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Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021)

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 19930

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Special Issue Editors

Prof. Dr. Shengzong Zhou

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1. Federation of Chinese Professional Associations in Europe, Franz-Schubert-Weg 70, 61118 Bad Vilbel, Germany
2. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
Interests: computer software and theory; software engineering and computer application technology research
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Prof. Dr. Yudong Zhang

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Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: explainable deep learning; medical image analysis; pattern recognition and medical sensors; artificial intelligence; intelligent computing
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Special Issue Information

Dear Colleagues,

With the increased integration of advances in robotics and automatons, sensors, and high-speed computing, the AI and manufacturing industries are entering a period of substantial innovation and change. Over the past few decades, AI has played a vital role in the manufacturing industry, from big data to full automation. However, in today’s complex, competitive, and dynamic external environment, manufacturing in many countries is facing huge bottlenecks and challenges to further development.

The FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021) aims to bring together researchers and scientists from artificial intelligence and advanced manufacturing, as well as researchers from various application areas to discuss problems and solutions in the area, identify new issues, and shape future directions for research.

This Special Issue will focus on introducing a new generation of intelligent manufacturing systems and technologies and their applications, such as AI systems; smart industrial Internet of Things; machine learning and AI for intelligent manufacturing; smart factory and logistics; manufacturing processes and management; sustainable, flexible, virtual, digital manufacturing; and other related topics.

We invite you and your colleagues to submit a contribution in the form of an original scientific research article for this Special Issue. We would also like to thank the presenters and speakers in advance for your attendance to this conference and look forward to a stimulating discussion.

The conference AIAM2021 will be held in Manchester, UK between 23 and 25 October 2021.

Prof. Dr. Shengzong Zhou
Prof. Dr. Yudong Zhang
Guest Editors

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Keywords

advanced manufacturing
data mining
advanced materials
advanced system management technology
human–robot collaborative
sustainable manufacturing
automation
big data analytics in manufacturing
AI for manufacturing system optimization
AI for manufacturing process
internet + manufacturing
machine learning techniques for intelligent manufacturing
application of artificial intelligence in transportation, energy and bioengineering, etc.

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Published Papers (8 papers)

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Research

13 pages, 999 KiB

Open AccessArticle

Securing Remote State Estimation against Sequential Logic Attack of Sensor Data

by Jing Wang and Tao Feng

Appl. Sci. 2022, 12(4), 2259; https://doi.org/10.3390/app12042259 - 21 Feb 2022

Viewed by 1825

Abstract

The SCADA system, which is widely used in the continuous monitoring and control of the physical process of modern critical infrastructure, relies on the feedback control loop. The remote state estimation system triggers the control algorithm or control condition of the controller according to the monitoring data returned by the sensor. The controller sends the control command to the actuator, and the actuator executes the command to control the physical process. Since SCADA system monitoring and control data are usually transmitted through unprotected wireless communication networks, attackers can use false sensor data to trigger control algorithms to make wrong decisions, disrupt the physical processing of the SCADA system, and cause huge economic losses, even casualties. We found an attack strategy based on the sequential logic of sensor data. This kind of attack changes the time logic or sequence logic of the response data, so that the false data detector can be successfully deceived. This would cause the remote state estimation system to trigger wrong control algorithms or control conditions, and eventually disrupt or destroy the physical process. This paper proposes a sequential signature scheme based on the one-time signature to secure the sequential logic and transmission of sensor data. The security analysis proves that the proposed scheme can effectively resist counterfeiting, forgery, denial, replay attacks, and selective forwarding attacks. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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19 pages, 4933 KiB

Open AccessArticle

Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas

by Weifeng Zhou, Fei Wang, Xi Wang, Fenghua Tang and Jiasheng Li

Appl. Sci. 2022, 12(3), 1170; https://doi.org/10.3390/app12031170 - 23 Jan 2022

Cited by 5 | Viewed by 2456

Abstract

Image fusion of satellite sensors can generate a high-resolution multi-spectral image from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image for feature extraction and target recognition, such as enclosure seines and floating rafts. However, there is currently no clear and definite method of image fusion for different aquaculture areas distribution extraction from high-resolution satellite images. This study uses three types of high-resolution remote sensing images, GF-1 (Gaofen-1), GF-2 (Gaofen-2), and WV-2 (WorldView-2), covering the raft and enclosure seines aquacultures in the Xiangshan Bay, China, to evaluate panchromatic and multispectral image fusion techniques to determine which is the best. This study applied PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms to panchromatic and multispectral images fusion of GF-1, GF-2, and WV-2. Two quantitative methods are used to evaluate the fusion effect. The first used seven statistical parameters, including gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, deviation index, and spectral distortion. The second is the CQ_max index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQ_max, the results prove that the CQ_max index can be applied to the evaluation of image fusion effects in different aquaculture areas. For the floating raft cultured area, the conclusion is consentaneous; NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data. For the enclosure seines culture area, the conclusion of quantitative evaluations is not consistent and it shows that there is no definite good method that can be applied to all areas; therefore, careful evaluation and selection of the best applicable image fusion method are required according to the study area and sensor images. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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14 pages, 4577 KiB

Open AccessArticle

Fast Target Recognition Based on Improved ORB Feature

by Yinggang Xie, Quan Wang, Yuanxiong Chang and Xueyuan Zhang

Appl. Sci. 2022, 12(2), 786; https://doi.org/10.3390/app12020786 - 13 Jan 2022

Cited by 13 | Viewed by 3711

Abstract

A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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17 pages, 6820 KiB

Open AccessArticle

Object-Aware Adaptive Convolution Kernel Attention Mechanism in Siamese Network for Visual Tracking

by Dongliang Yuan, Qingdang Li, Xiaohui Yang, Mingyue Zhang and Zhen Sun

Appl. Sci. 2022, 12(2), 716; https://doi.org/10.3390/app12020716 - 12 Jan 2022

Cited by 5 | Viewed by 1826

Abstract

As a classic framework for visual object tracking, the Siamese convolutional neural network has received widespread attention from the research community. This method uses a convolutional neural network to obtain the object features and to match them with the search area features to achieve object tracking. In this work, we observe that the contribution of each convolution kernel in the convolutional neural network for object tracking tasks is different. We propose an object-aware convolution kernel attention mechanism. Based on the characteristics of each object, the convolution kernel features are dynamically weighted to improve the expression ability of object features. The experiments performed using OTB and VOT benchmark datasets show that the performance of the tracking method fused with the convolution kernel attention mechanism is significantly better compared with the original method. Moreover, the attention mechanism can also be integrated with other tracking frameworks as an independent module to improve the performance. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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17 pages, 5987 KiB

Open AccessArticle

Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector

by Dongjun Li, Guoying Meng, Zhiyuan Sun and Lili Xu

Appl. Sci. 2022, 12(1), 107; https://doi.org/10.3390/app12010107 - 23 Dec 2021

Cited by 7 | Viewed by 1757

Abstract

In the coal mining process, various types of tramp materials will be mixed into the raw coal, which will affect the quality of the coal and endanger the normal operation of the equipment. Automatic detection of tramp materials objects is an important process and basis for efficient coal sorting. However, previous research has focused on the detection of gangue, ignoring the detection of other types of tramp materials, especially small targets. Because the initial Single Shot MultiBox Detector (SSD) lacks the efficient use of feature maps, it is difficult to obtain stable results when detecting tramp materials objects. In this article, an object detection algorithm based on feature fusion and dense convolutional network is proposed, which is called tramp materials in raw coal single-shot detector (TMRC-SSD), to detect five types of tramp materials such as gangue, bolt, stick, iron sheet, and iron chain. In this algorithm, a modified DenseNet is first designed and a four-stage feature extractor is used to down-sample the feature map stably. After that, we use the dilation convolution and multi-branch structure to enrich the receptive field. Finally, in the feature fusion module, we designed cross-layer feature fusion and attention fusion modules to realize the semantic interaction of feature maps. The experiments show that the module we designed is effective. This method is better than the existing model. When the input image is 300 × 300 pixels, it can reach 96.12% MAP and 24FPS. Especially in the detection of small objects, the detection accuracy has increased by 4.1 to 95.57%. The experimental results show that this method can be applied to the actual detection of tramp materials objects in raw coal. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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13 pages, 4395 KiB

Open AccessArticle

Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

by Gang Xu, Xiang Li, Xingyu Zhang, Guangxin Xing and Feng Pan

Appl. Sci. 2022, 12(1), 62; https://doi.org/10.3390/app12010062 - 22 Dec 2021

Cited by 5 | Viewed by 2943

Abstract

Loop closure detection is a key challenge in visual simultaneous localization and mapping (SLAM) systems, which has attracted significant research interest in recent years. It entails correctly determining whether a scene has previously been visited by a mobile robot and completely establishing the consistent maps of motion. There are many loop closure detection methods that have been proposed, but most of these algorithms are handcrafted features-based and perform weak robustness to illumination variations. In this paper, we investigate a Siamese Convolutional Neural Network (SCNN) to solve the task of loop closure detection in RGB-D SLAM. Firstly, we use a pre-trained SCNN model to extract features as image descriptors; then, the L2 norm distance is adopted as a similarity metric between descriptors. In terms of the learned features for matching, there are two key issues for discussion: (1) how to define an appropriate loss as supervision (utilizing the cross-entropy loss, the contrastive loss, or the combination of two); and (2) how to combine the appearance information in RGB images and position information in depth images (utilizing early fusion, mid-level fusion or late fusion). We compare our proposed method of different baseline by experiments carried out on two public datasets (New College and NYU), and our performance outperforms the state-of-the-art. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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18 pages, 1563 KiB

Open AccessArticle

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

by Yalong Zhang, Wei Yu, Qiuqin Zhu, Xuan Ma and Hisakazu Ogura

Appl. Sci. 2021, 11(21), 10399; https://doi.org/10.3390/app112110399 - 5 Nov 2021

Cited by 3 | Viewed by 2241

Abstract

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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15 pages, 952 KiB

Open AccessArticle

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

by Yalong Zhang, Wei Yu, Xuan Ma, Hisakazu Ogura and Dongfen Ye

Appl. Sci. 2021, 11(19), 8971; https://doi.org/10.3390/app11198971 - 26 Sep 2021

Cited by 7 | Viewed by 1838

Abstract

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments. Full article

(This article belongs to the Special Issue Selected Papers from FCPAE2021 and 3rd International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM2021))

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