首页 > 最新文献

Journal of Intelligent Systems最新文献

英文 中文
Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model 基于可变形卷积神经网络和时频注意模型的重放攻击检测
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0265
Dang-en Xie, Hai Hu, Qiang Xu
Abstract As an important identity authentication method, speaker verification (SV) has been widely used in many domains, e.g., mobile financials. At the same time, the existing SV systems are insecure under replay spoofing attacks. Toward a more secure and stable SV system, this article proposes a replay attack detection system based on deformable convolutional neural networks (DCNNs) and a time–frequency double-channel attention model. In DCNN, the positions of elements in the convolutional kernel are not fixed. Instead, they are modified by some trainable variable to help the model extract more useful local information from input spectrograms. Meanwhile, a time–frequency domino double-channel attention model is adopted to extract more effective distinctive features to collect valuable information for distinguishing genuine and replay speeches. Experimental results on ASVspoof 2019 dataset show that the proposed model can detect replay attacks accurately.
作为一种重要的身份认证方法,说话人验证(SV)在移动金融等领域得到了广泛应用。同时,现有的SV系统在重放欺骗攻击下是不安全的。为了使SV系统更加安全稳定,本文提出了一种基于可变形卷积神经网络(DCNNs)和时频双通道注意力模型的重放攻击检测系统。在DCNN中,卷积核中元素的位置是不固定的。相反,它们被一些可训练的变量修改,以帮助模型从输入谱图中提取更多有用的局部信息。同时,采用时频骨牌双通道注意模型提取更有效的显著特征,为区分真实演讲和重播演讲收集有价值的信息。在ASVspoof 2019数据集上的实验结果表明,该模型能够准确检测重放攻击。
{"title":"Replay attack detection based on deformable convolutional neural network and temporal-frequency attention model","authors":"Dang-en Xie, Hai Hu, Qiang Xu","doi":"10.1515/jisys-2022-0265","DOIUrl":"https://doi.org/10.1515/jisys-2022-0265","url":null,"abstract":"Abstract As an important identity authentication method, speaker verification (SV) has been widely used in many domains, e.g., mobile financials. At the same time, the existing SV systems are insecure under replay spoofing attacks. Toward a more secure and stable SV system, this article proposes a replay attack detection system based on deformable convolutional neural networks (DCNNs) and a time–frequency double-channel attention model. In DCNN, the positions of elements in the convolutional kernel are not fixed. Instead, they are modified by some trainable variable to help the model extract more useful local information from input spectrograms. Meanwhile, a time–frequency domino double-channel attention model is adopted to extract more effective distinctive features to collect valuable information for distinguishing genuine and replay speeches. Experimental results on ASVspoof 2019 dataset show that the proposed model can detect replay attacks accurately.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"23 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82168853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A novel distance vector hop localization method for wireless sensor networks 一种新的无线传感器网络距离矢量跳定位方法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2023-0031
Y. A. A. S. Aldeen, S. Kadhim, N. N. Kadhim, Syed Hamid Hussain Madni
Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.
摘要无线传感器网络(WSNs)需要精确定位传感器节点以满足各种应用需求。在本文中,我们提出了距离矢量跳定位方法(DVHLM)来解决实时网络中的节点错位问题。该方法结合了三边测量和粒子群优化技术来估计未知或错位节点的位置。该方法包括坐标计算、距离计算、未知节点位置估计和估计校正四个步骤。为了评估所提出的方法,我们进行了仿真实验,并将其在已知节点、错位节点和阴影效果的定位精度方面与最先进的方法进行了比较。结果表明,该方法优于现有的定位方法,在误差较小的情况下获得了更好的定位精度。本文提出了一种方法详细、性能优越的无线传感器网络新方法,为无线传感器网络领域做出了宝贵的贡献。
{"title":"A novel distance vector hop localization method for wireless sensor networks","authors":"Y. A. A. S. Aldeen, S. Kadhim, N. N. Kadhim, Syed Hamid Hussain Madni","doi":"10.1515/jisys-2023-0031","DOIUrl":"https://doi.org/10.1515/jisys-2023-0031","url":null,"abstract":"Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"235 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75572195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images 基于非迭代确定性学习分类器的RES-KELM融合模型用于新冠肺炎胸片图像分类
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2022-0235
Arshi Husain, Virendra P. Vishvakarma
Abstract In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.
本研究提出了一种基于非迭代确定性分类器、核极限学习机(KELM)和预训练网络ResNet50的胸部x线图像实时检测和分析新冠肺炎的新方法。该融合模型结合了深度学习的信息提取能力和KELM的非迭代确定性训练特性。使用基于非迭代确定性学习的分类器KELM进行二元分类。我们提出的方法在第一个数据集上的平均测试误差最小,达到2.76,在第二个数据集上的平均测试误差最小,达到0.79,实验验证了该方法的有效性。本研究还将该方法与其他现有的最先进的方法进行了比较分析,分类性能证实了我们的新方法RES-KELM算法的优势和优越性。
{"title":"RES-KELM fusion model based on non-iterative deterministic learning classifier for classification of Covid19 chest X-ray images","authors":"Arshi Husain, Virendra P. Vishvakarma","doi":"10.1515/jisys-2022-0235","DOIUrl":"https://doi.org/10.1515/jisys-2022-0235","url":null,"abstract":"Abstract In this research, a novel real time approach has been proposed for detection and analysis of Covid19 using chest X-ray images based on a non-iterative deterministic classifier, kernel extreme learning machine (KELM), and a pretrained network ResNet50. The information extraction capability of deep learning and non-iterative deterministic training nature of KELM has been incorporated in the proposed novel fusion model. The binary classification is carried out with a non-iterative deterministic learning based classifier, KELM. Our proposed approach is able to minimize the average testing error up to 2.76 on first dataset, and up to 0.79 on the second one, demonstrating its effectiveness after experimental confirmation. A comparative analysis of the approach with other existing state-of-the-art methods is also presented in this research and the classification performance confirm the advantages and superiority of our novel approach called RES-KELM algorithm.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"59 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83168299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure 在高级计量基础设施中使用Kerberos实现资源约束智能电表的高效相互认证
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-01-01 DOI: 10.1515/jisys-2021-0095
Md. Mehedi Hasan, Noor Afiza Mohd Ariffin, N. F. M. Sani
Abstract The continuous development of information communication technology facilitates the conventional grid in transforming into an automated modern system. Internet-of-Things solutions are used along with the evolving services of end-users to the electricity service provider for smart grid applications. In terms of various devices and machine integration, adequate authentication is the key to an accurate source and destination in advanced metering infrastructure (AMI). Various protocols are deployed to lead the identification between two parties, which require high computation time and communicational bit operations for system development. Therefore, Kerberos-based authentication protocols were designed in this study with the assistance of elliptic curve cryptography to manage the mutual authentication between two parties and reduce the time and bit operations. The protocols were evaluated in a widely adopted tool, AVISPA, which builds an understanding of the proposed protocol and ensures mutual authentication without unauthorized knowledge. In addition, upon comparing security and performance assessments to the current schemes, it was found that the protocol in this study required less time and bits to transmit information. Consequently, it effectively provides multiple security features making it suitable for resource constraint smart meters in AMI.
信息通信技术的不断发展促进了传统电网向自动化的现代化电网的转变。物联网解决方案与最终用户不断发展的服务一起用于智能电网应用的电力服务提供商。就各种设备和机器集成而言,在高级计量基础设施(AMI)中,充分的身份验证是准确的源和目的的关键。在系统开发过程中,需要大量的计算时间和通信位操作来实现双方的身份识别。因此,本文设计了基于kerberos的认证协议,借助椭圆曲线密码学来管理双方的相互认证,减少时间和比特操作。在广泛采用的工具AVISPA中对协议进行了评估,该工具建立了对拟议协议的理解,并确保在未经授权的情况下进行相互认证。此外,将安全性和性能评估与现有方案进行比较,发现本研究中的协议传输信息所需的时间和比特数更少。因此,它有效地提供了多种安全特性,使其适合AMI中的资源约束智能电表。
{"title":"Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure","authors":"Md. Mehedi Hasan, Noor Afiza Mohd Ariffin, N. F. M. Sani","doi":"10.1515/jisys-2021-0095","DOIUrl":"https://doi.org/10.1515/jisys-2021-0095","url":null,"abstract":"Abstract The continuous development of information communication technology facilitates the conventional grid in transforming into an automated modern system. Internet-of-Things solutions are used along with the evolving services of end-users to the electricity service provider for smart grid applications. In terms of various devices and machine integration, adequate authentication is the key to an accurate source and destination in advanced metering infrastructure (AMI). Various protocols are deployed to lead the identification between two parties, which require high computation time and communicational bit operations for system development. Therefore, Kerberos-based authentication protocols were designed in this study with the assistance of elliptic curve cryptography to manage the mutual authentication between two parties and reduce the time and bit operations. The protocols were evaluated in a widely adopted tool, AVISPA, which builds an understanding of the proposed protocol and ensures mutual authentication without unauthorized knowledge. In addition, upon comparing security and performance assessments to the current schemes, it was found that the protocol in this study required less time and bits to transmit information. Consequently, it effectively provides multiple security features making it suitable for resource constraint smart meters in AMI.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89426552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Face recognition algorithm based on stack denoising and self-encoding LBP 基于堆栈去噪和自编码LBP的人脸识别算法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0011
Yan-sheng Lu, Mudassir Khan, Mohd Dilshad Ansari
Abstract To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.
摘要针对传统人脸识别算法鲁棒性弱、分类准确率不高、运算速度较慢等问题,提出了一种基于局部二值模式(LBP)和堆叠自编码器(AE)的人脸识别算法。利用人脸图像的LBP纹理结构特征作为稀疏自编码器(SAE)学习的初始特征,利用统一模式LBP算子提取被阻塞人脸图像的直方图,连接形成整个图像的LBP特征。将其作为叠加声发射的输入,进行特征提取,实现人脸图像的识别与分类。实验结果表明,LBP-SAE算法在耶鲁数据库上的识别率达到99.05%,进一步表明该算法比经典人脸识别算法具有更高的识别率;它对光线变化具有很强的稳健性。在Olivetti研究实验室库上的实验结果表明,与传统人脸识别算法和标准堆栈AEs相比,所开发的方法对光线变化具有更强的鲁棒性,具有更好的识别效果。
{"title":"Face recognition algorithm based on stack denoising and self-encoding LBP","authors":"Yan-sheng Lu, Mudassir Khan, Mohd Dilshad Ansari","doi":"10.1515/jisys-2022-0011","DOIUrl":"https://doi.org/10.1515/jisys-2022-0011","url":null,"abstract":"Abstract To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"10 1","pages":"501 - 510"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73321286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Eurasian oystercatcher optimiser: New meta-heuristic algorithm 欧亚捕牡蛎优化器:新的元启发式算法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0017
A. Salim, Wisam K. Jummar, Farah Maath Jasim, Mohammed S. Yousif
Abstract Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance.
现代优化越来越依赖于元启发式方法。本研究提出了一种新的元启发式优化算法,称为欧亚捕牡蛎优化器(EOO)。EOO算法模拟了欧亚捕牡蛎者寻找贻贝的食物行为。在EOO中,种群中的每只鸟(解决方案)都充当一个搜索代理。EO根据最佳解决方案改变候选贻贝,最终吃到最好的贻贝(最佳结果)。贻贝的大小、卡路里和能量必须达到平衡。该算法对三个阶段(单峰、多峰和定缩多峰)的58个测试函数进行了基准测试,并与粒子群优化算法、灰狼优化算法、基于生物地理的优化算法、引力搜索算法和人工蜂群算法进行了比较。最后,测试函数的结果证明,该算法在改进的勘探开发平衡和局部最优避免方面能够提供非常有竞争力的结果。
{"title":"Eurasian oystercatcher optimiser: New meta-heuristic algorithm","authors":"A. Salim, Wisam K. Jummar, Farah Maath Jasim, Mohammed S. Yousif","doi":"10.1515/jisys-2022-0017","DOIUrl":"https://doi.org/10.1515/jisys-2022-0017","url":null,"abstract":"Abstract Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"107 1","pages":"332 - 344"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76831424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Interactive 3D reconstruction method of fuzzy static images in social media 社交媒体中模糊静态图像的交互式三维重建方法
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0049
Xiaomei Niu
Abstract Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.
摘要针对传统社交媒体模糊静态图像交互式三维(3D)重建方法存在重建完整性差、重建时间长等问题,提出了社交媒体模糊静态图像交互式三维重建方法。对社交媒体模糊静态图像进行预处理,采用Harris角点检测方法提取预处理后的社交媒体模糊静态图像的特征点。根据提取结果,利用对比散度参数估计算法学习受限玻尔兹曼机(RBM)网络模型,并将RBM网络模型划分为输入层、输出层和隐藏层。将基于rbm的联合字典学习方法与稀疏表示模型相结合,实现了社交媒体中模糊静态图像的交互式三维重建。基于CAD软件的实验结果表明,所提方法的重建完整性在95%以上,重建时间小于15 s,提高了重建的完整性和效率,有效地重建了社交媒体中的模糊静态图像,增加了社交媒体图像的真实感。
{"title":"Interactive 3D reconstruction method of fuzzy static images in social media","authors":"Xiaomei Niu","doi":"10.1515/jisys-2022-0049","DOIUrl":"https://doi.org/10.1515/jisys-2022-0049","url":null,"abstract":"Abstract Because the traditional social media fuzzy static image interactive three-dimensional (3D) reconstruction method has the problem of poor reconstruction completeness and long reconstruction time, the social media fuzzy static image interactive 3D reconstruction method is proposed. For preprocessing the fuzzy static image of social media, the Harris corner detection method is used to extract the feature points of the preprocessed fuzzy static image of social media. According to the extraction results, the parameter estimation algorithm of contrast divergence is used to learn the restricted Boltzmann machine (RBM) network model, and the RBM network model is divided into input, output, and hidden layers. By combining the RBM-based joint dictionary learning method and a sparse representation model, an interactive 3D reconstruction of fuzzy static images in social media is achieved. Experimental results based on the CAD software show that the proposed method has a reconstruction completeness of above 95% and the reconstruction time is less than 15 s, improving the completeness and efficiency of the reconstruction, effectively reconstructing the fuzzy static images in social media, and increasing the sense of reality of social media images.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"94 1","pages":"806 - 816"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83909130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
PUC: parallel mining of high-utility itemsets with load balancing on spark PUC:基于spark负载均衡的高效用项集并行挖掘
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0044
Anup Brahmavar, H. Venkatarama, Geetha Maiya
Abstract Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset (HUI) that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation (PUC) algorithm has been proposed with novel grouping and load balancing strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility (TWU) values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup.
MapReduce和Spark等分布式编程范式缓解了海量事务数据库挖掘时的顺序瓶颈。非常重要的是挖掘高效用项目集(HUI),它包含了交易中购买的项目的收入。尽管存在一些在分布式环境中挖掘hui的算法,但由于变换操作导致的工作负载倾斜和数据传输开销仍然是主要问题。为了在分布式环境下高效地挖掘hui,提出了并行效用计算(PUC)算法,并采用了新颖的分组和负载均衡策略。为了对项目进行分组,使用事务加权效用(TWU)值作为事务相似度。随后,通过考虑组中项目的挖掘负载,将这些组分配给集群中的节点。在真实数据集和合成数据集上进行的实验评估表明,结合负载均衡的TWU分组PUC可以更快地收敛挖掘。由于减少了数据传输和基于负载均衡的分配策略,PUC优于不同的分组策略和跨集群随机分配组。此外,PUC被证明比PHUI-Growth算法更快,并且具有很好的加速效果。
{"title":"PUC: parallel mining of high-utility itemsets with load balancing on spark","authors":"Anup Brahmavar, H. Venkatarama, Geetha Maiya","doi":"10.1515/jisys-2022-0044","DOIUrl":"https://doi.org/10.1515/jisys-2022-0044","url":null,"abstract":"Abstract Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset (HUI) that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation (PUC) algorithm has been proposed with novel grouping and load balancing strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility (TWU) values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"18 1","pages":"568 - 588"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88493013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Masking and noise reduction processing of music signals in reverberant music 混响音乐中音乐信号的掩蔽与降噪处理
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0024
Shenghuan Zhang, Ye Cheng
Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.
在录音过程中,噪声不可避免地混入音乐信号中。为了提高音乐信号的质量,必须尽可能地降低噪声。本文简要介绍了混响音乐中的噪声、掩蔽效应以及降低噪声的频谱减法。利用人耳掩蔽效应对谱减法进行改进,提高了谱减法的降噪性能。对传统的和改进的谱减法进行了仿真实验。结果表明,改进的谱减法能更有效地降低混响音乐中的噪声;在信噪比这一客观评价标准下,改进谱减法处理后的消混响音乐信号具有较高的信噪比;在主观评价标准平均意见评分(MOS)下,改进谱减法处理后的消混响音乐信号也有较好的评价。
{"title":"Masking and noise reduction processing of music signals in reverberant music","authors":"Shenghuan Zhang, Ye Cheng","doi":"10.1515/jisys-2022-0024","DOIUrl":"https://doi.org/10.1515/jisys-2022-0024","url":null,"abstract":"Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"22 1","pages":"420 - 427"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89062788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Iot-based power detection equipment management and control system 基于物联网的电力检测设备管理与控制系统
IF 3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-01 DOI: 10.1515/jisys-2022-0127
Jintao Chen, Jianfeng Jiang, Binruo Zhu
Abstract The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.
物联网的发展和应用范围也越来越广泛。特别是在功率测试改进系统的应用方面,取得了很大的进展。本文旨在研究如何分析基于物联网的系统检测设备。本文介绍了物联网和功率检测改进系统的基本理论知识。提出了基于物联网的聚类分析算法和支持向量机算法。在本文的实验中,专家传统检测系统的评分项目存在技术复杂、使用不便、智能不全等问题。其中,复杂技术最高得分8.6分,最低得分7分;“不便”的最高分数为8.6分,最低分数为8.3分。由此可见,相关专家认为,传统的功率检测改进系统不仅技术非常复杂,使用非常不方便,而且不完全智能化。因此,研究基于物联网的系统检测设备是非常有必要的。
{"title":"Iot-based power detection equipment management and control system","authors":"Jintao Chen, Jianfeng Jiang, Binruo Zhu","doi":"10.1515/jisys-2022-0127","DOIUrl":"https://doi.org/10.1515/jisys-2022-0127","url":null,"abstract":"Abstract The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.","PeriodicalId":46139,"journal":{"name":"Journal of Intelligent Systems","volume":"281 1","pages":"1229 - 1245"},"PeriodicalIF":3.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74979709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Journal of Intelligent Systems
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1