首页期刊导航|The Journal of Engineering
期刊信息/Journal information
The Journal of Engineering
Institute of Electrical & Electronics Engineers Inc.
The Journal of Engineering

Institute of Electrical & Electronics Engineers Inc.

月刊

The Journal of Engineering/Journal The Journal of Engineering
正式出版
收录年代

    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

    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

    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

    Discriminative sparsity preserving projection via global constraint for unconstrained face recognition

    Ying, TongShen, Yuehong
    348-352页
    查看更多>>摘要：The unconstrained face images collected in the real environments include many complicated and changeable interference factors, and sparsity preserving projections cannot well characterise the low-dimensional intrinsic structure embedded in the high-dimensional unconstrained face images, which is important for subsequent recognition task. To deal with this problem, in this study the authors propose a new dimensionality reduction method named as discriminative sparsity preserving projection via global constraint. It seeks an optimal sub-space in which the samples in intra-classes are as compact as possible, while the samples in inter-classes are as separable as possible by adopting the compactness constraint terms of reconstruction coefficients and the penalty terms of global distribution. Extensive experiments are carried out on Faces in Labeled the Wild database and PubFig database which are two representative unconstrained face sets, and the corresponding experimental results illustrate the effectiveness of the proposed method.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Method of social network analysis and visualisation based on reviews

    Xu, XiaoweiLiu, WeiTao, YeWang, Xiaodong...
    353-356页
    查看更多>>摘要：With the development of e-commerce platforms, the huge amount of transaction volume produces a lot of commentary information. To study the relationship between users and user comments and corresponding products, a social network analysis method based on user comments is proposed. The authors regard the network connection between goods and user comments as a problem on the network and use the spectral clustering algorithm to maintain the structural characteristics of the network. This method can visualise the network structure features such as user clustering and distribution, which can help to understand the network from a macro perspective and be directly applied to the visualisation of other comment-based networks. It is significant for analysing the relationship between users and user comments and corresponding products. The comparative visualisation experiments are also discussed, to validate the effectiveness of the proposed method.
      原文链接:
    • NETL
    • NSTL
    • Inst Engineering Technology-Iet

    Construction of knowledge graph of maritime dangerous goods based on IMDG code

    Zhang, QiWen, Yuan Q.Han, DongZhang, Fan...
    361-365页
    查看更多>>摘要：The International Maritime Dangerous Goods Code (IMDG Code) is the most important regulation in the international maritime transport chain of dangerous goods. Any international ship carrying dangerous goods must be strictly observed. Integrating and correlating cumbersome knowledge of IMDG Code and simplifying the query process are of great significance to the safe transportation and storage of dangerous goods. As a new method of knowledge representation and management, knowledge graph has been successfully applied in many industries. It can present the complex relationship between domain knowledge and correlate trivial and scattered knowledge, which provides a new way to solve this problem. This article starts with the knowledge system, structure, and classification of IMDG Code, and then analyses the related concepts of knowledge graph of maritime dangerous goods. Based on the above analysis, the authors construct the knowledge graph of maritime dangerous goods. It is helpful to simplify the retrieval process of dangerous goods professional knowledge, realise the automatic judgment of cargo stowage and segregation, and promote the intelligent transportation of dangerous goods.
      原文链接:
    • NETL
    • NSTL
    • Wiley