Special lssues
Table of Content

Development and Industrial Application of AI Technologies

Submission Deadline: 15 October 2023 (closed)

Guest Editors

Dr. Yunbo Rao, University of Electronic Science and Technology of China, China.
Prof. Yadong Wu, Sichuan University of Science and Technology, China.
Prof. Zhihan Lv, Uppsala University, Sweden.

Summary

Artificial Intelligence (AI) is an interdisciplinary and emerging discipline based on computer science and can be integrated with computer, psychology, philosophy and other disciplines. It is a new technological science that researches and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Research in this field includes computer vision, machine learning, robots, language recognition, image recognition, natural language processing, biometric recognition technology and expert systems.


With the further development of AI research and application, the deficiencies of the current mainstream AI technology in "big data, big computing power, and deep learning" are becoming more and more obvious, the development speed of the intelligence industry has slowed down, and the interpretability, reliability, and evolvability required for complex open scene applications are difficult to meet, and new exploration and breakthroughs are urgently needed. The development of AI come to a new stage. The research and application of AI has entered the deep-water area, which is full of opportunities and challenges.


With the advent of the digital era, artificial intelligence has been widely used in various areas, especially in smart manufacturing, smart home, industrial manufacturing, smart finance, smart medicalcare, smart education, smart security, smart logistics, smart transportation, smart aviation, smart retail, smart education and so on.


What is the trend of the AI industry? On the one hand, the current employment situation is caused by the impact of the whole field of the Internet, the bottleneck of deep learning itself in some aspects, and the limitations of application scenarios. On the other hand, there will be more demand in high-end fields in the future, such as intelligent manufacturing, electronic manufacturing and new energy manufacturing.


Therefore, AI will benefit many industries and fields in the future. This special issue aims to solicit contributions from scholars in the research direction of AI and collect the application and technological innovation of AI in different industries. This Special Issue will also collect the best paper from PRAI2023: http://www.prai.net.


Topics applicable to this special issue include but are not limited to:   

  • Robot Perception and Intelligent Interaction

  • Image Analysis and Smart Healthcare

  • Intelligent Manufacturing and Autonomous Drive

  • Intelligent Recommendation and Text Analysis

  • Speech, Signal and Video Processing

  • Metaverse, Virtual Reality and Augmented Reality

  • Quantum Computing and Applications

  • Internet of Things and Applications

  • Machine Learning, Privacy Computing and Federated Learning

  • Integration and Application of Unmanned Systems


Keywords

Robot Perception, Intelligent Interaction, Image Analysis, Smart Healthcare, Intelligent Manufacturing, Autonomous Drive, Intelligent Recommendation, Signal and Video Processing, Metaverse, Virtual Reality and Augmented Reality, Quantum Computing, Internet of Things, Privacy Computing and Federated Learning

Published Papers


  • Open Access

    ARTICLE

    Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

    Yuejiao Wang, Zhong Ma, Chaojie Yang, Yu Yang, Lu Wei
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047108
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

  • Open Access

    ARTICLE

    Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

    Jiao Wang, Bin Wu, Hongying Zhang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.046011
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >

  • Open Access

    ARTICLE

    Learning Epipolar Line Window Attention for Stereo Image Super-Resolution Reconstruction

    Xue Li, Hongying Zhang, Zixun Ye, Xiaoru Huang
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2847-2864, 2024, DOI:10.32604/cmc.2024.047093
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Transformer-based stereo image super-resolution reconstruction (Stereo SR) methods have significantly improved image quality. However, existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information. To address these challenges, this paper introduces a novel epipolar line window attention stereo image super-resolution network (EWASSR). For detail feature restoration, we design a feature extractor based on Transformer and convolutional neural network (CNN), which consists of (shifted) window-based self-attention ((S)W-MSA) and feature distillation and enhancement blocks (FDEB). This combination effectively solves the problem of global… More >

  • Open Access

    ARTICLE

    An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

    Zhaolin Lv, Yuexia Zhao, Hongyue Kang, Zhenyu Gao, Yuhang Qin
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2337-2360, 2024, DOI:10.32604/cmc.2023.045826
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Flexible job shop scheduling problem (FJSP) is the core decision-making problem of intelligent manufacturing production management. The Harris hawk optimization (HHO) algorithm, as a typical metaheuristic algorithm, has been widely employed to solve scheduling problems. However, HHO suffers from premature convergence when solving NP-hard problems. Therefore, this paper proposes an improved HHO algorithm (GNHHO) to solve the FJSP. GNHHO introduces an elitism strategy, a chaotic mechanism, a nonlinear escaping energy update strategy, and a Gaussian random walk strategy to prevent premature convergence. A flexible job shop scheduling model is constructed, and the static and dynamic FJSP is investigated to minimize… More >

  • Open Access

    ARTICLE

    Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

    Kwok Tai Chui, Brij B. Gupta, Varsha Arya, Miguel Torres-Ruiz
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

  • Open Access

    ARTICLE

    Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

    Dan Xu, Jiale Ru, Jinlong Shi
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 85-104, 2024, DOI:10.32604/cmc.2023.045258
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Video salient object detection (VSOD) aims at locating the most attractive objects in a video by exploring the spatial and temporal features. VSOD poses a challenging task in computer vision, as it involves processing complex spatial data that is also influenced by temporal dynamics. Despite the progress made in existing VSOD models, they still struggle in scenes of great background diversity within and between frames. Additionally, they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration. We propose a multi-stream temporal enhanced network (MSTENet) to address these problems. It… More >

  • Open Access

    ARTICLE

    A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

    Kun Fang, Julong Pan, Lingyi Li, Ruihan Xiang
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 493-514, 2024, DOI:10.32604/cmc.2023.045008
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract With the widespread use of Internet of Things (IoT) technology in daily life and the considerable safety risks of falls for elderly individuals, research on IoT-based fall detection systems has gained much attention. This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection (Skip-DSCGAN) for fall detection. The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data. A semisupervised learning approach is adopted to train the model using only activities of daily living (ADL) data, which can avoid data imbalance problems. Furthermore, a quantile-based approach… More >

  • Open Access

    ARTICLE

    Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion

    Shi Qiu, Pengchang Zhang, Xingjia Tang, Zimu Zeng, Miao Zhang, Bingliang Hu
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3783-3800, 2023, DOI:10.32604/cmc.2023.042074
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history, science, culture, art and research. However, mainstream analytical methods are contacting and detrimental, which is unfavorable to the protection of cultural relics. This paper improves the accuracy of the extraction, location, and analysis of artifacts using hyperspectral methods. To improve the accuracy of cultural relic mining, positioning, and analysis, the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques. Firstly, region stitching algorithm based on the relative position of hyper spectrally collected data… More >

  • Open Access

    ARTICLE

    A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

    Wei Zhang, Ping He, Ting Li, Fan Yang, Ying Liu
    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these representations through the utilization of… More >

  • Open Access

    ARTICLE

    An IoT-Based Aquaculture Monitoring System Using Firebase

    Wen-Tsai Sung, Indra Griha Tofik Isa, Sung-Jung Hsiao
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2179-2200, 2023, DOI:10.32604/cmc.2023.041022
    (This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
    Abstract Indonesia is a producer in the fisheries sector, with production reaching 14.8 million tons in 2022. The production potential of the fisheries sector can be optimally optimized through aquaculture management. One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions. IoT technology can be applied to support a fish pond aquaculture monitoring system, especially for catfish species (Siluriformes), in real-time and remotely. One of the technologies that can provide this convenience is the IoT. The problem of this study is how to integrate IoT devices with Firebase’s cloud data system to… More >

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