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The Journal of Engineering
Institute of Electrical & Electronics Engineers Inc.
The Journal of Engineering

Institute of Electrical & Electronics Engineers Inc.

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The Journal of Engineering/Journal The Journal of Engineering
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    Three-way decision of target threat decision making based on adaptive threshold algorithms

    Li, BoTian, LinyuHan, YueChen, Daqing...
    293-297页
    查看更多>>摘要：In order to solve the problems of existing threat assessment algorithms, a threat assessment method based on intuitionistic fuzzy three-way decision method is proposed. Firstly, the indexes are described by theory of intuitionistic fuzzy sets on the basis of that the threat assessment index system is established. Then, TOPSIS method is used to calculate the target threat. Finally, the targets are classified by three-way decision method according to the result of target threat. It is proved that it is feasible to introduce three-way decision method into threat assessment. At the same time, an improved algorithm for calculating the threshold of three-way decision is proposed. Experiments show that the improved algorithm has stable results and good classification effect.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Prioritised experience replay based on sample optimisation

    Wang, XuesongXiang, HaopengCheng, YuhuYu, Qiang...
    298-302页
    查看更多>>摘要：The sample-based prioritised experience replay proposed in this study is aimed at how to select samples to the experience replay, which improves the training speed and increases the reward return. In the traditional deep Q-networks (DQNs), it is subjected to random pickup of samples into the experience replay. However, the effect of each sample is different for the training process of agent. A better sampling method will make the agent training more effective. Therefore, when selecting a sample to the experience replay, the authors first allow the agent to learn randomly through the sample optimisation network, and take the average value returned after each study, so that the mean value is used as a threshold for selecting samples to the experience replay. Second, on the basis of sample optimisation, the authors increase the priority update and use the idea of reward-shaping to give additional reward values to the returns of certain samples, which speeds up the agent training. Compared with traditional DQN and the prioritised experience replay DQN, this study uses OpenAI Gym as platform to improve agent learning efficiency.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    HRIPCB: a challenging dataset for PCB defects detection and classification

    Huang, WeiboWei, PengZhang, ManhuaLiu, Hong...
    303-309页
    查看更多>>摘要：To cope with the difficulties in inspection and classification of defects in printed circuit board (PCB), many methods have been proposed in previous work. However, few of them publish their datasets before, which hinders the introduction and comparison of new methods. In this study, HRIPCB, a synthesised PCB dataset that contains 1386 images with 6 kinds of defects is proposed for the use of detection, classification and registration tasks. Besides, a reference-based method is adopted to inspect and an end-to-end convolutional neural network is trained to classify the defects, which are collectively referred to as the RBCNN approach. Unlike conventional approaches that require pixel-by-pixel processing, the RBCNN method proposed in this study firstly locates the defects and then classifies them by deep neural networks, which shows superior performance on the dataset.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Collaborative representation-based locality preserving projections for image classification

    Gou, JianpingYang, YuanyuanLiu, YongYuan, Yunhao...
    310-315页
    查看更多>>摘要：Graph embedding has attracted much more research interests in dimensionality reduction. In this study, based on collaborative representation and graph embedding, the authors propose a new linear dimensionality reduction method called collaborative representation-based locality preserving projection (CRLPP). In the CRLPP, they assume that the similar samples should have similar reconstructions by collaborative representation and the similar reconstructions should also have the similar low-dimensional representations in the projected subspace. CRLPP first reconstructs each training sample using the collaborative representation of the other remaining training samples, and then designs the graph construction of all training samples, finally establishes the objective function of graph embedding using the collaborative reconstructions and the constructed graph. The proposed CRLPP can well preserve the intrinsic geometrical and discriminant structures of high-dimensional data in low-dimensional subspace. The effectiveness of the proposed is verified on several image datasets. The experimental results show that the proposed method outperforms the state-of-art dimensionality reduction.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Balancing large margin nearest neighbours for imbalanced data

    Zhang, XiaotianHan, NanQiao, ShaojieZhang, Yongqing...
    316-321页
    查看更多>>摘要：It is critical to learn and obtain a good distance metric that can precisely measure the distance between samples in imbalanced data. However, traditional metric learning algorithms, e.g. large margin nearest neighbour (LMNN), information-theoretic metric learning, neighbourhood component analysis, do not take imbalanced distributions of classes into consideration. The traditional methods are apt to be affected by the majority samples, so those important minority samples are often ignored during the learning phase of distance metrics matrix, this may gravely confuse decision-making systems on classifying samples. In order to resolve this problem, the authors propose a novel metric-learning method named balancing large margin nearest neighbour (BLMNN) for imbalanced data. BLMNN can improve the objective function according to the distribution of classes, which treats the minority and majority classes equally during the optimisation process. Thus, the contribution of minority class is taken into full consideration, which can greatly improve the accuracy of classification. Substantial experiments were performed on real-world imbalanced datasets. The experiments results in various evaluation indexes of the proposed method comparing it with other metric-learning methods show the advantages of the proposed method.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Human body posture recognition algorithm for still images

    Yu, NaigongLv, Jian
    322-325页
    查看更多>>摘要：Aiming at the low accuracy and poor robustness of the current algorithm based on manual features, this study proposed a posture recognition method combining joint point information with convolutional neural network. The deformable convolution is used in the proposed method to improve the stacked hourglass model, so that it can extract the position of the human joint point accurately. At the same time, the convolutional neural network structure is designed to analyse the position information and confidence of the joint point autonomously, and extract the intrinsic link of the joint point of the human body. Finally, the softmax classifier is used to determine the pose category. Experimental verification has been carried out on the Willow data set. Moreover, the recognition accuracy demonstrates the effectiveness and superiority of the improved method.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Natural scene text detection based on multiscale connectionist text proposal network

    Huang, MinLan, ChaohaoHuang, WeiTao, Yang...
    326-329页
    查看更多>>摘要：The technique of recognising text in natural scene pictures is widely used in social production. For the existing identification methods, it is difficult to accurately identify in complex environments. The accuracy of the detection determines the efficiency of the identification. A text detection method based on Multiscale Connectionist Text Proposal Network is proposed. The Multiscale-Region Proposal Network regresses and classifies the extracted region to obtain the final candidate region. Taking a large number of commodity image samples as a dataset, the multi-scale joint text proposal network is used to detect and locate the text content area in the image. The experimental results show that the proposed algorithm improves the detection accuracy in complex environments.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Design of a collaborative monitoring system for hydroelectric generating sets and its application

    Fanwu, Chu
    330-334页
    查看更多>>摘要：Diagnosing faults of hydroelectric generating sets (HGS) need the joint efforts of many experts. It is really hard to obtain an accurate interpretation for detected faults relying solely on one independent monitoring system. Therefore, a collaborative monitoring system exploiting independent specialised monitoring systems as the functional unit containing several team members (TMs) has been designed based on facilitator method and team intelligence. Then, the division of labour and responsibilities of each functional unit and communication technology used by TMs in this system are detailed. In addition, this article describes how collaborative monitoring behaviours have been realised through a conversation model based on communication technology. This system provides a good technical support for comprehensive analysis and fault interpretation. A concrete application example is given at the last of this article.
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Updating knowledge in multigranulation decision-theoretic rough set model based on decision support degree

    Lin, GuopingLiu, FenglingChen, ShengyuYu, Xiaolong...
    335-343页
    查看更多>>摘要：Based on the majority rules, a multigranulation decision-theoretic rough set model based on the decision support degree is proposed, in which the thresholds can be computed by the decision risk minimisation based on the Bayesian decision-theoretic. In various practical situations, information systems may alter dynamically with time. Incremental learning is an alternative manner for maintaining knowledge by utilising previous computational results under dynamic data. Therefore, the authors investigate dynamic approaches to update the knowledge in the new model when adding or deleting granular structures. Besides, the corresponding dynamic and static algorithms are designed and their time complexities are analysed. Finally, comparative experiments by using six data sets from UCI are carried out; the results illustrate that the proposed dynamic algorithm is effective and is more efficient than the static algorithm.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model

    Li, YanDai, Wei
    344-347页
    查看更多>>摘要：In this study, aiming at the problem that the price of Bitcoin varies greatly and is difficult to predict, a hybrid neural network model based on convolutional neural network (CNN) and long short-term memory (LSTM) neural network is proposed. The transaction data of Bitcoin itself, as well as external information, such as macroeconomic variables and investor attention, are taken as input. Firstly, CNN is used for feature extraction. Then the feature vectors are input into LSTM for training and forecasting the short-term price of Bitcoin. The result shows that the CNN-LSTM hybrid neural network can effectively improve the accuracy of value prediction and direction prediction compared with the single structure neural network. The finding has important implications for researchers and investors in the digital currencies market.
      原文链接:
    • NETL
    • NSTL
    • Wiley