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Review of Steganalysis for Digital Images WANG Zichi, LI Bin, FENG Guorui, ZHANG Xinpeng Journal of Applied Sciences    2024, 42 (5): 723-732.   DOI: 10.3969/j.issn.0255-8297.2024.05.001 Abstract （539）      PDF（pc） （2695KB）（503）       Knowledge map    Save Digital steganography plays a crucial role in securely transmitting confidential data by concealing it within common multimedia, such as images, videos, and audio, to facilitate covert communication. To discover the covert communication of steganography, the technique of steganalysis can be employed. Steganalysis determines whether a given multimedia object contains secret data according to the statistical anomaly of stego data caused by steganography. In recent years, both steganography and steganalysis have made significant progress and development in their mutual confrontation, particularly in the context of the growing prevalence of digital images on social networks. Focusing on digital images, this paper sorts out the development of steganalysis in the past decade, and reviews the traditional steganalysis and deep learning based-steganalysis. Then, the limitations of each approach are discussed. Finally, the study offers insights into the prospective development trends in steganalysis. Reference | Related Articles | Metrics | Comments（0）

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Multi-modal Diagnosis Method of Alzheimer’s Disease LI Weihan, HOU Beiping, HU Feiyang, ZHU Bihong Journal of Applied Sciences    2023, 41 (6): 1004-1018.   DOI: 10.3969/j.issn.0255-8297.2023.06.008 Abstract （465）      PDF（pc） （3577KB）（492）       Knowledge map    Save The current grading methods for Alzheimer’s disease（AD）, Early Mild Cognitive Impairment（EMCI）, and Normal Control（NC） suffer from difficulties recognizing EMCI and low multi-classification accuracy. To address these issues, a brain region feature extraction method is proposed, and an AD multi-modal classification model is designed with a fusion of ResNet network. Brain MRI images are spatially registered, segmented by Bayesian and Gaussian mixture models to obtain gray matter, the regions with the greatest difference are selected as the feature image area, and images and biomarkers are processed by the classification model. The proposed method improves performance by at least 5% and achieves an accuracy of 95.5%, 93.5%, and 86.3% for AD＆NC, AD＆EMCI,and AD＆EMCI＆NC classification, respectively, surpassing any single-modal network and verifying the effectiveness of this method. Reference | Related Articles | Metrics | Comments（0）

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Land Cover Classification of Sentinel-2 Image Based on Multi-feature Convolution Neural Network HUANG Xianpei, MENG Qingxiang Journal of Applied Sciences    2023, 41 (5): 766-776.   DOI: 10.3969/j.issn.0255-8297.2023.05.004 Abstract （450）      PDF（pc） （1320KB）（486）       Knowledge map    Save The 10 m resolution Sentinel-2 images takes the spectral values of the image as input in the original GoogLeNet without treating the ground objects in the image as a whole. To leverage object-oriented features in Sentinel-2 remote sensing image classification, this paper proposes an Object-oriented GoogLeNet network structure based on multiple features. Object-oriented GoogLeNet incorporates object-oriented spectral and shape features, and fully utilizes the shape features of differences between different ground objects for classification. On the data set of cloudless images in Wuhan and its surrounding areas, the overall accuracy of the classification results of Object-oriented GoogLeNet model has increased by 1.773% compared to GoogLeNet. The results show that the model with object-oriented features enhances the classification performance of Sentinel-2 remote sensing images. Reference | Related Articles | Metrics | Comments（0）

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Cross-Modal Person Re-identification Driven by Cross-Channel Interactive Attention Mechanism in Dual-Stream Networks HE Lei, LI Fengyong, QIN Chuan Journal of Applied Sciences    2024, 42 (5): 884-892.   DOI: 10.3969/j.issn.0255-8297.2024.05.014 Abstract （250）      PDF（pc） （4390KB）（485）       Knowledge map    Save Existing cross-modal person re-identification methods often fail to take into account the difference of target person between modes and within modes, making it difficult to further improve the retrieval accuracy. To solve this problem, this paper introduces the cross-channel interaction attention mechanism to enhance the robust extraction of person features, effectively suppresses the extraction of irrelevant features and achieves more discriminative feature expression. Furthermore, hetero-center triplet loss, triplet loss and identity loss are combined for supervised learning, effectively integrating the intermodal and intra-class differences in person features. Experimental results demonstrate the effectiveness of the proposed method, which outperforms seven existing methods on two standard datasets, RegDB and SYSU-MM01. Reference | Related Articles | Metrics | Comments（0）

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Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism ZHANG Chunsen, ZHU Jiangle, ZHANG Xuefen, LIU Xudong, SHI Shu Journal of Applied Sciences    2023, 41 (5): 789-800.   DOI: 10.3969/j.issn.0255-8297.2023.05.006 Abstract （364）      PDF（pc） （10129KB）（452）       Knowledge map    Save Aiming at the problems of existing DEM data void filling algorithms, such as discontinuous repair effect, narrow applicable null value range and loss of detail reconstruction, this paper proposes a DEM void filling method integrating self-attention mechanism with generative adjunctive network. Firstly, a self-attention mechanism is constructed to extract DEM data feature information to improve the elevation discontinuity and texture detail loss of DEM cavity filling results. Secondly, symmetric convolutional and deconvolution network structures are used in the generator to ensure the generation of high reliability data to realize the filling of the void region, and the discriminator is used to realize the pre-classification of the filling results. Finally, combined with the reconstruction of loss function and the generation of adversarial loss function, the network training was carried out to improve the robustness of DEM cavity filling results to outliers and enhance the regression ability of the model. The experimental results show that compared with the filling results of spatial interpolation and deep learning, the proposed method can greatly improve the filling accuracy and effectively solve the problems of holes in the original data. Reference | Related Articles | Metrics | Comments（0）

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Dynamic Byzantine Fault Tolerance Algorithm Based on Reputation and Clustering WU Guangfu, YANG Zi, HUANG Baozhu Journal of Applied Sciences    2023, 41 (6): 1046-1057.   DOI: 10.3969/j.issn.0255-8297.2023.06.011 Abstract （389）      PDF（pc） （1143KB）（430）       Knowledge map    Save This paper presents a dynamic Byzantine fault-tolerant consensus algorithm based on reputation and clustering. The existing practical algorithms lack a response mechanism for joining or exiting nodes, leading to decreased consensus efficiency with a large number of nodes. To address this issue, the proposed algorithm utilizes a clustering algorithm to divide nodes into K consensus regions, improving efficiency when more nodes participate in consensus. Additionally, K reliable proxy nodes are selected based on high reputation, while low reputation nodes are eliminated to reduce the probability of Byzantine nodes becoming main nodes. The node classification process combines the reputation evaluation algorithm to select K proxy nodes, enhancing system stability and security. Simulation results demonstrate that compared to PBFT, the proposed algorithm supports dynamic node joining and exiting, with lower communication cost, transaction delay, and higher throughput. It also exhibits better fault tolerance and scalability. Reference | Related Articles | Metrics | Comments（0）

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Constructing Sentiment Lexicon in the Education Field by Integrating Skip-Gram and R-SOPMI CHEN Jun, XI Ningli, LI Jiamin, WAN Xiaorong Journal of Applied Sciences    2023, 41 (5): 870-880.   DOI: 10.3969/j.issn.0255-8297.2023.05.012 Abstract （307）      PDF（pc） （710KB）（408）       Knowledge map    Save This paper presents a method for constructing a fine-grained Sentiment Lexicon in Education to address specific emotional issues in sentiment analysis of educational feedback texts. First, we construct an educational domain corpus, which contains emotional features in both formal and informal domains. Second, a fusion-based method is proposed to construct a domain Sentiment Lexicon by identifying linguistic probability features and statistical probability features of words through sentiment classification. The proposed repetitive semantic orientation pointwise mutual information (R-SOPMI) algorithm enhances SO-PMI for sentiment classification, enabling co-occurrence multi-category sentiment classification. Finally, a fine-grained Sentiment Lexicon in the field of education is obtained, and the dictionary expands to 39 138 emotional words. Experiment results show that except for “anger”, the F1 of the emotion category of the constructed educational field emotion dictionary is all higher than 78.09%. Compared with a general dictionary, the Macro_Precision, Macro_Recall and Macro_F1 increased by 21.95%, 2.50% and 13.01%, respectively. The fusion feature method effectively extracts domain features, facilitating the construction of a comprehensive fine-grained domain dictionary. Reference | Related Articles | Metrics | Comments（0）

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Charge-Discharge Optimization for Shared Electric Vehicles Under Carbon Trading Regulation LI Junxiang, HE Wenting, WANG Jinling Journal of Applied Sciences    2023, 41 (5): 896-910.   DOI: 10.3969/j.issn.0255-8297.2023.05.014 Abstract （209）      PDF（pc） （1701KB）（385）       Knowledge map    Save In this paper, a charge-discharge optimization model of electric vehicles participating in carbon trading market and peak-shaving auxiliary service market is proposed for the study of shared electric vehicles under the switching mode. In the upper model, EV charge and discharge scheduling is carried out with the goal of minimizing the daily operating cost of EV operators, while the lower model continues to optimize the scheduling results of the upper layer with the goal of minimizing the fluctuation of power grid load. Then, this paper compared and analyzed the charge-discharge schemes from the perspectives of EV operators and the power grid. The carbon emissions produced by electric vehicle operators are compared with that by gas-powered vehicle operators over one operating cycle. Finally, simulation results show that the proposed model can meet users’ travel needs while reducing the cost of operators and the load fluctuation of power grid. Reference | Related Articles | Metrics | Comments（0）

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News Recommendation Algorithm Incorporating Headline Sentiment and Topic Characteristics AI Jun, HONG Xingqi Journal of Applied Sciences    2024, 42 (5): 810-822.   DOI: 10.3969/j.issn.0255-8297.2024.05.008 Abstract （251）      PDF（pc） （1748KB）（379）       Knowledge map    Save Traditional lexicon-based news recommendation algorithms often ignore the emotional nuances present in words beyond the confines of the dictionary. This oversight can lead to issues such as diminished prediction accuracy and subpar sorting performance. To address these challenges, this paper introduces a heuristic approach to deduce the sentiment of unfamiliar words and devises a news recommendation algorithm to verify its feasibility. A tripartite graph model is constructed to propagate sentiment from a sentiment dictionary to individual words and obtain the headline sentiment. In addition, the bag-of-words model is used to extract topic features from the headlines. The sentiment similarity and topic similarity between headlines are calculated, consolidating these into a comprehensive similarity evaluation index. The news with higher similarity to the target news is then selected as the neighbor. The algorithm predicts the hourly average click volume of the target news by considering the hourly average click volume of neighbors, treating this as the predicted score for the target news. Finally, users receive a selection of high-scoring news articles. Validation using real data from NetEase News confirms the feasibility and effectiveness of our algorithm. Compared with other algorithms, our algorithm has shown improvements in the optimal accuracy of mean absolute error (MAE) by 2.2% to 3.4%, root mean square error (RMSE) by 2.3% to 2.9%, and the mean score of normalized discounted cumulative gain (NDCG) by 0.7% to 1.8%, respectively. Reference | Related Articles | Metrics | Comments（0）

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Research on Hash Algorithm Heterogeneous Reconfigurable High Energy Efficiency Computing System ZHENG Bowen, NIE Yi, CHAI Zhilei Journal of Applied Sciences    2023, 41 (6): 1031-1045.   DOI: 10.3969/j.issn.0255-8297.2023.06.010 Abstract （336）      PDF（pc） （829KB）（376）       Knowledge map    Save To meet the high-speed computing requirements of different hash algorithms and the combination of different hash algorithms in various application scenarios, a highefficiency computing system for Hash algorithm with heterogeneous and reconfigurable acceleration end hardware is presented in this paper. The computing system consists of an algorithm hardware acceleration module, a data transmission module, and a multithread management module. The computing energy efficiency is improved through the dynamically reconfigurable hardware design. Experimental results on the Intel Stratix10FPGA heterogeneous computing platform demonstrate significant performance and energy efficiency improvements. Compared with the Intel Core I7-10700 CPU, the system achieves up to 18.7 times performance improvement and 34 times energy efficiency improvement.Compared with the NVIDIA GTX 1650 SUPER GPU, the system achieves up to 2 times performance improvement and 5.6 times energy efficiency improvement. Reference | Related Articles | Metrics | Comments（0）

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Dual Authorization Sharing Scheme of Searchable Electronic Medical Data Based on Consortium Blockchain MA Xue, PAN Heng, YAO Zhongyuan, SI Xueming Journal of Applied Sciences    2023, 41 (5): 881-895.   DOI: 10.3969/j.issn.0255-8297.2023.05.013 Abstract （289）      PDF（pc） （816KB）（373）       Knowledge map    Save Retrieval of electronic medical record (EMR) in cloud environments induces security problems and patient privacy data leakage problems. To this end, a dual-authorization sharing scheme for EMR that supports on-chain keyword ciphertext retrieval is proposed. In the scheme, original medical data ciphertexts are stored in a cloud, and the information of medical data keyword index is constructed with searchable encryption technology and stored on the blockchain. On the premise of obtaining the hospital retrieval authority, a keyword retrieval algorithm under distributed conditions is used to realize the secure re trieval of the medical data ciphertexts. Based on searchable proxy re-encryption algorithm, an authorization on-chain method for the electronic medical data is proposed, which en sures the access control of patients’ medical data and realizes a double authorization of the shared medical data by the hospital and patients. Finally, random oracle model is used to verify the semantic security of the scheme under the assumption of n-QBDH, and the superiority of the scheme in terms of computational cost is proved by experiments. Reference | Related Articles | Metrics | Comments（0）

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Auditable and Traceable Blockchain Privacy Protection Model under Zero-Knowledge Proof WU Meng, QI Yong Journal of Applied Sciences    2024, 42 (4): 598-612.   DOI: 10.3969/j.issn.0255-8297.2024.04.004 Abstract （262）      PDF（pc） （558KB）（373）       Knowledge map    Save In order to address the issues of sensitive data exposure due to shared ledgers among nodes in a blockchain network, alongside the inability to audit and trace encrypted privacy data, a blockchain privacy protection model based on directed graphs and zeroknowledge proofs has been proposed. This model extends the open-source Hyperledger Fabric framework and effectively inherits the features of Fabric. By encrypting on-chain transaction information and utilizing Pedersen commitments and Schnorr-type zero-knowledge proofs, it generates proofs of balance, traceability, asset ownership, and consistency to provide fast and verifiable privacy data audits. The model utilizes a directed graph structure to construct a transaction graph, thus achieving traceability of transaction information on the blockchain. Moreover, it generates proofs to validate the correctness of forward tracing transactions. Experimental results demonstrate that the proposed model achieves complete audit and traceability on Fabric at a cost of less than 10% throughput, outperforming existing related models. Reference | Related Articles | Metrics | Comments（0）

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Target Counting Method Based on UAV View in Large Area Scenes XIE Ting, ZHANG Shoulong, DING Laihui, XU Zhiwei, YANG Xiaogang, WANG Shengke Journal of Applied Sciences    2024, 42 (1): 67-82.   DOI: 10.3969/j.issn.0255-8297.2024.01.006 Abstract （378）      PDF（pc） （9343KB）（372）       Knowledge map    Save In recent years, unmanned aerial vehicles (UAVs) have been widely used in the field of crowd counting due to their high flexibility and maneuverability. However, most of the existing crowd counting methods are based on single viewpoints, with limited studies focusing on multi-viewpoint counting in large-scale, multi-camera scenes. To solve this problem, this paper proposes a UAV-based target counting method which can accurately count the number of targets in a given scene. Specifically, this study selects a sea-front area for data acquisition, utilizes deep learning technology for target detection and image stitching fusion on the acquired images. The detection information is then mapped onto the spliced image, and a counting algorithm is employed to fulfill the counting task for the regional scene. The effectiveness of the counting algorithm based on target detection is validated through experiments conducted on both public dataset and the dataset produced in this paper. Reference | Related Articles | Metrics | Comments（0）

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Implementation and Acceleration of Linear KNN Algorithm for Laser Point Cloud Based on FPGA CHEN Xiaoyu, YANG Mengxue, LI Changdui, ZHAO Pengcheng Journal of Applied Sciences    2023, 41 (5): 831-839.   DOI: 10.3969/j.issn.0255-8297.2023.05.009 Abstract （385）      PDF（pc） （1729KB）（371）       Knowledge map    Save To address the time-consuming problem of 3D laser point cloud for linear K-nearest neighbor (KNN) search, a fast KNN search method based on multi-processor system on chip (MPSoC) field-programmable gate array (FPGA) is proposed. Firstly, the implementation framework of 3D laser point cloud KNN algorithm based on MPSoC FPGA is given. Then, the design ideas and implementation process of each module are elaborated. Finally, the proposed method is validated through tests and verification on platform built based on MZU15A development board and TM-LIDAR-16. Results demonstrate that the 3D laser point cloud KNN algorithm based on MPSoC FPGA can reduce time consumption while ensuring the accuracy of neighboring point search. Reference | Related Articles | Metrics | Comments（0）

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Quantum Attacks on Symmetric Cryptosystems FENG Xiaoning, WU Hongyu Journal of Applied Sciences    2024, 42 (1): 39-52.   DOI: 10.3969/j.issn.0255-8297.2024.01.004 Abstract （324）      PDF（pc） （682KB）（356）       Knowledge map    Save This paper undertakes an investigation of recent research trends in quantum attacks on symmetric encryption schemes, offering an analysis of the connections between mainstream attack methods and various literature sources. Mainstream attack methods are systematically categorized into three types: quantum period attacks, Grover algorithmrelated attacks, and quantum differential attacks. For each category, representative attack methods are introduced, accompanied by an elucidation of the core concepts underlying each approach. Furthermore, we contemplate future research directions within this domain, considering potential advancements in light of existing attack schemes. Reference | Related Articles | Metrics | Comments（0）

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Sign Language Recognition Based on Two-Stream Adaptive Enhanced Spatial Temporal Graph Convolutional Network JIN Yanliang, WU Xiaowei Journal of Applied Sciences    2024, 42 (2): 189-199.   DOI: 10.3969/j.issn.0255-8297.2024.02.001 Abstract （286）      PDF（pc） （1979KB）（354）       Knowledge map    Save Aiming at the issues of poor information representation ability and incomplete information during the extraction of sign language features, this paper designs a two-stream adaptive enhanced spatial temporal graph convolutional network (TAEST-GCN) for sign language recognition based on isolated words. The network uses human body, hands and face nodes as inputs to construct a two-stream structure based on human joints and bones. The connection between different parts is generated by the adaptive spatial temporal graph convolutional module, ensuring the full utilization of the position and direction information. Meanwhile, an adaptive multi-scale spatial temporal attention module is built through residual connection to further enhance the convolution ability of the network in both spatial and temporal domain. The effective features extracted from the dual stream network are weighted and fused to classify and output sign language vocabulary. Finally, experiments are carried out on the public Chinese sign language isolated word dataset, achieving accuracy rates of 95.57% and 89.62% in 100 and 500 categories of words, respectively. Reference | Related Articles | Metrics | Comments（0）

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Entity Relationship Extraction Framework Based on Pre-trained Large Language Model and Its Application WEI Wei, JIN Chenggong, YANG Long, ZHOU Mo, MENG Xiangzhu, FENG Hui Journal of Applied Sciences    2025, 43 (1): 20-34.   DOI: 10.3969/j.issn.0255-8297.2025.01.002 Abstract （362）      PDF（pc） （1446KB）（354）       Knowledge map    Save Entity relationship extraction is a crucial foundation for building large-scale knowledge graphs and domain-specific datasets. This paper proposes an entity relationship extraction framework based on pre-trained large language models (PLLM-RE) for relation extraction in circular economy policies. Within this framework, entity recognition of circular economy policy texts is performed based on the model RoBERTa. Subsequently, the bidirectional encoder representation from Transformers (BERT) is employed for entity relation extraction, facilitating the construction of a knowledge graph in the field of circular economic policies. Experimental results demonstrate the framework outperforms the baseline models including BiLSTM-ATT, PCNN, BERT and ALBERT in task of entity relationship extraction for circular economy policies. These findings validate the adaptability and superiority of the proposed framework, providing new ideas for information mining and policy analysis in the field of circular economy resources in the future. Reference | Related Articles | Metrics | Comments（0）

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Design of the Autonomous Navigation Test System for Unmanned Surface Vehicle Combining Virtual and Reality LIU Hongxiao, YANG Cheng, TAN Aidi, LU Jing, LI You Journal of Applied Sciences    2023, 41 (6): 1068-1077.   DOI: 10.3969/j.issn.0255-8297.2023.06.013 Abstract （410）      PDF（pc） （3284KB）（351）       Knowledge map    Save The cost of on-site testing for unmanned surface vehicle（USV） autonomous navigation systems is high, and pure virtual simulation tests lack authenticity in marine dynamics simulation. To address these challenges and improve the effectiveness-cost ratio of USV autonomous navigation system testing, a hybrid testing system combining virtual and real elements is proposed. The designed system leverages virtual simulation technology for task scene construction and environmental sensing sensors, while real-in-loop technology is used for the transmission system and marine dynamics of the USV. The system achieves real-time position and attitude synchronization between the digital twins of the USV and its physical entities through virtual and real space registration technology. Simulation results validate the correctness and rationality of the system, demonstrating its effectiveness in testing USV autonomous navigation systems while reducing costs. Reference | Related Articles | Metrics | Comments（0）

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Infrared Dim and Small Target Detection Algorithm Based on Low-Rank and Reweighted Sparse Representation YANG Yadong, HUANG Shengyi, TAN Yihua Journal of Applied Sciences    2023, 41 (5): 753-765.   DOI: 10.3969/j.issn.0255-8297.2023.05.003 Abstract （301）      PDF（pc） （2679KB）（346）       Knowledge map    Save The detection of infrared dim and small targets is one of the key technologies in the infrared warning system. It remains challenging to accurately, quickly, and robustly detect dim and small targets. This paper proposes an infrared dim and small target detection algorithm based on low-rank and reweighted sparse representation. The algorithm formulates a new optimization equation to more accurately describe the rank of the background matrix and utilizes the structure tensor to extract local prior information. Experimental results show that the proposed algorithm improves the accuracy, speed, and robustness of detecting dim and small infrared targets. Reference | Related Articles | Metrics | Comments（0）

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Optical Fiber Memory Based on Phase Change Material Ge2Sb2Te5 YIN Jiayue, CHENG Siying, LOU Cunkai, YANG Bozhi, ZHANG Yu Journal of Applied Sciences    2023, 41 (5): 727-737.   DOI: 10.3969/j.issn.0255-8297.2023.05.001 Abstract （298）      PDF（pc） （8717KB）（317）       Knowledge map    Save The typical functions of optical fiber are communication and sensing, this paper gives the function of optical fiber storage and designs an all-fiber memory to meet the needs of intelligent development of optical fiber communication systems. In this paper, single-mode fiber (SMF) and multimode fiber (MMF) are used to coaxial soldering, and Ge2Sb2Te5 (GST) material is deposited on the end face of MMF by the magnetron sputtering method, then the end face will emit the Bessel-like beam that can switch the phase state of GST, the length of MMF affects the end face light field, and finally 1.5 mm long MMF is selected to achieve non-volatile memory with arbitrary level access ability, high optical contrast, good stability, and high repeatability. The memory can realize 11 levels of storage randomly and stably, with an optical contrast of 50% and repeated cycles at least 34 times. Reference | Related Articles | Metrics | Comments（0）