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LMGA: Lightweight multi-graph augmentation networks for safe medication recommendation LMGA:用于安全用药推荐的轻量级多图增强网络
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-17 DOI: 10.1016/j.jksuci.2024.102245
Xingxu Fan , Xiaomei Yu , Xue Li , Fengru Ge , Yanjie Zhao
The rapid accumulation of large-scale electronic health records (EHRs) has witnessed the prosperity of intelligent medicine, such as medication recommendation (MR). However, most studies either fail to fully capture the structural correlation and temporal dependence among various medical records, or disregard the computational efficiency of the MR models. To fill this gap, we put forward a Lightweight Medication recommendation method which integrates bidirectional gate recurrent units (BiGRUs) with light graph convolutional networks (LGCNs) based on the multiple Graph Augmentation networks (LMGA). Specifically, BiGRUs are deployed to encode longitudinal visit histories and generate patient representations from a holistic perspective. Additionally, a memory network is constructed to extract local personalized features in the patients’ historical EHRs, and LGCNs are deployed to learn both drug co-occurrence and antagonistic relationships for integral drug representations with reduced computational resource requirements. Moreover, a drug molecular graph is leveraged to capture structural information and control potential DDIs in predicted medication combinations. Incorporating the representations of patients and medications, a lightweight and safe medication recommendation is available to promote prediction performance with reduced computational resource consumption. Finally, we conduct a series of experiments to evaluate the proposed LMGA on two publicly available datasets, and the experimental results demonstrate the superior performance of LMGA in MR tasks compared with the state-of-the-art baseline models.
大规模电子健康记录(EHR)的快速积累见证了智能医疗的繁荣,例如药物推荐(MR)。然而,大多数研究要么未能充分捕捉各种医疗记录之间的结构相关性和时间依赖性,要么忽视了 MR 模型的计算效率。为了填补这一空白,我们提出了一种轻量级用药推荐方法,该方法将双向门递归单元(BiGRUs)与基于多重图增强网络(LMGA)的轻图卷积网络(LGCNs)整合在一起。具体来说,BiGRU 用于编码纵向就诊历史,并从整体角度生成患者表征。此外,还构建了一个记忆网络来提取患者历史 EHR 中的局部个性化特征,并部署 LGCNs 来学习药物共现和拮抗关系,从而在减少计算资源需求的情况下获得完整的药物表征。此外,还利用药物分子图来捕捉结构信息,并控制预测药物组合中潜在的 DDI。结合患者和药物的表征,可以提供轻量级的安全药物推荐,从而在降低计算资源消耗的同时提高预测性能。最后,我们在两个公开数据集上进行了一系列实验来评估所提出的 LMGA,实验结果表明,与最先进的基线模型相比,LMGA 在 MR 任务中的性能更优越。
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引用次数: 0
ACTF: An efficient lossless compression algorithm for time series floating point data ACTF:针对时间序列浮点数据的高效无损压缩算法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-16 DOI: 10.1016/j.jksuci.2024.102246
Weijie Wang , Wenhui Chen , Qinhon Lei , Zhe Li , Huihuang Zhao
The volume of time series data across various fields is steadily increasing. However, this unprocessed massive data challenges transmission efficiency, computational arithmetic, and storage capacity. Therefore, the compression of time series data is essential for improving transmission, computation, and storage. Currently, improving time series floating-point coding rules is the primary method for enhancing compression algorithms efficiency and ratio. This paper presents an efficient lossless compression algorithm for time series floating point data, designed based on existing compression algorithms. We employ three optimization strategies data preprocessing, coding category expansion, and feature refinement representation to enhance the compression ratio and efficiency of compressing time-series floating-point numbers. Through experimental comparisons and validations, we demonstrate that our algorithm outperforms Chimp, Chimp128, Gorilla, and other compression algorithms across multiple datasets. The experimental results on 30 datasets show that our algorithm improves the compression ratio of time series algorithms by an average of 12.25% and compression and decompression efficiencies by an average of 27.21%. Notably, it achieves a 24.06% compression ratio improvement on the IOT1 dataset and a 42.96% compression and decompression efficiency improvement on the IOT4 dataset.
各领域的时间序列数据量正在稳步增长。然而,这些未经处理的海量数据对传输效率、计算运算和存储容量提出了挑战。因此,时间序列数据的压缩对于提高传输、计算和存储能力至关重要。目前,改进时间序列浮点编码规则是提高压缩算法效率和压缩比的主要方法。本文在现有压缩算法的基础上,提出了一种高效的时间序列浮点数据无损压缩算法。我们采用了数据预处理、编码类别扩展和特征细化表示三种优化策略,以提高时间序列浮点数的压缩比和压缩效率。通过实验对比和验证,我们证明了我们的算法在多个数据集上优于 Chimp、Chimp128、Gorilla 和其他压缩算法。在 30 个数据集上的实验结果表明,我们的算法将时间序列算法的压缩率平均提高了 12.25%,压缩和解压缩效率平均提高了 27.21%。值得注意的是,它在 IOT1 数据集上提高了 24.06% 的压缩率,在 IOT4 数据集上提高了 42.96% 的压缩和解压缩效率。
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引用次数: 0
The multi-objective task assignment scheme for software crowdsourcing platforms involving new workers 新员工参与的软件众包平台的多目标任务分配方案
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-16 DOI: 10.1016/j.jksuci.2024.102237
Minglan Fu, Zhijie Zhang, ZouXi Wang, Debao Chen
Software crowdsourcing has become a cornerstone of the Internet economy because of its unique capacity for selecting optimal workers to complete specific tasks. However, new workers face limited task opportunities compared to experienced workers, which negatively impacts their motivation and decreases overall activity on crowdsourcing platforms. This reduced activity can harm platform reputation. To encourage the active participation of new workers, this study introduces a novel method to identify and match worker–task preferences. Our approach categorizes preferred tasks based on golden tasks, historical data, and worker interests. We then present the Multi-Objective Worker–Task Recommendation (MOWTR) algorithm, built upon the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The MOWTR algorithm allocates tasks by considering worker preferences, salaries, and capabilities, aiming to optimize collective team performance while minimizing team costs, especially for new workers. New crossover and two-stage mutation operators are incorporated to increase algorithm efficiency. Experimental evaluations on four real and synthetic datasets demonstrate that MOWTR outperforms four advanced baseline methods, confirming its effectiveness.
软件众包因其选择最佳工人完成特定任务的独特能力而成为互联网经济的基石。然而,与经验丰富的员工相比,新员工面临的任务机会有限,这对他们的积极性产生了负面影响,并降低了众包平台的整体活跃度。活跃度降低会损害平台声誉。为了鼓励新员工积极参与,本研究引入了一种新方法来识别和匹配员工的任务偏好。我们的方法根据黄金任务、历史数据和工人兴趣对首选任务进行分类。然后,我们在非支配排序遗传算法 II(NSGA-II)的基础上提出了多目标工人任务推荐(MOWTR)算法。MOWTR 算法通过考虑工人的偏好、工资和能力来分配任务,旨在优化团队集体绩效,同时最大限度地降低团队成本,尤其是新工人的成本。新的交叉和两阶段突变算子的加入提高了算法的效率。在四个真实和合成数据集上进行的实验评估表明,MOWTR 优于四种先进的基线方法,证实了它的有效性。
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引用次数: 0
Correlation analysis of multifractal stock price fluctuations based on partition function 基于分区函数的多分形股价波动相关性分析
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-16 DOI: 10.1016/j.jksuci.2024.102233
Huan Wang, Wei Song
Studying the correlation analysis of stock price fluctuations helps to understand market dynamics better and improve the scientific nature of investment decisions and risk management capabilities. Most existing methods use multifractals to explore the correlation between different economic entities. However, the study of multifractals fails to fully consider the weight of each entity’s impact on the market, resulting in an inaccurate description of the overall market dynamics. To address this problem, this paper creatively proposes a weighted multifractal analysis method (WMA). The correlation analysis of government regulation, market supply and demand, and stock price index is performed using the data of A-share listed companies in Shenzhen and Shanghai as samples. First, we consider the amplitude fluctuation information the signal carries and weigh the partition function according to the proportion of variance in the segment for different amplitude changes. Secondly, we derive the theoretical analytical form of the classical multifractal model (SMA) of the scaling indicator under WMA. Finally, through numerical simulation experiments, it is confirmed that WMA is equally effective as SMA. In addition, the re-fractal correlation analysis of real financial time series also confirms that WMA can effectively utilize the amplitude fluctuation information in the series and outperforms the classical SMA method in distinguishing different signals.
研究股价波动的相关性分析有助于更好地了解市场动态,提高投资决策的科学性和风险管理能力。现有方法大多采用多分形来探讨不同经济实体之间的相关性。然而,对多分形的研究未能充分考虑各实体对市场影响的权重,导致对整体市场动态的描述不准确。针对这一问题,本文创造性地提出了加权多分形分析方法(WMA)。以深市和沪市 A 股上市公司数据为样本,对政府调控、市场供求和股价指数进行相关性分析。首先,我们考虑了信号所携带的振幅波动信息,并根据不同振幅变化在分段中的方差比例来权衡分区函数。其次,我们推导出 WMA 下缩放指标的经典多分形模型(SMA)的理论解析形式。最后,通过数值模拟实验,证实 WMA 与 SMA 同样有效。此外,对真实金融时间序列的重分形相关性分析也证实,WMA 可以有效利用序列中的振幅波动信息,在区分不同信号方面优于经典的 SMA 方法。
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引用次数: 0
Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks 优化资源分配,增强低地轨道无人机-RIS 网络的城市连通性
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-15 DOI: 10.1016/j.jksuci.2024.102238
Abdulbasit A. Darem , Tareq M. Alkhaldi , Asma A. Alhashmi , Wahida Mansouri , Abed Saif Ahmed Alghawli , Tawfik Al-Hadhrami
Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.
第六代(6G)通信技术的发展目标是实现大规模连接、超可靠低延迟通信(URLLC)和高数据传输速率,这对于物联网应用至关重要。然而,在自然灾害中,特别是在密集的城市地区,当地面网络(如基站)不可用、不稳定或因用户密度高和环境多变而紧张时,6G 的服务质量(QoS)就会出现问题。此外,智能城市中的高层建筑也会造成信号阻塞。为确保可靠、高质量的连接,将低地轨道(LEO)卫星、无人机(UAV)和可重构智能表面(RIS)集成到多层(ML)网络中提供了一种解决方案:低地轨道卫星可提供广泛的覆盖范围,无人飞行器可通过灵活定位减少拥堵,而可重构智能表面(RIS)可提高信号质量。尽管有这些优势,但这种整合也给资源分配带来了挑战,需要同时考虑视距(LOS)和非视距(NLOS)链路的路径损耗模型。为解决这些问题,提出了一个联合优化问题,重点是资源分配的公平性。考虑到问题的复杂性,提出了一个框架,利用分块坐标下降(BCD)方法将问题分解为多个子问题。这些子问题包括无人机位置优化、用户关联、通过正交频分多址(OFDMA)分配子载波、功率分配和 RIS 相移控制。OFDMA 可有效管理共享资源并减少干扰。这种迭代方法对每个子问题进行优化,确保收敛到局部最优解。此外,我们还为 RIS 相移控制提出了一种低复杂度解决方案,从数学上证明了其可行性和效率。数值结果表明,与没有 RIS 的方案相比,所提出的方案最多可提高 43.5% 的总和率,降低 80% 的中断概率。RIS 优化的低复杂度解决方案在总和率方面的性能仅为 SDP 方法的 1.8%。该模型大大提高了网络性能和可靠性,与 SAT-UAV 资源联合优化相比,总和率提高了 16.3%,中断概率降低了 44.4%。这些发现凸显了 ML 网络模型的稳健性和高效性,使其成为高密度城市环境中下一代通信系统的理想选择。
{"title":"Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks","authors":"Abdulbasit A. Darem ,&nbsp;Tareq M. Alkhaldi ,&nbsp;Asma A. Alhashmi ,&nbsp;Wahida Mansouri ,&nbsp;Abed Saif Ahmed Alghawli ,&nbsp;Tawfik Al-Hadhrami","doi":"10.1016/j.jksuci.2024.102238","DOIUrl":"10.1016/j.jksuci.2024.102238","url":null,"abstract":"<div><div>Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102238"},"PeriodicalIF":5.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
引用次数: 0
Visually meaningful image encryption for secure and authenticated data transmission using chaotic maps 利用混沌图对有视觉意义的图像进行加密,以实现安全的认证数据传输
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-14 DOI: 10.1016/j.jksuci.2024.102235
Deep Singh , Sandeep Kumar , Chaman Verma , Zoltán Illés , Neerendra Kumar
Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (ARno times), to produce the secret encrypted image (Sr5). To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions Mh10 and Mh20 for modified Hénon map, and initial position Zip=(zrow,zcol) for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.
图像加密技术通常将给定的普通图像数据转换成类似噪声的加密图像数据,作为存在秘密图像数据的指示器。然而,传输这种类似噪声的图像会引起注意,从而吸引攻击者,并可能面临多种攻击。本文提出了一种生成视觉意义图像加密(VMIE)方案的方法,该方案结合了三层安全保护:加密、数字签名和隐写术。本方案致力于实现稳健性、安全性和运行效率的平衡。首先,使用 RSA 密码系统和修正的赫农图谱(MHM)对原始图像进行部分加密。第二阶段,使用哈希函数和 RSA 密码系统为部分加密的图像生成数字签名。在对上述部分加密图像进行之字形混淆后,将获得的数字签名附加到部分加密图像上。此外,为了达到更好的混淆和扩散效果,包含数字签名的部分加密图像还要经过三维阿诺德猫图的应用(ARno 次),以生成秘密加密图像(Sr5)。为确保所提技术的安全性和鲁棒性,以抵御各种经典攻击,利用 SHA-256 哈希函数和载波图像获得的哈希值生成修正 Hénon 映射的初始条件 Mh10 和 Mh20,以及之字形混淆的初始位置 Zip=(zrow,zcol)。在所提出的算法中,数字签名既可用于验证发送者的真实性,也可用于提高加密质量。载波图像经过提升小波变换,其高频分量通过 MHM 的包络模式被用于嵌入过程,从而得到视觉上有意义的加密图像。所提出的方案以最小的失真实现了高效的视觉加密,并确保了解密时的无损图像质量(PSNR 无穷大),同时兼顾了高水平的安全性和良好的计算效率。
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引用次数: 0
Leukocyte segmentation based on DenseREU-Net 基于 DenseREU-Net 的白细胞分割技术
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-12 DOI: 10.1016/j.jksuci.2024.102236
Jie Meng , Yingqi Lu , Wangjiao He , Xiangsuo Fan , Gechen Zhou , Hongjian Wei
The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases.
白细胞的检测为有关感染、炎症、免疫功能和血液病理的细胞研究提供了重要信息。有效分割血液显微图像中的白细胞不仅有助于病理学家做出更准确的诊断和早期检测,而且对确定病变类型也至关重要。由于各种类型的病理白细胞之间存在显著差异,而且细胞成像和染色技术非常复杂,因此准确识别和分割这些不同类型的白细胞仍然具有挑战性。为了应对这些挑战,本文提出了一种基于 DenseREU-Net 的白细胞分割技术,该技术通过使用密集块和残留单元来增强特征交换和重用。此外,它还引入了混合池和跳过多尺度融合模块,以提高不同类型病理白细胞的识别和分割精度。这项研究在 DML-LZWH(柳州市工人医院医学实验室)提供的两个数据集上进行了验证。实验结果表明,在多类数据集上,DenseREU-Net 的平均 IoU 为 73.05%,Dice 系数为 80.25%。在二元分类盲样本数据集上,平均 IoU 和 Dice 系数分别为 83.98% 和 90.41%。在这两个数据集中,所提出的模型明显优于其他先进的医学图像分割算法。总之,DenseREU-Net 能有效分析血液显微图像,准确识别和分割不同类型的白细胞,为血液相关疾病的诊断提供有力支持。
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引用次数: 0
Anomaly detection in sensor data via encoding time series into images 通过将时间序列编码成图像来检测传感器数据中的异常情况
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-09 DOI: 10.1016/j.jksuci.2024.102232
Jidong Ma (继东) , Hairu Wang (王海茹)
Detecting anomalies in multivariate time series data is crucial for maintaining the optimal functionality of control system equipment. While existing research has made significant strides in this area, the increasing complexity of industrial environments poses challenges in accurately capturing the interactions between variables. Therefore, this paper introduces an innovative anomaly detection approach that extends one-dimensional time series into two-dimensions to capture the spatial correlations within the data. Unlike traditional approaches, we utilize the Gramian Angular Field to encode the correlations between different sensors at specific time points into images, enabling precise learning of spatial information across multiple variables. Subsequently, we construct an adversarial generative model to accurately identify anomalies at the pixel level, facilitating precise localization of abnormal points. We evaluate our method using five open-source datasets from various fields. Our method outperforms state-of-the-art anomaly detection techniques across all datasets, showcasing its superior performance. Particularly, our method achieves a 11.5% increase in F1 score on the high-dimensional WADI dataset compared to the baseline method. Additionally, we conduct thorough effectiveness analysis, parameter impact experiments, significant statistical analysis, and burden analysis, confirming the efficacy of our approach in capturing both the temporal dynamics and spatial relationships inherent in multivariate time series data.
检测多变量时间序列数据中的异常情况对于保持控制系统设备的最佳功能至关重要。虽然现有研究在这一领域取得了长足进步,但工业环境的日益复杂性给准确捕捉变量之间的相互作用带来了挑战。因此,本文引入了一种创新的异常检测方法,将一维时间序列扩展到二维,以捕捉数据中的空间相关性。与传统方法不同,我们利用格拉米安角场(Gramian Angular Field)将特定时间点上不同传感器之间的相关性编码成图像,从而实现跨多个变量的空间信息的精确学习。随后,我们构建了一个对抗生成模型,以准确识别像素级别的异常,从而促进异常点的精确定位。我们使用来自不同领域的五个开源数据集对我们的方法进行了评估。在所有数据集上,我们的方法都优于最先进的异常检测技术,展示了其卓越的性能。特别是,与基线方法相比,我们的方法在高维 WADI 数据集上的 F1 分数提高了 11.5%。此外,我们还进行了全面的有效性分析、参数影响实验、重要统计分析和负担分析,证实了我们的方法在捕捉多元时间序列数据中固有的时间动态和空间关系方面的功效。
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引用次数: 0
Knowledge-embedded multi-layer collaborative adaptive fusion network: Addressing challenges in foggy conditions and complex imaging 知识嵌入式多层协作自适应融合网络:应对多雾条件和复杂成像的挑战
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-07 DOI: 10.1016/j.jksuci.2024.102230
Zhu Chen, Fan Li, Yueqin Diao, Wanlong Zhao, Puyin Fan
Infrared and visible image fusion aims at generating high-quality images that serve both human and machine visual perception under extreme imaging conditions. However, current fusion methods primarily rely on datasets comprising infrared and visible images captured under clear weather conditions. When applied to real-world scenarios, image fusion tasks inevitably encounter challenges posed by adverse weather conditions such as heavy fog, resulting in difficulties in obtaining effective information and inferior visual perception. To address these challenges, this paper proposes a Mean Teacher-based Self-supervised Image Restoration and multimodal Image Fusion joint learning network (SIRIFN), which enhances the robustness of the fusion network in adverse weather conditions by employing deep supervision from a guiding network to the learning network. Furthermore, to enhance the network’s information extraction and integration capabilities, our Multi-level Feature Collaborative adaptive Reconstruction Network (MFCRNet) is introduced, which adopts a multi-branch, multi-scale design, with differentiated processing strategies for different features. This approach preserves rich texture information while maintaining semantic consistency from the source images. Extensive experiments demonstrate that SIRIFN outperforms current state-of-the-art algorithms in both visual quality and quantitative evaluation. Specifically, the joint implementation of image restoration and multimodal fusion provides more effective information for visual tasks under extreme weather conditions, thereby facilitating downstream visual tasks.
红外和可见光图像融合旨在生成高质量的图像,以满足人类和机器在极端成像条件下的视觉感知。然而,目前的融合方法主要依赖于在晴朗天气条件下拍摄的红外和可见光图像数据集。当应用到实际场景时,图像融合任务不可避免地会遇到大雾等恶劣天气条件带来的挑战,导致难以获得有效信息和视觉感知能力下降。为了应对这些挑战,本文提出了一种基于平均值教师的自监督图像复原和多模态图像融合联合学习网络(SIRIFN),该网络通过从指导网络到学习网络的深度监督,增强了融合网络在恶劣天气条件下的鲁棒性。此外,为了增强网络的信息提取和整合能力,我们引入了多层次特征协作自适应重构网络(MFCRNet),该网络采用多分支、多尺度设计,针对不同特征采用不同的处理策略。这种方法既能保留丰富的纹理信息,又能保持源图像的语义一致性。大量实验证明,SIRIFN 在视觉质量和定量评估方面都优于目前最先进的算法。具体来说,图像复原和多模态融合的联合实施为极端天气条件下的视觉任务提供了更有效的信息,从而为下游视觉任务提供了便利。
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引用次数: 0
Feature-fused residual network for time series classification 用于时间序列分类的特征融合残差网络
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-11-06 DOI: 10.1016/j.jksuci.2024.102227
Yanxuan Wei , Mingsen Du , Teng Li , Xiangwei Zheng , Cun Ji
In various fields such as healthcare and transportation, accurately classifying time series data can provide important support for decision-making. To further improve the accuracy of time series classification, we propose a Feature-fused Residual Network based on Multi-scale Signed Recurrence Plot (MSRP-FFRN). This method transforms one-dimensional time series into two-dimensional images, representing the temporal correlation of time series in a two-dimensional space and revealing hidden details within the data. To enhance these details further, we extract multi-scale features by setting receptive fields of different sizes and using adaptive network depths, which improves image quality. To evaluate the performance of this method, we conducted experiments on 43 UCR datasets and compared it with nine state-of-the-art baseline methods. The experimental results show that MSRP-FFRN ranks first on critical difference diagram, achieving the highest accuracy on 18 datasets with an average accuracy of 89.9%, making it the best-performing method overall. Additionally, the effectiveness of this method is further validated through metrics such as Precision, Recall, and F1 score. Results from ablation experiments also highlight the efficacy of the improvements made by MSRP-FFRN.
在医疗保健和交通等多个领域,对时间序列数据进行准确分类可为决策提供重要支持。为了进一步提高时间序列分类的准确性,我们提出了基于多尺度符号递归图的特征融合残差网络(MSRP-FFRN)。该方法将一维时间序列转换为二维图像,在二维空间中表示时间序列的时间相关性,并揭示数据中隐藏的细节。为了进一步增强这些细节,我们通过设置不同大小的感受野和使用自适应网络深度来提取多尺度特征,从而提高图像质量。为了评估该方法的性能,我们在 43 个 UCR 数据集上进行了实验,并将其与九种最先进的基线方法进行了比较。实验结果表明,MSRP-FFRN 在临界差分图上排名第一,在 18 个数据集上达到了最高的准确率,平均准确率为 89.9%,是整体表现最好的方法。此外,精确度、召回率和 F1 分数等指标也进一步验证了该方法的有效性。消融实验的结果也凸显了 MSRP-FFRN 所做改进的功效。
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Journal of King Saud University-Computer and Information Sciences
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