence issues. To improve the convergence performance of FA, a new variant named EFA is proposed. The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions, and simulation results show its superior performance compared to biogeography-based optimization (BBO), bat algorithm, artificial bee colony, and FA. As an… More >

B. Keerthi Samhitha^*, R. Subhashini

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3125-3142, 2023, DOI:10.32604/iasc.2023.040643

Abstract Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management decisions about recirculating the aquaculture system while decreasing labor. The classic detection approach involves placing sensors on the skin or body of the fish, which may interfere with typical behavior and welfare. The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy. This study develops an intelligent fish behavior classification using… More >

Difeng Zhu¹, Zhimou Zhu², Xuan Gong¹, Demao Ye¹, Chao Li^3,*, Jingjing Chen^4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3083-3100, 2023, DOI:10.32604/iasc.2023.040517

Abstract Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them with the historical data to… More >