2023 Vol.41

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Journal of Applied Sciences    2023, 41 (1): 0-0.   Abstract （1332）      PDF（pc） （78KB）（77）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Named Entity Recognition Algorithm Enhanced with Entity Category Information LIU Minghui, TANG Wangjing, XU Bin, TONG Meihan, WANG Liming, ZHONG Qi, XU Jianjun Journal of Applied Sciences    2023, 41 (1): 1-9.   DOI: 10.3969/j.issn.0255-8297.2023.01.001 Abstract （1596）      PDF（pc） （1423KB）（221）       Knowledge map    Save To solve the problem that the character level model of Chinese named entity recognition (NER) may ignore word information in sentences, a Chinese NER method based on entity category information enhancement in knowledge graph was proposed. Firstly, a training set was segmented with word segmentation tool, and all possible words were selected to construct a vocabulary. Secondly, the category information of entities in the vocabulary was retrieved by using generic knowledge graph, to construct a word set related to characters in a simple and effective way, and an entity category information set is generated according to the category information of entities in the word set. Finally, word embedding method was used to convert the set of category information into embeddings and concatenation of character embeddings, so as to enrich features in embedding layer. The proposed method can either be used as a module to expand feature diversity of embedding layer, or jointly applies with a variety of encoder-decoder models. Experiments on the Chinese NER dataset proposed by Microsoft Research Asia (MSRA) show the superiority of the proposed model. Compared with the models of Bi-directional long short-term memory (Bi-LSTM) and Bi-LSTM plus with conditional random field (CRF), the proposed method increases F1 by 11.00% and 3.09% respectively, verifying that the category information of entities in knowledge graph performs high effectiveness in the enhancement of Chinese NER. Reference | Related Articles | Metrics | Comments（0）

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Journal of Applied Sciences    2023, 41 (1): 2-0.   Abstract （1331）      PDF（pc） （47KB）（37）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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A Spatio-Temporal Similarity Query Algorithm for Trajectory Based on Graph Structure XIONG Wei, XIONG Shuyi, CAO Jingzhi, CHEN Hao, GAO Jiayuan Journal of Applied Sciences    2023, 41 (1): 10-22.   DOI: 10.3969/j.issn.0255-8297.2023.01.002 Abstract （1690）      PDF（pc） （643KB）（130）       Knowledge map    Save To address the problem of slow similarity query of massive spatio-temporal trajectory data, a similarity query algorithm based on graph structure is proposed. Firstly, the trajectory is modeled as a path with spatial and temporal dimensions in a graph, and a trajectory similarity metric function is designed to match spatial and temporal distances simultaneously. Secondly, based on the similarity metric function, an edge-based inverted index structure combined with time filtering is designed, which supports spatio-temporal similarity query of trajectories while improving query performance using a pruning strategy with distance upper bound. The query algorithm performs distance calculation for each trajectory in the returned set of similar trajectories and sorts to obtain the top k results with the highest similarity. Finally, synthetic dataset and real dataset are used to compare the proposed algorithm with NTrajI algorithm, SHQ algorithm and SHQT algorithm. Experimental results show that the proposed method outperforms the comparison methods in index building and query processing, and obtains higher quality of query results. Therefore, the proposed algorithm is feasible and effective. Reference | Related Articles | Metrics | Comments（0）

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Link Prediction in Multiplex Networks Based on Naïve Bayes Model ZHANG Yakun, LI Longjie, CHEN Xiaoyun Journal of Applied Sciences    2023, 41 (1): 23-40.   DOI: 10.3969/j.issn.0255-8297.2023.01.003 Abstract （1382）      PDF（pc） （3957KB）（104）       Knowledge map    Save To solve the problem of information fusion between layers in link predictions of multiplex networks, this paper proposes a new link prediction method based on the naïve Bayes model. The proposed method predicts links by combining the neighborhood information of target layers with the global information of distinct auxiliary layers relevant to the target layers. In a target layer, according to the neighborhood information of a node pair, the connection probability of the node pair is computed using the naïve Bayes model. In an auxiliary layer, based on whether there is a link between the node pair, the probability that the node pair has a link in the target layer is calculated. Experimental results on real and synthetic networks show that the proposed method achieves superior performance in both positively and negatively correlated multiplex networks. Reference | Related Articles | Metrics | Comments（0）

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Medical Electronic Data Feature Learning Method Based on Deep Learning WANG Ting, WANG Na, CUI Yunpeng, LIU Juan Journal of Applied Sciences    2023, 41 (1): 41-54.   DOI: 10.3969/j.issn.0255-8297.2023.01.004 Abstract （1589）      PDF（pc） （746KB）（150）       Knowledge map    Save How can we effectively carry out the feature learning of high-dimensional and heterogeneous medical electronic data to optimize the risk prediction of concurrent medical use in patients? To address the problem, this paper proposed a method of multi-stage deep feature learning. Firstly, we performed the feature learning of medical use data with temporal properties by combining deep learning models of long short-term memory (LSTM) and auto-encoder (AE), and generated the synthetic factor of concurrent medical use with bisecting k-means clustering method. Secondly, we constructed two types of feature vectors for patients to predict adverse event risk, and analyzed the associated factors of high risk. Finally, we compared the performance of the proposed method with existing methods on real-word dataset, and the results show that the proposed method increases the accuracy by 5%~10%, and reduces the false rate by 3%~5% in the risk prediction of concurrent medical use. Reference | Related Articles | Metrics | Comments（0）

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Multi-modal Emotion Recognition Using Speech, Text and Motion JIA Ning, ZHENG Chunjun Journal of Applied Sciences    2023, 41 (1): 55-70.   DOI: 10.3969/j.issn.0255-8297.2023.01.005 Abstract （1756）      PDF（pc） （1376KB）（277）       Knowledge map    Save For the problems of low accuracy and weak generalization ability in the process of human emotion recognition, a fusion method of multi-modal emotion recognition based on speech, text and motion is proposed. In the speech mode, a depth wavefield extrapolation-improved wave physics model (DWE-WPM) is designed to simulate the sequence information mining process of long short-term memory (LSTM) network; In the text mode, a transformer model with multi-attention mechanism is used to capture the potential semantic expression of emotion; In the motion mode, sequential features of facial expression and hand action are combined by using two-way three-layer LSTM model with attention mechanism. Accordingly, a multi-modal fusion scheme is designed to achieve high-precision and strong generalization ability of emotion recognition. In the general emotion corpus IEMOCAP, the method proposed in this paper is compared with existing emotion recognition algorithms. Experimental results show that the proposed method has higher recognition accuracy both in single modality and multi-modals, with average accuracy improved by 16.4% and 10.5% respectively, effectively improving the ability of human emotion recognition in human-computer interaction. Reference | Related Articles | Metrics | Comments（0）

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Force Haptic-Enhanced Virtual Reality Factory System XU Wenbiao, XU Chi, SHI Hongyan, LI Lin Journal of Applied Sciences    2023, 41 (1): 71-79.   DOI: 10.3969/j.issn.0255-8297.2023.01.006 Abstract （1446）      PDF（pc） （7922KB）（69）       Knowledge map    Save Existing virtual reality factories that depend on helmets and handles can only provide one-way visual and auditory sensing for virtual operation, and cannot support immersive two-way tactile interaction. To deal with the problem, a haptics-enhanced virtual reality factory system is developed using force feedback controller. With Unity3D, 3DMax is used to model multiple kinds of tools and parts with 3D vision in the factory, and enhances their stereo vision by components such as reflection probes and light probes. Furthermore, by integrating components such as rigid bodies, collision bodies, joints to the virtual model, a tactile feedback model for the virtual space is established, which supports the real-time interaction between the haptic controller and virtual bodies. Experiments show that the system can provide a variety of force tactile perception capabilities such as mass, friction, and constant force. It supports tactile operations by touching and grasping more than 20 tools and parts, where the model, contour, and other physical attributes such as mass are provided. In this way, the system realizes the immersive operation experience of multi-dimensional audio-visual-haptic operation in the virtual factory. Reference | Related Articles | Metrics | Comments（0）

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Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network ZENG Jing, LI Ying, QI Xiaosha, JI Genlin Journal of Applied Sciences    2023, 41 (1): 80-94.   DOI: 10.3969/j.issn.0255-8297.2023.01.007 Abstract （1496）      PDF（pc） （7047KB）（67）       Knowledge map    Save In order to improve the accuracy of video anomaly detection, we propose a video anomaly detection method based on secondary prediction of multi-layer memory enhancement generative adversarial networks. Firstly, a spatiotemporal cube is extracted from target detection, and sent into encoder to obtain a prediction frame. Secondly, the apparent feature of the prediction frame and the optical flow feature of corresponding real frames are fused to form fusion features. Finally, a secondary prediction future frame is generated by using multi-layer memory enhancement generative adversarial networks, for learning normal feature patterns of different levels and capturing the semantic information of context. Experimental results on UCSD Ped2 and CUHK Avenue datasets show that the proposed method can effectively improve the performance of video anomaly detection compared with other video anomaly detection methods, and its frame level AUC reaches 99.57% and 91.59%, respectively. Reference | Related Articles | Metrics | Comments（0）

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Chinese Event Trigger Extraction Based on Span Regression ZHAO Yuhao, CHEN Yanping, HUANG Ruizhang, QING Yongbin Journal of Applied Sciences    2023, 41 (1): 95-106.   DOI: 10.3969/j.issn.0255-8297.2023.01.008 Abstract （1392）      PDF（pc） （640KB）（85）       Knowledge map    Save In Chinese event trigger word extraction tasks, word-based models suffer from errors caused by word separation, while character-based models have difficulty in capturing the structural and contextual semantic information of trigger words. In view of the problem, a spanwise regression-based trigger word extraction method is proposed. Considering that a specific length of character subsequence (span) in a sentence may constitute an event trigger word, the method obtains the feature representation of the sentence with a pre-trained model of bidirectional encoder representation from Transformer (BERT), and generates the candidate span of the trigger word on the sentence feature representation. Then the model filters the candidate span with low confidence using a classifier, and adjusts the boundaries of the candidate span by regression to accurately locate the trigger word. Finally, the adjusted candidate spans are classified, and extraction results are obtained. Experimental results on the ACE2005 Chinese dataset show that the F1 value of the span-based regression method is 73.20% for trigger word recognition task and 71.60% for trigger word classification task, better than existing models. Also, experimental comparison with span-based method without regression verifies that the regression adjustment of span boundaries can improve the accuracy of event trigger word detection. Reference | Related Articles | Metrics | Comments（0）

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Person Re-identification Algorithm Based on Channel Feature Aggregation XU Zengmin, LU Guangjian, CHEN Junyan, CHEN Jinlong, DING Yong Journal of Applied Sciences    2023, 41 (1): 107-120.   DOI: 10.3969/j.issn.0255-8297.2023.01.009 Abstract （1425）      PDF（pc） （1940KB）（164）       Knowledge map    Save In deep-learning person re-identification algorithms, channel characteristics may be neglected, leading to a degraded model-expression ability. Address to the problem, we choose the ResNeSt50 as backbone network, and add an SE block to the end of residual blocks by using characteristics of SENet channel attention for enhancing features extraction of channels in networks. In addition, due to lack of control factors, ReLU function may reduce the correct responses of different feature graphs to activation values. Thus, we present two new activation functions. One is named as Weighted ReLU (WReLU) by combining ReLU with weight bias term, which can effectively improve feature selection ability in neural networks, and the other is Leaky Weighted ReLU (LWReLU), which is applied in Split-Attention and SE block, and enables Split-Attention to promote the weight learning ability from feature maps. Moreover, a new loss function with circle loss is also proposed for optimizing the convergence of objective function. Experimental results show that the proposed algorithm outperforms original backbone by 19.08%, 0.98%, and 2.02% in Rank-1, and 17.13%, 2.11%, and 2.56% in mAP respectively on CUHK03-NP, Market1501, and DukeMTMC-ReID datasets. Reference | Related Articles | Metrics | Comments（0）

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Proactive Self-Adaptive Approach Driven by LSTM Prediction for Software System XIE Shenglong, WANG Lu, LIU Ruijia, PU Ying, LIU Xiao Journal of Applied Sciences    2023, 41 (1): 121-140.   DOI: 10.3969/j.issn.0255-8297.2023.01.010 Abstract （1486）      PDF（pc） （1389KB）（120）       Knowledge map    Save Aiming at the adjustment lag problem of reactive self-adaptive software systems, a proactive self-adaptive approach based on long short-term memory (LSTM) prediction driven is proposed. In this approach, LSTM neural network prediction technology is embedded in the analysis phase of monitor-analyze-plan -execute-knowledge (MAPE-K) control model; Operating data relating to self-adaptive environments, self-adaptive qualities, and self-adaptive goals, and historical data are used for classification prediction to form a self-adaptive early warning mechanism, which can effectively improve the proactive selfadaptive ability of software systems and reduce the lag influence of reactive self-adaptive decision-making at the same time. In order to illustrate the initiative, robustness and effectiveness of this approach, evaluation on the classic distribution tele-assistance system (dTAS) platform is carried out. Experimental results show that the proposed approach can provide early warning to self-adaptive demand, and enable software systems to complete proactive self-adaptive adjustment when necessary. Reference | Related Articles | Metrics | Comments（0）

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Design and Implementation of Decentralized Trusted Crowdsourcing Platform Based on Commitment Scheme WANG Huajian, LI Renwei, ZHOU Huan, YANG Guogui Journal of Applied Sciences    2023, 41 (1): 141-152.   DOI: 10.3969/j.issn.0255-8297.2023.01.011 Abstract （1443）            Knowledge map    Save In order to remove the dependence of traditional crowdsourcing on third-party central institutions, and at the same time ensure the fair distribution of crowdsourcing tasks and credible submission of results, a design scheme of trusted crowdsourcing platform based on blockchain smart contracts is proposed. First, a commitment-based two-stage submission mechanism is proposed and applied to the data submission process of recipients to solve the data transparency problem on the blockchain, so that the recipients cannot steal data from each other. Second, an unbiased random selection algorithm is designed to select relatively independent receivers from scattered receiver pools, preventing possible collusion among receivers. Finally, the randomness of the selection algorithm and the feasibility of the overall scheme are verified through selection simulation experiment and the implementation of the prototype system on Ethereum. Reference | Related Articles | Metrics | Comments（0）

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Optimization Algorithm for Dark Edge Detection of Deep-Sea Image Based on Particle Swarm Optimization ZOU Qianying, CHEN Huiyang, LI Yongsheng, HU Liwen, WANG Xiaofang Journal of Applied Sciences    2023, 41 (1): 153-169.   DOI: 10.3969/j.issn.0255-8297.2023.01.012 Abstract （1434）      PDF（pc） （6687KB）（219）       Knowledge map    Save In order to solve the problem of image recognition for deep-sea resource detection, an optimization algorithm of image dark edge detection based on particle swarm optimization is proposed. The algorithm improves activation functions by using exponential linear unit and Gaussian error linear unit, constructs a dark edge detection algorithm in combination with the improved activation function according to the detection results of Marr-Hildreth operator, and uses particle swarm to train and optimize the improved dark edge detection algorithm. Finally, the proposed and several existing algorithms are applied and compared on 11 underwater data sets. Experimental results show that the proposed algorithm has the highest peak signal-to-noise ratio, structural similarity and edge retention index, reaching 18.769 6 dB, 0.660 7 and 0.834 5, respectively, and has the lowest mean square error of image of 3 750.225 3. Its average detection time is 0.667 4 s, about 14% shorter than that of the second best performance algorithm in the experiment. Reference | Related Articles | Metrics | Comments（0）

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Quantum Key Lifecycle Management Based on Blockchain LIN Yusheng, CHANG Yan, CHEN Tiansu, YU Shipeng, ZHANG Shibin Journal of Applied Sciences    2023, 41 (1): 170-182.   DOI: 10.3969/j.issn.0255-8297.2023.01.013 Abstract （1481）      PDF（pc） （706KB）（116）       Knowledge map    Save In order to ensure a higher security of quantum key from generation, distribution, storage, use, update and destruction, this paper proposes a quantum key lifecycle management scheme based on blockchain. The two-party which has the requirement of confidential communication generates a truly random symmetric quantum negotiation key pool through quantum key distribution devices, and stores it in the quantum device administrator of each party. Then the quantum device administrators of two parties generate quantum key files according to negotiated numbering rules of quantum keys. Users of the two parties respectively apply for quantum keys from their quantum device administrators for communication. In the process of communication, the log information related to the generation, distribution, use, update and destruction of quantum keys is uploaded to a blockchain, and the quantum device administrators and communication users cooperate with the blockchain administrator to complete the management and traceability of the full lifecycle of quantum keys. Theoretical analysis shows that this scheme can solve the problem that quantum key cannot be effectively traced and managed in communication system, and realize the transparency and reliability of management and traceability of quantum key in whole lifecycle. Reference | Related Articles | Metrics | Comments（0）

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Journal of Applied Sciences    2023, 41 (2): 1-0.   Abstract （1933）      PDF（pc） （85KB）（127）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Journal of Applied Sciences    2023, 41 (2): 2-0.   Abstract （1886）      PDF（pc） （48KB）（43）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Traceable DNN Model Protection Based on Watermark Neural Network LIU Yalei, HE Hongjie, CHEN Fan, LIU Zhuohua Journal of Applied Sciences    2023, 41 (2): 183-196.   DOI: 10.3969/j.issn.0255-8297.2023.02.001 Abstract （2214）      PDF（pc） （9105KB）（215）       Knowledge map    Save This paper proposes a multi-user traceability watermarking neural network approach to model security and copyright certification for deep neural networks (DNN). The watermark is generated by the key driver and embedded invisibly in the output images of the DNN model, hence realizing the intellectual property protection and copyright tracking of DNN model. A codec network is added to the DNN model to embed the watermark, and a two-stream tamper detection network is used as the discriminator. Thus, the problem of residual watermark in the output images of the model is solved, which, reduces the impact on the performance of DNN model and enhances the security. In addition, a two-stage training method is proposed in this paper to distribute different watermarked models to different users. When copyright disputes occur, another residual network can be used to extract the watermark image from the output image. Experiments show that the proposed method is efficient in distributing watermarked models, and is able to trace the source of DNN models embedded with similar watermarked images for multiple users. Reference | Related Articles | Metrics | Comments（0）

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Reversible Data Hiding in Encrypted Image Based on Dual-Domain Joint Coding and Secret Sharing WENG Ke, QIN Jianhao, SONG Tianran, SHI Hui Journal of Applied Sciences    2023, 41 (2): 197-217.   DOI: 10.3969/j.issn.0255-8297.2023.02.002 Abstract （2054）      PDF（pc） （7394KB）（91）       Knowledge map    Save A reversible data hiding scheme in encrypted image (RDHEI) based on dualdomain joint coding and secret sharing is proposed to improve the security and embedding capacity. First, the image is predicted by median-edge detector (MED) and the optimal threshold l is calculated. Pixels with different prediction errors are divided into predictable and unpredictable pixels according to the threshold. Second, to enhance the embedding capacity, the dual-domain joint coding is adopted to compress the auxiliary information on the pixel domain and bit domain, respectively. Then, the original image is encrypted to generate multiple encrypted images using the secret sharing technique based on cipher feedback, where the auxiliary information and secret data from multiple parties are embedded into the encrypted images. Finally, the embedded secret data as well as the original image are perfectly recovered based on the extracted auxiliary information. Experimental results show that the algorithm significantly improves the embedding capacity and security. Reference | Related Articles | Metrics | Comments（0）

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Segmented Backdoor Defense Based on Local Gradient and Global Gradient Ascent XIAO Xiaotong, DING Jianwei, ZHANG Qi Journal of Applied Sciences    2023, 41 (2): 218-227.   DOI: 10.3969/j.issn.0255-8297.2023.02.003 Abstract （2165）      PDF（pc） （1267KB）（160）       Knowledge map    Save Backdoor triggers tend to be hidden and are difficult to detect. To solve this problem, a segmented backdoor defense (SBD) method based on local and global gradient ascent is proposed. In the early stage of training, local gradient ascent is introduced to enlarge the difference between the average training loss of backdoor samples and clean samples. A small number of high-precision backdoor samples are isolated to facilitate backdoor forgetting in the later stage. In the backdoor forgetting stage, global gradient ascent is introduced to reduce the correlation between backdoor samples and target categories to achieve defense. Based on three benchmark datasets GTSRB, Cifar10 and MNIST, a large number of experiments are conducted on the WideResNet-16-1 model against six advanced backdoor attacks. It is shown that the proposed segmented backdoor defense method can reduce the success rate of most attacks to below 5%. Moreover, the proposed method can train a clean equivalent learning model on both backdoor dataset and clean dataset. Reference | Related Articles | Metrics | Comments（0）

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Image Privacy Protection Based on Cycle-Consistent Generative Adversarial Networks XIE Yiyi, ZHANG Yushu, ZHAO Ruoyu, WEN Wenying, ZHOU Yuqian Journal of Applied Sciences    2023, 41 (2): 228-239.   DOI: 10.3969/j.issn.0255-8297.2023.02.004 Abstract （2212）      PDF（pc） （9135KB）（285）       Knowledge map    Save Social media and cloud computing have facilitated the distribution and storage of images. Meanwhile, concerns about image privacy have been raised. It is crucial to protect image privacy from privacy violation and illegal use. Motivated by this, an image privacy protection method based on cycle-consistent generative adversarial networks (CycleGAN) is proposed in this paper. Considering the usability in image privacy protection, the method first combines image segmentation with CycleGAN to select different segmentation coefficients to generate images with different degrees of privacy protection. Then reversible information hiding is used to embed information in the generated privacy preserving image, so as to prevent illegal users from reversing the image. Thus, a balance is achieved between image privacy protection and usability. The proposed method is trained and tested using PIPA dataset, using peak signal to noise ratio and structural similarity index are used as performance metrics to evaluate the privacy-preserving images. Experimental results show that the proposed method outperforms other schemes in both image privacy preservation and usability. Reference | Related Articles | Metrics | Comments（0）

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Research Progress on Glyph Perturbation for Anti-print Scanning and Anti-screen Shooting WANG Chen, YAO Ye, LI Li Journal of Applied Sciences    2023, 41 (2): 240-251.   DOI: 10.3969/j.issn.0255-8297.2023.02.005 Abstract （2037）      PDF（pc） （3084KB）（83）       Knowledge map    Save In this paper, we provide a review on the research progress of glyph perturbation. Following an introduction of the application scenarios of glyph perturbation, the existing works related to glyph perturbation for anti-print scanning and anti-screen shooting are systematically presented. The existing methods can be classified into two categories: traditional glyph perturbation and deep learning-based glyph perturbation. According to the attributes of characters, the former can be subdivided into pixel flipping, height adjustment, spacing adjustment, stroke adjustment, feature point adjustment, and skeleton adjustment. From the perspective of high-dimensional features, the latter slightly modifies the glyph of characters to embed additional messages. According to the property of the generated data, it can be divided into perturbed text image generation and vector font generation. The glyph perturbation methods are compared and analyzed in terms of robustness, embedding capacity, and complexity. Finally, the challenges and prospects of glyph perturbation are summarized. Reference | Related Articles | Metrics | Comments（0）

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FBG Temperature Distribution Detection Based on TS-DFT High Speed Spectral Demodulation YANG Mei, CHEN Na, LIU Zhenmin, SHANG Yana, LIU Shupeng, CHEN Zhenyi, WANG Tingyun Journal of Applied Sciences    2023, 41 (2): 252-261.   DOI: 10.3969/j.issn.0255-8297.2023.02.006 Abstract （2085）      PDF（pc） （2320KB）（110）       Knowledge map    Save In this paper, high speed demodulation of fiber Bragg grating (FBG) reflectivity spectrum is realized based on time-stretch dispersive Fourier transformation (TS-DFT). The demodulation system consists of mode-locked laser, optical circulator, dispersion compensating fiber, reference FBG, sensing FBG, and data collection and processing module. The time domain mapping spectra of sensing FBG under different temperature fields were obtained in experiments. By comparison with the measured spectra of the optical spectrum analyzer, the high-speed spectral demodulation ability of the system is verified, and the demodulation rate is 51.2 MHz. Combined with the spectral inversion algorithm, the temperature distribution along the axis of sensing FBG is obtained, and the spatial resolution is 200 mm. Therefore, temperature sensing with high speed and high spatial resolution is realized. Reference | Related Articles | Metrics | Comments（0）

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Protected Wildlife Monitoring System with Low Power Consumption Based on NB-IoT ZHANG Ximin, ZHAN Haisheng, LIU Qiang, YUAN Zhanjun, ZHANG Jinbo Journal of Applied Sciences    2023, 41 (2): 262-271.   DOI: 10.3969/j.issn.0255-8297.2023.02.007 Abstract （2038）      PDF（pc） （10189KB）（135）       Knowledge map    Save A distributed protected wildlife tracking and monitoring system based on NBIoT is proposed to realize low power consumption, remote monitoring, customization and low cost. The key solutions including efficient power management, concurrent communication processing, and network transparent transmission are given. The system consists of trackers and a monitoring information system. Each tracker includes a NB-IoT communication and location module, a low-power microcontroller, a solar composite power supply component with lithium battery, and a high-efficiency power converter. The monitoring information system is composed of data communication equipment, server and monitoring terminals. Experiments show that the system can collect the position of wild animals remotely and display their real-time positions and activity trajectories on the WEB map. Customized statistical analysis of the data and reports can be generated. The average power consumption of the tracker is less than 50mW, and the average positioning error is less than 20 meters. The system has a large monitoring range and low cost, and has been applied in a wildlife protection station showing good performance. Reference | Related Articles | Metrics | Comments（0）

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Reversible Information Hiding Algorithm in Ciphertext Domain with Multiple Embedding Based on Block Classification ZHANG Xiangyu, LI Fengyong, QIN Chuan Journal of Applied Sciences    2023, 41 (2): 272-283.   DOI: 10.3969/j.issn.0255-8297.2023.02.008 Abstract （2119）      PDF（pc） （1824KB）（152）       Knowledge map    Save Insufficient utilization of image blocks in the existing reversible information hiding methods results in a low embedding capacity of secret information. In order to address this issue, this paper proposes a multiple embedding reversible information hiding algorithm based on block classification. First, the original image is encrypted with a stream cipher, and the encrypted image is further divided into multiple non-overlapping blocks. Subsequently, the Most Significant Bit (MSB) adaptive prediction algorithm is used to predict the first pixel and other pixels in each block, which is marked as a usable block or an unusable block. Finally, the available blocks are reconstructed and embedded with secret data, and the non-available blocks are re-embedded with the Median-Edge Detector (MED) prediction algorithm to realize the embedding of secret information. When the receiver receives the secret image, the secret information is extracted by the data-hiding key, and the original image is restored by the encryption key. Experimental results demonstrate that the proposed method can significantly improve the embedding capacity of secret information embedding, while keeping a high image restoration quality. The overall performance is superior to existing methods in terms of both embedding capacity and image restoration quality. Reference | Related Articles | Metrics | Comments（0）

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Adaptive Range Digital Beam-Forming Method for Ka-band Automotive Synthetic Aperture Radar WEN Jing, HUA Wei, WANG Hui, GONG Qingkun Journal of Applied Sciences    2023, 41 (2): 284-295.   DOI: 10.3969/j.issn.0255-8297.2023.02.009 Abstract （1981）      PDF（pc） （19146KB）（79）       Knowledge map    Save The directional deviation on the topographic relief of the image arising from the low height of the vehicle is nonnegligible during the verification of digital beam-forming (DBF) using Ka-band automotive synthetic aperture radar (Ka-SAR). Due to the inevitable errors of the DBF weight coefficients using scan on receive (SCORE) algorithm, the synthetic beam pattern would deviate from the ideal state and degrade the system performance. In this paper, an adaptive range DBF processing algorithm based on multi-channel SAR is proposed. The DBF weighting coefficients are adaptively generated by interference processing and phase extraction, which improves the receiving gain. The adaptive algorithm has the advantages of high precision, simple processing flow, low computational load and real-time implementation. Finally, the effectiveness of the algorithm is verified based on simulation and experimental data. Reference | Related Articles | Metrics | Comments（0）

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Identification Method for Vessel Interrupt Track Correlating Based on Fuzzy Membership Degree CHEN Zhaotong, CHEN Jiangping, PAN Li Journal of Applied Sciences    2023, 41 (2): 296-310.   DOI: 10.3969/j.issn.0255-8297.2023.02.010 Abstract （2149）      PDF（pc） （2053KB）（222）       Knowledge map    Save In order to integrate the vessels’ tracks from different monitoring sources and form a unified maritime posture, a track correlation identification method based on track prediction and fuzzy membership evaluation is proposed. A polynomial fitting method is used to speculate the past and future tracks. Position, course, and speed are selected as the fuzzy factors. The membership degree of each fuzzy factor between the predicted track is calculated by ridge fuzzy membership function and weighted to obtain the membership of a single moment. A weighting function is constructed to calculate the comprehensive membership degree and finally the threshold is set to determine whether the two tracks are correlated. In simulation experiments, the precision and the recall rate of the proposed method is higher than 90% and 80% respectively, which outperforms the traditional method. In order to further investigate the applicability of the proposed method, the radar monitoring data of vessel tracks under different scenarios are simulated, including stable scenarios, speed change and course change scenarios during the interruption interval. Simulation results show that the proposed method provides an effective way to solve the problem of correlation identification of interrupted tracks in the cross environment of non-cooperative vessels. Reference | Related Articles | Metrics | Comments（0）

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A Mosaic Puzzle Camouflage Steganography with Image Block Rotation LIU Zhaozhi, ZHAO Yan Journal of Applied Sciences    2023, 41 (2): 311-325.   DOI: 10.3969/j.issn.0255-8297.2023.02.011 Abstract （1967）      PDF（pc） （3356KB）（75）       Knowledge map    Save In order to improve the efficiency and robustness of coverless steganography, a mosaic puzzle camouflage steganography with image block rotation is proposed. Firstly, the sender reduces the image randomly selected using the key in the shared image library and adjusts its tone. Then a random rotation angle is added to each image block according to the secret information and secret key to encode the image. Finally, the encoded image is arranged based on the rules to generate the secret mosaic image. The receiver removes the random rotation according to the secret key and obtains the secret information by reading the rotation angle of the encoded image. Experimental results show that the proposed method outperforms other coverless steganography methods in terms of embedding capacity and robustness. Reference | Related Articles | Metrics | Comments（0）

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Experimental Evaluation of FSM Conformance Testing Based on Structure Coverage and State Identification LIN Weiwei, ZENG Hongwei, MIAO Huaikou, WANG Xiaolin Journal of Applied Sciences    2023, 41 (2): 326-343.   DOI: 10.3969/j.issn.0255-8297.2023.02.012 Abstract （1946）      PDF（pc） （786KB）（170）       Knowledge map    Save In finite state machine (FSM) conformance testing, there are two widely used test generation techniques which are based on structure coverage and state identification respectively. Under the condition of scarce test resources, we often face the problem of weighing selection of different test methods. To the best of our knowledge, there is no comprehensive comparative study of these two test techniques so far. This paper presents the necessity of experimental evaluation of the two test methods, and conducts experiments based on 10 FSM empirical cases. The performance is evaluated in terms of test cost and fault coverage capability, so as to provide empirical suggestions for the selection and application of these two techniques in FSM conformance testing. Reference | Related Articles | Metrics | Comments（0）

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Stock Price Trend Prediction Based on Dual-Stream LSTM Neural Network WU Feng, XIE Cong, JI Shaopei Journal of Applied Sciences    2023, 41 (2): 344-358.   DOI: 10.3969/j.issn.0255-8297.2023.02.013 Abstract （2042）      PDF（pc） （1900KB）（289）       Knowledge map    Save Previous research on stock price volatility prediction relies on analyzing shallow features of financial news datasets and ignores the structural relationship between words in financial news, resulting in poor prediction performance. Aiming at this problem, we propose a stock price trend prediction model (Sent2Vec-DLSTM) based on a dual-stream long short-term memory network (LSTM) neural network. A vector generation model of emotional words called Sent2Vec is first proposed based on financial stock news data set and Harvard IV-4 emotion dictionary training, which is then combined with dual-stream LSTM neural network (DLSTM). In the experiment, the historical data of the S&P 500 index and the financial articles obtained by crawling are used to predict the trend of the S&P 500 index. the VietStock news and stock price data from cophieu68 are then used to predict the trend of the VN index. The results show that Sent2Vec-DLSTM outperforms existing models in stock price trend prediction. Reference | Related Articles | Metrics | Comments（0）

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An Electronic Contract Sharing Scheme Based on Blockchain ZHAO Haihong, YAO Zhongyuan, ZHU Weihua, ZHU Ziqiang, PAN Changfeng, SI Xueming Journal of Applied Sciences    2023, 41 (2): 359-368.   DOI: 10.3969/j.issn.0255-8297.2023.02.014 Abstract （2088）      PDF（pc） （725KB）（165）       Knowledge map    Save In order to solve the problems of data tampering or leakage in the storage and sharing of electronic contracts, an electronic contract sharing scheme based on blockchain is proposed. First, a proxy smart contract is constructed by combining the contract with the proxy re-encryption to replace the traditional proxy, and the secure sharing of electronic contract is decentralized. Inter planetary file system (IPFS) is then used to store the ciphertext of electronic contract, and the electronic contract index address is stored in the blockchain, which effectively alleviates the storage pressure of the blockchain. Finally, the security performance of the proposed scheme is analyzed. Reference | Related Articles | Metrics | Comments（0）

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Journal of Applied Sciences    2023, 41 (3): 1-0.   Abstract （615）      PDF（pc） （79KB）（64）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Journal of Applied Sciences    2023, 41 (3): 2-0.   Abstract （586）      PDF（pc） （47KB）（15）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Energy-Aware Resource Scheduling Method for Edge-Cloud Collaborative Computing YANG Jun, ZHU Yingwen Journal of Applied Sciences    2023, 41 (3): 369-377.   DOI: 10.3969/j.issn.0255-8297.2023.03.001 Abstract （734）      PDF（pc） （1627KB）（185）       Knowledge map    Save An energy-aware resource scheduling method for edge-cloud collaborative computing is proposed to address the issues of degraded real-time execution performance and high energy consumption when processing computationally complex tasks in edge computing. First, tasks are assigned to cloud computing and edge computing according to the real-time guaranteed rate. Then, an energy-aware resource scheduling strategy is proposed based on elastic resource characteristics to generate virtual resource configuration schemes for real-time tasks. Finally, simulation results verify the effectiveness of the proposed algorithm, which reduces energy consumption while ensuring real-time performance. Reference | Related Articles | Metrics | Comments（0）

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Fault Analysis and Retrieval of Message Based on Knowledge Graph JI Wenlu, DENG Xing, ZHU Hongqin, ZHAO Yang, JIANG Jielin Journal of Applied Sciences    2023, 41 (3): 378-390.   DOI: 10.3969/j.issn.0255-8297.2023.03.002 Abstract （777）      PDF（pc） （1490KB）（196）       Knowledge map    Save Aiming at the problems of complex fault analysis mode and increasing difficulty of fault removal caused by the expansion of the power grid, a fault analysis and retrieval of message based on knowledge graph is proposed. Firstly, the construction of power dispatching fault knowledge graph is completed by combining natural language processing technology with expert documents. Then the expert knowledge is stored in the graph in the form of atomic rules (indivisible rules) to achieve intelligent fault analysis and retrieval and assist maintenance personnel in decision-making, thereby improving the efficiency of business process. Finally, combined with the knowledge graph and log information, artificial intelligence is used to analyze the cause of failure, and the optimal solution is obtained from multiple potential solutions. Experimental results on both real and synthetic data sets show that the proposed method can achieve good results on fault analysis and retrieval in power dispatching. Reference | Related Articles | Metrics | Comments（0）

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Computing Offloading of Multi-dependent Tasks in Smart Cities PENG Kai, LIU Peichen, XU Xiaolong, ZHOU Xingyu Journal of Applied Sciences    2023, 41 (3): 391-404.   DOI: 10.3969/j.issn.0255-8297.2023.03.003 Abstract （720）      PDF（pc） （7892KB）（85）       Knowledge map    Save Aiming at the delay-sensitive multi-dependent task scheduling problem of smart cities, this paper proposes a smart city architecture empowered by edge computing and designs a computation offloading method to meet the scheduling requirements of tasks. Firstly, this paper first establishes a multi-dependent task model, as well as a latency constraint for the task and a load balancing constraint model for the smart city server. Secondly, agents that perceive dependencies between tasks are trained using deep reinforcement learning algorithms to make computational transfer decisions in real-time. Finally, a series of experiments are conducted to verify the effectiveness of this method in latency and energy consumption optimization. Reference | Related Articles | Metrics | Comments（0）

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Game Theory and Particle Swarm Optimization Based Task Offloading Method in Mobile-Edge Computing LI Han, MENG Shunmei, CAI Zhicheng Journal of Applied Sciences    2023, 41 (3): 405-418.   DOI: 10.3969/j.issn.0255-8297.2023.03.004 Abstract （773）      PDF（pc） （1603KB）（115）       Knowledge map    Save Mobile-edge computing (MEC) is an innovative computing paradigm. Mobile devices can reduce local computation energy consumption and delay by offloading computation intensive tasks to the edge servers. In this paper, we first study the computation offloading problem for multiple mobile devices with independent task sets in the dense area of microcell base stations, where each microcell base station is equipped with a computationally limited MEC server. To reduce the task sets computation energy consumption and delay of the mobile devices as much as possible, adopting a game theoretic approach, the problem is formulated as a non-cooperative multi-mobile-device computation offloading strategy game. Through analysis, the Nash equilibrium existence and the finite improvement property of the game are proved. Then, we design a game theory based distributed computation offloading algorithm, namely GDCOA, and introduce a particle swarm optimization (PSO) based improving computation offloading policy algorithm named PSOIPA in it. GDCOA can reach an equilibrium state after a finite number of iterations. Finally, the simulation and comparison experiments corroborate that the proposed algorithm GDCOA in this paper can achieve better computation offloading performance. Reference | Related Articles | Metrics | Comments（0）

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Multi-swarm Particle Swarm Optimization for Task Scheduling in Supply Chain Datacenter ZENG Lei, BAI Jinming, LIU Qi Journal of Applied Sciences    2023, 41 (3): 419-430.   DOI: 10.3969/j.issn.0255-8297.2023.03.005 Abstract （728）      PDF（pc） （1585KB）（104）       Knowledge map    Save In order to deal with the problems of low service efficiency brought by the increasing scale of datacenters and task demands, a load balancing multi-swarm PSO task scheduling approach is proposed. Through the improved fitness function, the maximum completion time of the task and the variance of the completion time among machines are optimized to improve the cluster’s load balance. A novel adaptive inertia weights method is designed to enhance particle search efficiency and algorithm convergence speed. Meanwhile, a new particle initialization method is adopted to improve the quality and diversity of the initial solution. Multi-swarm particle collaborative search is further used to bring the final result closer to the optimal solution. The performance of the proposed algorithm is verified and compared with others based on the public dataset of Alibaba datacenter. The experimental results show that the method can improve task scheduling efficiency of datacenters in diversified supply chain environments. Reference | Related Articles | Metrics | Comments（0）

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Improved Hashed Timelock Contract Based on Miners ZUO Yuxuan, QIANG Zhenping, DAI Fei, SU Shiqi, LIANG Zhihong Journal of Applied Sciences    2023, 41 (3): 431-447.   DOI: 10.3969/j.issn.0255-8297.2023.03.006 Abstract （704）      PDF（pc） （900KB）（63）       Knowledge map    Save To overcome the challenging problem of the inability of hashed timelock contract to realize cross-chain asset transfers, an improved cross-chain protocol based on miners is proposed. The protocol incorporates elliptic curve cryptography during the atomic swap process, enabling miners to generate and lock transactions on the target chain, thereby completing cross-chain asset transfers. Additionally, a competitive selection algorithm and a reward-punishment algorithm for miner nodes are integrated into the protocol to implement a new proof-of-coin-trust consensus mechanism. Finally, the protocol’s smart contracts and their functional capabilities are described in detail. Simulation results based on Ethereum show that cross-chain asset transfer is safely completed, and honest miners are motivated to actively participate in cross-chain transactions. Reference | Related Articles | Metrics | Comments（0）

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Building Change Detection in Remote Sensing Images Based on Semantic Segmentation YIN Meijie, NI Cui, WANG Peng, ZHANG Guangyuan Journal of Applied Sciences    2023, 41 (3): 448-460.   DOI: 10.3969/j.issn.0255-8297.2023.03.007 Abstract （876）      PDF（pc） （6613KB）（256）       Knowledge map    Save Remote sensing image change detection is to use multi-temporal images to determine the changes of objects or phenomena within a certain period of time, and to provide qualitative and quantitative information on spatial changes of objects. Traditional remote sensing image change detection methods are mainly based on ground texture and spatial features, which is difficult to accurately identify new buildings in remote sensing images. Therefore, this paper adopts a building change detection method based on UNet network. Firstly, the lightweight efficient channel attention network (ECANet) is injected into the original UNet network model to adjust and optimize the network structure and improve the accuracy of image segmentation. The parameters of SENet are then tuned to enhance the accuracy of building change detection in remote sensing images. Experiments on a high-resolution dataset LIVER-CD show that the accuracy of the proposed method reaches a semantic segmentation accuracy of 99.03% and a building change detection accuracy of 98.62%. Compared with other methods, the proposed method can effectively enhance the effective features of images and improve the detection accuracy of ground buildings in remote sensing images. Reference | Related Articles | Metrics | Comments（0）

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