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Advanced Intelligent Decision and Intelligent Control with Applications in Smart City

Submission Deadline: 01 November 2022 (closed) View: 139

Guest Editors

Prof. Zhengtian Wu, Suzhou University of Science and Technology, China
Prof. Michael V. Basin, the Autonomous University of Nuevo Leon, Mexico
Prof. Qing Gao, Beihang University, China

Summary

The research on intelligent decision and intelligent control have promoted the development of smart city greatly. Studies on these area have attracted engineers and scientists from various disciplines such as control theory, mathematics, computer science,management, and so on. This Special Issue will feature recent developments of intelligent decision and intelligent control with applications in smart city. It aims to provide a platform for sharing recent results and team experience to contribute to the advancing of intelligent decision and intelligent control.

Potential topics include but are not limited to the following:


l  Combinatorial optimization of intelligent decision;

l  Emergency management;

l  Intelligent systems and control theory;

l  Intelligent unmmaned systems;

l  Advanced smart manufacturing;

l  Smart city and smart grid;

l  Intelligent Perceptual and Diagnosis of Equipment;

l  Computer vision and its industrial applications;

l  Integrated equipment design and validations;

l  Robot design, control, and applications;

l  Urban environment monitoring and emergency early warning;

l  Pollution control policies and their social effects;

l  Machine learning and deep learning.

Keywords

Intelligent decision; intelligent control; smart city; emergency management; smart manufacturing

Published Papers

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  • Open Access

    ARTICLE

    An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

    Jing Xin, Kenan Du, Jiale Feng, Mao Shan
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud More >

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    936

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    445

  • Open Access

    ARTICLE

    Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

    Qingyue Zhao, Qiaoyu Gu, Zhijun Gao, Shipian Shao, Xinyuan Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1773-1788, 2023, DOI:10.32604/cmes.2023.027500
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition. A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism (GLA) model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features. The network connects GCN and LSTM network in series, and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction, which fully excavates the temporal and spatial features of the skeleton sequence. Finally, More >

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    1680

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    605

  • Open Access

    ARTICLE

    High Utility Periodic Frequent Pattern Mining in Multiple Sequences

    Chien-Ming Chen, Zhenzhou Zhang, Jimmy Ming-Tai Wu, Kuruva Lakshmanna
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. More >

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    1048

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    587

  • Open Access

    ARTICLE

    Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

    Muwei Jian, Ronghua Wu, Hongyu Chen, Lanqi Fu, Chengdong Yang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of… More >

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    1495

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    772

  • Open Access

    ARTICLE

    Residential Energy Consumption Forecasting Based on Federated Reinforcement Learning with Data Privacy Protection

    You Lu, Linqian Cui, Yunzhe Wang, Jiacheng Sun, Lanhui Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 717-732, 2023, DOI:10.32604/cmes.2023.027032
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Most studies have conducted experiments on predicting energy consumption by integrating data for model training. However, the process of centralizing data can cause problems of data leakage. Meanwhile, many laws and regulations on data security and privacy have been enacted, making it difficult to centralize data, which can lead to a data silo problem. Thus, to train the model while maintaining user privacy, we adopt a federated learning framework. However, in all classical federated learning frameworks secure aggregation, the Federated Averaging (FedAvg) method is used to directly weight the model parameters on average, which may… More >

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    959

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    569

  • Open Access

    ARTICLE

    A Novel Detection Method for Pavement Crack with Encoder-Decoder Architecture

    Yalong Yang, Wenjing Xu, Yinfeng Zhu, Liangliang Su, Gongquan Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 761-773, 2023, DOI:10.32604/cmes.2023.027010
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract As a current popular method, intelligent detection of cracks is of great significance to road safety, so deep learning has gradually attracted attention in the field of crack image detection. The nonlinear structure, low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning. Therefore, an end-to-end deep convolutional neural network (AttentionCrack) is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels. The AttentionCrack network is built on U-Net based encoder-decoder architecture, and an attention mechanism is incorporated into the… More >

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    865

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    682

  • Open Access

    ARTICLE

    HVAC Optimal Control Based on the Sensitivity Analysis: An Improved SA Combination Method Based on a Neural Network

    Lifan Zhao, Zetian Huang, Qiming Fu, Nengwei Fang, Bin Xing, Jianping Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2741-2758, 2023, DOI:10.32604/cmes.2023.025500
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Aiming at optimizing the energy consumption of HVAC, an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis (SA), named the sensitivity analysis combination method (SAC). Based on the SA, neural network and the related settings about energy conservation of HVAC systems, such as cooling water temperature, chilled water temperature and supply air temperature, were optimized. Moreover, based on the data of the existing HVAC system, various optimal control methods of HVAC systems were tested and evaluated by a simulated HVAC system in TRNSYS. The results show that the proposed More >

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    1025

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    839

  • Open Access

    ARTICLE

    MAQMC: Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

    Zhengkai Ding, Qiming Fu, Jianping Chen, You Lu, Hongjie Wu, Nengwei Fang, Bin Xing
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2759-2785, 2023, DOI:10.32604/cmes.2023.026091
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract The optimization of multi-zone residential heating, ventilation, and air conditioning (HVAC) control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads. Deep reinforcement learning (DRL) methods have recently been proposed to address the HVAC control problem. However, the application of single-agent DRL for multi-zone residential HVAC control may lead to non-convergence or slow convergence. In this paper, we propose MAQMC (Multi-Agent deep Q-network for multi-zone residential HVAC Control) to address this challenge with the goal of minimizing energy consumption while maintaining occupants’ thermal comfort. MAQMC… More >

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    1615

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    667

  • Open Access

    ARTICLE

    Observer-Based Control for a Cable-Driven Aerial Manipulator under Lumped Disturbances

    Li Ding, Yong Yao, Rui Ma
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1539-1558, 2023, DOI:10.32604/cmes.2022.023003
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract With the increasing demand for interactive aerial operations, the application of aerial manipulators is becoming more promising. However, there are a few critical problems on how to improve the energetic efficiency and pose control of the aerial manipulator for practical application. In this paper, a novel cable-driven aerial manipulator used for remote water sampling is proposed and then its rigid-flexible coupling dynamics model is constructed which takes joint flexibility into account. To achieve high precision joint position tracking under lumped disturbances, a newly controller, which consists of three parts: linear extended state observer, adaptive super-twisting More >

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    1930

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    827

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    1

  • Open Access

    ARTICLE

    A Road Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network

    Fenglei Xu, Haokai Zhao, Fuyuan Hu, Mingfei Shen, Yifei Wu
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1559-1570, 2023, DOI:10.32604/cmes.2022.023217
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Convolutional neural networks (CNN) based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks. Recently, Transformer starts to lead new trends in the image segmentation task. Transformer layer can construct the relationship between all pixels, and the two parties can complement each other well. On the basis of these characteristics, we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both. The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution, in which skip connections are More >

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    1189

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    640

  • Open Access

    ARTICLE

    Optimal Joint Space Control of a Cable-Driven Aerial Manipulator

    Li Ding, Rui Ma, Zhengtian Wu, Rongzhi Qi, Wenrui Ruan
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 441-464, 2023, DOI:10.32604/cmes.2022.022642
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract This article proposes a novel method for maintaining the trajectory of an aerial manipulator by utilizing a fast nonsingular terminal sliding mode (FNTSM) manifold and a linear extended state observer (LESO). The developed control method applies an FNTSM to ensure the tracking performance’s control accuracy, and an LESO to estimate the system’s unmodeled dynamics and external disturbances. Additionally, an improved salp swarm algorithm (ISSA) is employed to parameter tune the suggested controller by integrating the salp swarm technique with a cloud model. This approach also uses a model-free scheme to reduce the complexity of controller More >

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    1229

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    1037

  • Open Access

    ARTICLE

    Robot Zero-Moment Control Algorithm Based on Parameter Identification of Low-Speed Dynamic Balance

    Saixuan Chen, Jie Yang, Guohua Cui, Fuzhou Niu, Baiqiang Yao, Yu Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2021-2039, 2023, DOI:10.32604/cmes.2022.022669
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract This paper proposes a zero-moment control torque compensation technique. After compensating the gravity and friction of the robot, it must overcome a small inertial force to move in compliance with the external force. The principle of torque balance was used to realise the zero-moment dragging and teaching function of the lightweight collaborative robot. The robot parameter identification based on the least square method was used to accurately identify the robot torque sensitivity and friction parameters. When the robot joint rotates at a low speed, it can approximately satisfy the torque balance equation. The experiment uses More >

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    1284

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    1051

  • Open Access

    ARTICLE

    Retrieval and Regional Distribution Analysis of Ammonia, Sulfur Dioxide and Nitrogen Dioxide in the Urban Environment Using Ultraviolet DOAS Algorithm

    Hao Chen, Jie Xu, Yibo Hu, Fuzhou Niu, Zhiyan Li, Dan Wang, Guizhong Fu, Chuanxin Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1251-1262, 2023, DOI:10.32604/cmes.2022.022279
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Aiming at the in situ and mobile observation of urban environmental air pollution, a portable instrument using ultraviolet spectrum retrieval algorithm was developed based on the basis of Differential Optical Absorption Spectroscopy (DOAS) and multiple-pass cell technique. Typical trace gas pollutants, NH3, SO2, and NO2, were explored using their optical spectral characteristics in deep ultraviolet wavelength range from 210 to 215 nm. The gas concentration was retrieved by Lambert-Beer's law and nonlinear least square method. With an optimized optical alignment, the detection limits of NH3, SO2, NO2 were estimated to be 2.2, 2.3, and 36.2 ppb, respectively. The More >

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    1019

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    726

  • Open Access

    ARTICLE

    Research on Leak Location Method of Water Supply Pipeline Based on MVMD

    Qiansheng Fang, Haojie Wang, Chenlei Xie, Jie Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1237-1250, 2023, DOI:10.32604/cmes.2022.021131
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract At present, the leakage rate of the water distribution network in China is still high, and the waste of water resources caused by water distribution network leakage is quite serious every year. Therefore, the location of pipeline leakage is of great significance for saving water resources and reducing economic losses. Acoustic emission technology is the most widely used pipeline leak location technology. The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters, ignoring periodic noise and components unrelated to pipeline leakage. Aiming at the above problems, this paper proposes a… More >

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    1784

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    987

  • Open Access

    ARTICLE

    A Fixed-Point Iterative Method for Discrete Tomography Reconstruction Based on Intelligent Optimization

    Luyao Yang, Hao Chen, Haocheng Yu, Jin Qiu, Shuxian Zhu
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 731-745, 2023, DOI:10.32604/cmes.2022.020656
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Discrete Tomography (DT) is a technology that uses image projection to reconstruct images. Its reconstruction problem, especially the binary image (0–1 matrix) has attracted strong attention. In this study, a fixed point iterative method of integer programming based on intelligent optimization is proposed to optimize the reconstructed model. The solution process can be divided into two procedures. First, the DT problem is reformulated into a polyhedron judgment problem based on lattice basis reduction. Second, the fixed-point iterative method of Dang and Ye is used to judge whether an integer point exists in the polyhedron of More >

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    1949

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    1050

  • Open Access

    ARTICLE

    Interpreting Randomly Wired Graph Models for Chinese NER

    Jie Chen, Jiabao Xu, Xuefeng Xi, Zhiming Cui, Victor S. Sheng
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771
    （This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
    Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify More >

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    1580

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    785

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