检索结果-内蒙古大学图书馆

International Joint Conference on Neural Networks (IJCNN)

作者： Xingjie Huang Jianming Lv School of Computer Science and Engineering South China University of Technology Guangzhou China Intelligent Information Fusion Research Group South China University of Technology Guangzhou China

The remote heart rate measurement aims to use the remote photoplethysmography (rPPG) technology to extract the heart rate information from face videos in a non-contact manner. Most of existing methods treat each part of the face image equally and usually have significant performance drop in some real-world application scenarios, where the face area is partially masked or occluded. To address this challenge, we propose a part-based resilient remote heart rate measurement algorithm, namely PBHeart, which is based on the fine-grained estimation of the effectiveness of different parts in a face. In particular, we apply the 3D convolutional neural network to process face frames and evenly divide the feature map into adjacent parts, each of which is used to predict the rPPG signal and the heart rate jointly. Meanwhile, we measure the effectiveness of each part of face based on the precision of prediction and the cluster based reliability of model. Finally, the prediction results from each part of face is weighted by the effectiveness, and integrated to achieve the final result. Comprehensive experiments based on three real-world datasets are conducted to prove the state-of-the-art performance of PBHeart, especially for partially masked faces. More importantly, we have collected and will release a new masked face dataset namely FMD, which includes more than 1 hour videos from 22 volunteers, to support further research in this field.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Simulating Cyber Attacks and Designing Malware using Python 10

Simulating Cyber Attacks and Designing Malware using Python

引用

10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification 15th

Numerical Feature Preprocessing Method for Daily Solar Radia...

引用

15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous information Technologies and Applications, CUTE 2023

作者： Li, You Wang, Yafei Zheng, Ying Qian, Hui Zhou, Weisheng Dual-Carbon Research Center Hangzhou City University Hangzhou310015 China Asia-Japan Research Institute Ritsumeikan University Ibaraki567-8570 Japan School of Civil Engineering and Architecture Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science and Information Technologies Frostburg State University FrostburgMD21532 United States College of Policy Science Ritsumeikan University Ibaraki567-8570 Japan

ISBN: (纸本)9789819724468

One challenging issue in daily solar radiation classification involves addressing the geometric differences in daily solar radiation patterns caused by geographic location and seasonal influences. A standardization method based on matrix transformation has been proposed to eliminate the daily geometric variations of solar radiation using classification for daily patterns of solar radiation. This method was tested using measurements taken at the Tokyo monitoring station from 2000 to 2021. Through the implementation of the proposed classification algorithm, this paper discerned twelve categories of daily clearness index distributions. The suggested classification framework has the potential to assist in the development of solar radiation transformation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar irradiance

来源：评论

学校读者我要写书评

暂无评论

Weather Prediction Based on the Cloud Images Using Machine and Deep Learning Algorithms

Weather Prediction Based on the Cloud Images Using Machine a...

引用

Next Generation Communication & information Processing (INCIP), International Conference on

作者： Rohil P. Utture Alan K. Biju Y.V. Srinivasa Murthy Linga Reddy Cenkeramaddi Nancy Kumari Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka India Department of Information and Communication Technology The ACPS Research Group The University of Agder Grimstad Norway School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The process of weather forecasting based on the climatic images captured at various geographical locations and times can guide humans to plan their daily routines. In this paper, we propose forecasting the weather based on the images captured from the sky. The image dataset collected from various online search engines and mobile cameras pertains to timings such as sunrise, sunset, daytime, and night. Care has been taken to consider various weather conditions such as clear, foggy, rainy, and glare. Machine learning (ML) is widely adapted for various real-time applications. The inception of deep learning (DL) architectures has already been proven to outperform the traditional feature engineering approaches. This paper proposes two approaches. One is using the traditional ML models and the other is using trending convolutional neural networks (CNNs). In ML-based approach, decision tree (DT) and random forest (RF) are considered. Manually extracted features such as brightness, sharpness, contrast, white pixel intensity, haze factor, and color histogram are fed to them. Meanwhile, in the CNN-based approach, VGG16 and RESNET architectures are used. The performance of CNNs is found to be better when compared to the traditional ML algorithms. The basic CNN, VGG16, and RESNET models provide an accuracy of 62%, 72%, and 73.35%, respectively.

关键词： Support vector machines Radio frequency Accuracy Weather forecasting Predictive models Prediction algorithms Classification algorithms Convolutional neural networks Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

Innovative anti-phishing framework using machine learning approach with evolutionary optimization to secure multimedia applications

引用

Multimedia Tools and Applications 2025年 1-28页

作者： Sharma, Bhawna Khurana, Savita Sharma, Gaurav Rani, Sunita Nandal, Sudesh Kumari Chandi, Pragya Rastogi, Rashi Castilla, Ángel Kuc Goyal, Nitin Department of Computer Science & Engineering JMIT Radaur India Department of Information Technology JMIT Radaur India Department of Computer Science & Engineering and Information Technology BPS Mahila Vishwavidalaya Sonipat India Department of Electronics and Communication Engineering BPS Mahila Vishwavidalaya Sonipat India Department of Computer Science & Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Computer Application Sir Chhotu Ram Institute of Engineering and Technology CCS University Meerut India Engineering Research & Innovation Group Universidad Europea del Atlántico Santander Spain Department of Project Management Universidad Internacional Iberoamericana Campeche México Department of Project Management Universidad Internacional Iberoamericana Arecibo USA Department of Computer Science & Engineering School of Engineering and Technology Central University of Haryana Mahendergarh India

The transmission of users’ personal data via networks poses an increased risk of being exposed to phishing threats. Phishing URLs hiding malicious web addresses have become a widespread means of compromising user privacy. This paper is to improve the accuracy and efficiency of detecting and defending against phishing attacks in multimedia applications, aiming to safeguard users'personal data from exposure via phishing URLs. The proposed approach can be summarized in three phases. In the first phase, cosine and Jaccard similarities between training and test datasets are calculated to obtain a comprehensive understanding of the similarities of data points. A hybrid similarity score is built by combining these two measures in the second phase of the experiment to effectively blend content-related and quantity-related similarities. The third and final phase involves training a model on XGBoost that uses a hybrid similarity score as an input and then tunning up its hyperparameters using a genetic algorithm with the optimization of classification performance. Finally, the created powerful classifiers are merged to form a stacked ensemble model, increasing the overall accuracy of the classification. The strenuous test evaluation of 11,429 rows, 88 columns, through key metrics, such as Precision, Recall, F-Score, and Accuracy show that the proposed anti-phishing model has outperformance parameters in other models. With this lean approach, accuracy in classification can be improved by the incorporation of similarity-based techniques and machine learning and evolutionary optimization, in which defense can be performed against phishing threats in multimedia environments. The new innovation involved in the proposed work is based on stacking an ensemble of similarity-based techniques with a learning and optimization framework of machines, ensuring robustness toward phishing threat defense.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

引用

computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

来源：评论

学校读者我要写书评

暂无评论

SEAS: FEW-SHOT INDUSTRIAL ANOMALY IMAGE GENERATION WITH SEPARATION AND SHARING FINE-TUNING

arXiv

引用

arXiv 2024年

作者： Dai, Zhewei Zeng, Shilei Liu, Haotian Li, Xurui Xue, Feng Zhou, Yu School of Electronic Information and Communications Huazhong University of Science and Technology China Artificial Intelligence Research Institute Wuhan JingCe Electronic Group Co.LTD China Department of Information Engineering and Computer Science University of Trento Italy

Current segmentation methods typically require many training images and precise masks, while insufficient anomaly images hinder their application in industrial scenarios. To address such an issue, we explore producing diverse anomalies and accurate pixel-wise annotations. By observing the real production lines, we find that anomalies vary randomly in shape and appearance, whereas products hold globally consistent patterns with slight local variations. Such a characteristic inspires us to develop a Separation and Sharing Fine-tuning (SeaS) approach using only a few abnormal and some normal images. Firstly, we propose the Unbalanced Abnormal (UA) Text Prompt tailored to industrial anomaly generation, consisting of one product token and several anomaly tokens. Then, for anomaly images, we propose a Decoupled Anomaly Alignment (DA) loss to bind the attributes of the anomalies to different anomaly tokens. Re-blending such attributes may produce never-seen anomalies, achieving a high diversity of anomalies. For normal images, we propose a Normal-image Alignment (NA) loss to learn the products’ key features that are used to synthesize products with both global consistency and local variations. The two training processes are separated but conducted on a shared U-Net. Finally, SeaS produces high-fidelity annotations for the generated anomalies by fusing discriminative features of U-Net and high-resolution VAE features. The extensive evaluations on the challenging MVTec AD and MVTec 3D AD dataset (RGB images) demonstrate the effectiveness of our approach. For anomaly image generation, on MVTec AD dataset, we achieve 1.88 on IS and 0.34 on IC-LPIPS, while on the MVTec 3D AD dataset, we obtain 1.95 on IS and 0.30 on IC-LPIPS. For the downstream task, by using our generated anomaly image-mask pairs, three common segmentation methods achieve an average 11.17% improvement on IoU on MVTec AD dataset, and a 15.49% enhancement in IoU on the MVTec 3D AD dataset. The source code is ava

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

MUSC: ZERO-SHOT INDUSTRIAL ANOMALY CLASSIFICATION AND SEGMENTATION WITH MUTUAL SCORING OF THE UNLABELED IMAGES

arXiv

引用

arXiv 2024年

作者： Li, Xurui Huang, Ziming Xue, Feng Zhou, Yu School of Electronic Information and Communications Huazhong University of Science and Technology China Artificial Intelligence Research Institute Wuhan JingCe Electronic Group Co.Ltd China Department of Information Engineering and Computer Science University of Trento Italy

This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial product images, the normal image patches could find a relatively large number of similar patches in other unlabeled images, while the abnormal ones only have a few similar patches. We leverage such a discriminative characteristic to design a novel zero-shot AC/AS method by Mutual Scoring (MuSc) of the unlabeled images, which does not need any training or prompts. Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees (LNAMD) to obtain the patch features that are capable of representing anomalies in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to leverage the unlabeled test images to assign the anomaly score to each other. Furthermore, we present an optimization approach named Re-scoring with Constrained Image-level Neighborhood (RsCIN) for image-level anomaly classification to suppress the false positives caused by noises in normal images. The superior performance on the challenging MVTec AD and VisA datasets demonstrates the effectiveness of our approach. Compared with the state-of-the-art zero-shot approaches, MuSc achieves a 21.1% PRO absolute gain (from 72.7% to 93.8%) on MVTec AD, a 19.4% pixel-AP gain and a 14.7% pixel-AUROC gain on VisA. In addition, our zero-shot approach outperforms most of the few-shot approaches and is comparable to some one-class methods. Code is available at https://***/xrli-U/MuSc. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Future research Trend for Improving Large Reflector Antenna Service Performance

引用

engineering 2021年第8期7卷 1047-1050页

作者： Peiyuan Lian Congsi Wang Song Xue Yan Wang Yuefei Yan Qian Xu Baoyan Duan Na Wang Yuhu Duan Yang Wu School of Electromechanical Engineering Xidian UniversityXi’an 710071China School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’an 710055China Xinjiang Astronomical Observatory Chinese Academy of ScienceUrumqi 830011China China Electronics Technology Group Corporation No.39 Research Institute Xi’an 710065China China Electronics Technology Group Corporation No.54 Research Institute Shijiazhuang 050081China

A large reflector antenna is one of the key components of a deep space telemetry track and command(TT&C)network,which is of great importance for human space activities,such as satellite communication,spaceflight,and deep space exploration[1].The main feature of this antenna type is its large aperture and often it requires strict surface accuracy to ensure high gain and high pointing performance[2,3].There are two kinds of reflector antennas:non-fully movable antenna and fully steerable *** Fivehundred-meter Aperture Spherical Radio Telescope in China is one representative non-fully movable antenna with a super large aperture and low operation *** fully steerable reflector antennas worldwide can be further divided into four categories according to their aperture diameters and highest operation frequencies,as shown in *** of the antennas in Fig.1 can be found in Ref.[4]and the details are not presented *** 1 includes the traditional reflectors with large apertures and low operation frequencies.

关键词： antenna antennas network

来源：评论

学校读者我要写书评

暂无评论

3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving

3D Multi-Object Tracking Employing MS-GLMB Filter for Autono...

引用

International Conference on Control, Automation and information Sciences (ICCAIS)

作者： Linh Van Ma Muhammad Ishfaq Hussain Kin-Choong Yow Moongu Jeon School of Electrical Engineering and Computer Science GIST Republic of Korea Large-scale AI Research Group Korea Institute of Science and Technology Information (KISTI) South Korea Faculty of Engineering and Applied Science University of Regina Canada

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

The MS-GLMB filter offers a robust framework for tracking multiple objects through the use of multi-sensor data. Building on this, the MV-GLMB and MV-GLMB-AB filters enhance the MS-GLMB capabilities by employing cameras for 3D multi-sensor multi-object tracking, effectively addressing occlusions. However, both filters depend on overlapping fields of view from the cameras to combine complementary information. In this paper, we introduce an improved approach that integrates an additional sensor, such as LiDAR, into the MS-GLMB framework for 3D multi-object tracking. Specifically, we present a new LiDAR measurement model, along with a multi-camera and LiDAR multi-object measurement model. Our experimental results demonstrate a significant improvement in tracking performance compared to existing MS-GLMB-based methods. Importantly, our method eliminates the need for overlapping fields of view, broadening the applicability of the MS-GLMB filter. Our source code for nuScenes dataset is available at https://***/linh-gist/ms-glmb-nuScenes.

关键词： Laser radar Three-dimensional displays Source coding Buildings Radar tracking Cameras information filters Robustness Sensors Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：