计算机科学最新文献

{"title":"Unsupervised Adaptive Hypergraph Correlation Hashing for multimedia retrieval","authors":"Yunfei Chen ,&nbsp;Yitian Long ,&nbsp;Zhan Yang ,&nbsp;Jun Long","doi":"10.1016/j.ipm.2024.103958","DOIUrl":"10.1016/j.ipm.2024.103958","url":null,"abstract":"<div><div>Cross-modal hashing has attracted widespread attention from researchers due to its capabilities to handle large volumes of heterogeneous multimedia information with fast retrieval speed and low storage cost. However, current cross-modal hashing methods still face issues such as incomplete embedding of semantic correlation information and long parameter tuning cycles. To address these problems, we propose a method called Unsupervised Adaptive Hypergraph Correlation Hashing (UAHCH). First, the hypergraph-based correlation enhanced hashing constructs a hypergraph based on semantic information and correlation information, leveraging a hypergraph neural network to integrate the hypergraph information into the hash codes, ensuring the richness of the semantics and the integrity of correlation relationships. Next, the fast parameter adaptive strategy is designed for the automated optimization of neural network parameters for the UAHCH method and various neural network models, achieving optimal performance more efficiently. Finally, comprehensive experiments are conducted on widely used datasets. The results show that the proposed UAHCH method achieves superior performance, with average improvements of 3.06% on MIRFlickr, 1.45% on NUS-WIDE, and 4.65% on MSCOCO compared to the latest baseline methods. The code has been made publicly available at <span><span>https://github.com/YunfeiChenMY/UAHCH</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 2","pages":"Article 103958"},"PeriodicalIF":7.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Enabled Compact Frequency‐Tunable Triple‐Band Hexagonal‐Shaped Graphene Antenna for THz Communication","authors":"Jayant Kumar Rai, Uditansh Patel, Poonam Tiwari, Pinku Ranjan, Rakesh Chowdhury","doi":"10.1002/dac.6044","DOIUrl":"https://doi.org/10.1002/dac.6044","url":null,"abstract":"In this article, a compact triple‐band frequency‐tunable (FT) hexagonal‐shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide () substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm<jats:sup>2</jats:sup>. The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and <jats:italic>SiO</jats:italic><jats:sub><jats:italic>2</jats:italic></jats:sub> are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band‐1 is 2.51–5.05 THz, Band‐2 is 5.99–7.43 THz, and Band‐3 is 7.94–9.63 THz. The bandwidth in Band‐1, Band‐2, and Band‐3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band‐1, Band‐2, and Band‐3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K‐nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the <jats:italic>S</jats:italic><jats:sub>11</jats:sub> of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra‐speed short‐distance communication applications in the THz region.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":"112 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.","authors":"Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa","doi":"10.1080/0954898X.2024.2424248","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2424248","url":null,"abstract":"<p><p>Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time domain correlation entropy image conversion: A new method for fault diagnosis of vehicle-mounted cable terminals","authors":"Kai Liu ,&nbsp;Like Fan ,&nbsp;Guangbo Nie ,&nbsp;Kai Wang ,&nbsp;Bo Gao ,&nbsp;Jianmin Fu ,&nbsp;Junbin Mu ,&nbsp;Guangning Wu","doi":"10.1016/j.compeleceng.2024.109865","DOIUrl":"10.1016/j.compeleceng.2024.109865","url":null,"abstract":"<div><div>The identification of partial discharge (PD) in cable terminals is crucial for the safe operation of trains. However, the complexity of the operational environment and the similarity of PD signals make defect identification challenging. Consequently, this paper proposes a Time-domain Local Correlation Entropy Image (T-LCEI) transformation method, which constructs an entropy matrix to convert raw PD signals into images. These images embed feature and bandwidth information from the original PD data, significantly enhancing the ability to differentiate between similar PD signals. Furthermore, the method combines a Dual Attention Convolutional Neural Network (DA_CNN) for the effective classification of correlation entropy images. Experimental results demonstrate that this approach achieves an average classification accuracy of 99.69% across four typical PD defect datasets, with a testing accuracy of 97.75% in practical scenarios. Compared to existing PD detection methods, T-LCEI offers significant improvements in effectiveness and discriminability. The integration of DA_CNN further enhances recognition accuracy. The study demonstrates that the proposed method excels in PD defect identification, providing reliable technical support for on-site fault detection and maintenance, thereby significantly improving the operational safety of cable terminals.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109865"},"PeriodicalIF":4.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fiber laser system for Rb atomic fountain clock","authors":"Yang Bai ,&nbsp;Jun Ruan ,&nbsp;Hui Zhang ,&nbsp;Dan-dan Liu ,&nbsp;Si-Chen Fan ,&nbsp;Xin-Liang Wang ,&nbsp;Yong Guan ,&nbsp;Jun-Ru Shi ,&nbsp;Shou-gang Zhang","doi":"10.1016/j.yofte.2024.104043","DOIUrl":"10.1016/j.yofte.2024.104043","url":null,"abstract":"<div><div>A compact and robust all-fiber laser system comprising fiber-optical components for a Rb atomic fountain clock is demonstrated. The laser sources were based on the frequency doubling of two seed lasers at a wavelength of 1560 nm, which were locked using digital frequency locking and modulation transfer spectroscopy. During the Sisyphus cooling period, the PZT control voltage of the fiber laser was ramped to detune the laser frequency to 170 MHz, and we get an atomic temperature of 1.9 □K. A series of customized optical fiber splitters, acousto-optic modulators (AOMs), and shutters were integrated into two 2U enclosures as cooling and repumping light modules. The entire laser system was integrated into a 22U cabinet and was characterized via polarization, power, and frequency stability measurements over 100 days. Apply the laser system to the Rb atomic fountain clock, which exhibited a frequency stability of less than 4.5 × 10^-16 at the interval of 24 h.</div></div>","PeriodicalId":19663,"journal":{"name":"Optical Fiber Technology","volume":"88 ","pages":"Article 104043"},"PeriodicalIF":2.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Bayesian ECG denoising using adaptive covariance estimation and nonlinear Kalman Filtering","authors":"Hamed Danandeh Hesar ,&nbsp;Amin Danandeh Hesar","doi":"10.1016/j.compeleceng.2024.109869","DOIUrl":"10.1016/j.compeleceng.2024.109869","url":null,"abstract":"<div><div>Model-based Bayesian methods for denoising electrocardiogram (ECG) signals have demonstrated promise in preserving ECG morphology and diagnostic properties. These methods are effective for preserving and enhancing the features of ECG signals. However, their performance heavily relies on accurately selecting model parameters, particularly the state and measurement noise covariance matrices. Some of these frameworks also involve computationally intensive computations and loops for state estimation. To address these problems, in this study, we propose a novel approach to improve the performance of several model-based Bayesian frameworks, including the extended Kalman filter/smoother (EKF/EKS), unscented Kalman filter/smoother (UKF/UKS), cubature Kalman filter/smoother (CKF/CKS), and ensemble Kalman filter/smoother (EnKF/EnKS), specifically for ECG denoising tasks. Our methodology dynamically adjusts the state and measurement covariance matrices of the filters using outputs from nonlinear Kalman-based filtering methods. For each filter, we develop a unique approach based on the theoretical foundations of that filter. Additionally, we introduce two distinct strategies for updating these matrices, considering whether the noise in the signals is stationary or nonstationary. Furthermore, we propose a computationally efficient method that significantly reduces the calculation time required for implementing CKF/CKS, UKF/UKS, and EnKF/EnKS frameworks, while maintaining their denoising performance. Our approach can achieve a 50 % reduction in computation time for these frameworks, effectively making them twice as fast as their original implementations We thoroughly evaluated our approach by comparing denoising performance between the original filters and their adaptive versions, as well as against the state-of-the-art marginalized particle extended Kalman filter (MP-EKF). The evaluation utilized various normal ECG segments obtained from different records. The results demonstrate that the adaptive adjustment of covariance matrices significantly improves the denoising performance of nonlinear Kalman-based frameworks in both stationary and non-stationary environments, achieving performance comparable to that of the MP-EKF framework.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109869"},"PeriodicalIF":4.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Study of Handheld Augmented Reality Interaction Techniques for Developing AR Instructions using AR Authoring Tools","authors":"Kay Hönemann ,&nbsp;Björn Konopka ,&nbsp;Michael Prilla ,&nbsp;Manuel Wiesche","doi":"10.1016/j.compind.2024.104205","DOIUrl":"10.1016/j.compind.2024.104205","url":null,"abstract":"<div><div>Augmented Reality (AR) instructions offer companies tremendous savings potential. However, developing these AR instructions has traditionally been challenging due to the need for programming skills and spatial knowledge. To address this complexity, industry and academia are working to simplify AR development. A crucial aspect of this process is the accurate positioning of AR content within the physical environment, which requires effective AR interaction techniques that enable full 3D manipulation of AR elements. In this study, we conducted an experimental comparison of three different AR interaction techniques with 55 participants to empirically assess their performance, workload, and user satisfaction across tasks related to AR instruction development. Our findings contribute to the design of future AR instructions and AR authoring tools, emphasizing the importance of evaluating AR interaction techniques that can be utilized by users without programming experience tailored to the specific needs of the intended application domain.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104205"},"PeriodicalIF":8.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chimney detection and size estimation from high-resolution optical satellite imagery using deep learning models","authors":"Che-Won Park ,&nbsp;Hyung-Sup Jung ,&nbsp;Won-Jin Lee ,&nbsp;Kwang-Jae Lee ,&nbsp;Kwan-Young Oh ,&nbsp;Joong-Sun Won","doi":"10.1016/j.engappai.2024.109686","DOIUrl":"10.1016/j.engappai.2024.109686","url":null,"abstract":"<div><div>This study shows an efficient method to estimate the location and size of chimneys from high-resolution satellite optical images using deep learning models. Korean multi-purpose satellite (KOMPSAT) −3 and -3A satellite images with spatial resolutions of 0.7 m and 0.55 m were used for model performance estimation, and the You Only Look Once version 8 (YOLOv8) and Residual Network (ResNet) regression models were integrated for the detection and size estimation of the chimneys. In the chimney detection and size estimation, we compared the model performances between 1) imbalanced and balanced data, 2) South Korea and Thailand data, and 3) KOMPSAT-3 and -3A data. We also analyzed the model performance according to the ResNet convolutional layers in chimney size estimation. In chimney detection, the model performances between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 0.723 and 0.739, 0.674 and 0.805, and 0.702 and 0.786 in the average precision (AP) 50–95 measure, respectively. The model performance between the South Korea and Thailand data showed a significant difference, likely because the chimneys in South Korea are very diverse, making it harder to generalize the YOLOv8 model. Furthermore, the model root mean square errors (RMSE) between the imbalanced and balanced data, South Korea and Thailand data, and KOMPSAT-3 and -3A data were about 2.917 and 2.788, 2.690 and 2.951, and 2.913 and 2.580 in chimney height, respectively, and about 1.285 and 1.190, 1.228 and 1.120, and 1.291 and 1.013 in chimney diameter, respectively. Keywords: Chimneys; deep learning; You Only Look Once version 8; Residual Network; Korean Multi-purpose Satellite-3/3A; object detection; regression model.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109686"},"PeriodicalIF":7.5,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breaking down barriers: A new approach to virtual museum navigation for people with visual impairments through voice assistants","authors":"Yeliz Yücel,&nbsp;Kerem Rızvanoğlu","doi":"10.1016/j.ijhcs.2024.103403","DOIUrl":"10.1016/j.ijhcs.2024.103403","url":null,"abstract":"<div><div>People with visual imparments (PWVI) encounter challenges in accessing cultural, historical, and practical information in a predominantly visual world, limiting their participation in various activities, including visits to museums.Museums, as important centers for exploration and learning, often overlook these accessibility issues.This abstract presents the iMuse Model, an innovative approach to create accessible and inclusive museum environments for them.The iMuse Model centers around the co-design of a prototype voice assistant integrated into Google Home, aimed at enabling remote navigation for PWVI within the Basilica Cistern museum in Turkey.This model consists of a two-layer study.The first layer involves collaboration with PWVI and their sight loss instructors to develop a five level framework tailored to their unique needs and challenges.The second layer focuses on testing this design with 30 people with visual impairments, employing various methodologies, including the Wizard of Oz technique.Our prototype provides inclusive audio descriptions that encompass sensory, emotional, historical, and structural elements, along with spatialized sounds from the museum environment, improving spatial understanding and cognitive map development.Notably, we have developed two versions of the voice assistant: one with a humorous interaction and one with a non-humorous approach. Users expressed a preference for the humorous version, leading to increased interaction, enjoyment, and social learning, as supported by both qualitative and quantitative results.In conclusion, the iMuse Model highlights the potential of co-designed, humor-infused, and culturally sensitive voice assistants.Our model not only aid PWVI in navigating unfamiliar spaces but also enhance their social learning, engagement, and appreciation of cultural heritage within museum environments.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"194 ","pages":"Article 103403"},"PeriodicalIF":5.3,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical-anchored graph learning for process key indicator prediction","authors":"Mingwei Jia ,&nbsp;Lingwei Jiang ,&nbsp;Bing Guo ,&nbsp;Yi Liu ,&nbsp;Tao Chen","doi":"10.1016/j.conengprac.2024.106167","DOIUrl":"10.1016/j.conengprac.2024.106167","url":null,"abstract":"<div><div>Data-driven soft sensors in the process industry, whilst intensively investigated, struggle to handle unforeseen disruptions and operating changes not covered in the training data. Incorporating physical knowledge, such as mass/energy balances and reaction mechanisms, into a data-driven model is a potential remedy. In this study, a physical-anchored graph learning (PAGL) soft sensor is proposed, integrating process variable causality and mass balances. Knowledge-derived causality is further supplemented by mining dependencies from data. PAGL uses causality and mass balance as physical anchors to predict key indicators and evaluate whether the prediction logic aligns with physical principles, ensuring physical consistency in inference. The case study on wastewater treatment demonstrates PAGL's interpretability and reliability, maintaining physical consistency instead of acting as a black box.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106167"},"PeriodicalIF":5.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}