检索结果-内蒙古大学图书馆

Ex(o)plain: Subgroup-Level Analysis of Exoplanet Atmospheric Parameters

IEEE ACCESS 2024年 12卷 139773-139788页

作者： Koudounas, Alkis Giobergia, Flavio Baralis, Elena Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy

Deep learning has been shown to be a valuable tool in astrophysics. In the field of exoplanetary science, deep learning-based approaches are being used extensively to automate the characterization of exoplanet atmospheres, reducing computational costs when compared to conventional methods. However, many atmospheric reconstruction models lack interpretability. We introduce Ex(o)plain, a model-agnostic framework to identify and describe the most meaningful traits that characterize exoplanet atmospheres. Our approach categorizes exoplanets into subgroups based on combinations of various metadata, such as surface gravity, planet radius, and star temperature. We analyze these subgroups to identify those for which the deep learning model performs better or worse than average. This provides useful insights into what is being effectively learned by these black box models and where they still struggle. We explore a practical case based on the synthetic observations generated for the upcoming Ariel mission. Experimental results demonstrate the effectiveness of adopting explanation techniques in revealing meaningful variations in reconstruction quality between individual models and their aggregated ensemble. We additionally show that ensemble approaches significantly outperform single learners. We leverage the same subgroup-based exploration techniques to assess the situations that are most beneficial for the ensemble. Our work provides a more nuanced understanding of deep learning results for exoplanet characterization, aiming to delineate feasible accuracy limits and enable more informed evaluations of these techniques' atmospheric reconstruction capabilities.

关键词： Atmospheric modeling Exoplanet Atmospheric measurements Computational modeling Atmospheric waves Metadata Deep learning Explainable AI detection algorithms divergence exoplanet atmospheric parameters explainable AI subgroup detection

来源：评论

学校读者我要写书评

暂无评论

Redefining Malware Sandboxing: Enhancing Analysis Through Sysmon and ELK Integration

引用

IEEE ACCESS 2024年 12卷 68624-68636页

作者： Mahmoud, Rasmi-Vlad Anagnostopoulos, Marios Pastrana, Sergio Pedersen, Jens Myrup Aalborg Univ Dept Elect Syst CMI Sect Cyber Secur Grp DK-2450 Copenhagen Denmark Univ Carlos III Madrid Escuela Politecn Super Dept Comp Sci Comp Secur COSEC Lab Leganes 28911 Spain

In cybersecurity, adversaries employ a myriad of tactics to evade detection and breach defenses. Malware remains a formidable weapon in their arsenal. To counter this threat, researchers unceasingly pursue dynamic analysis, which aims to comprehend and thwart established malware strains. This paper introduces an innovative methodology for dynamic malware analysis while critically evaluating prevailing technologies and their limitations. The proposed approach hinges on harnessing the capabilities of an open-source Security Information and Event Management (SIEM) toolset, namely the Elastic-Stack. This toolset is utilized to capture, structure, and analyze the behavioral patterns of malware without relying on any pre-existing sandbox framework. This augmentation facilitates a profound understanding of the activities exhibited by malware samples. With the help of the proposed ecosystem, we compile the AAU_MalData dataset that encompasses distinctly the benign and malicious behavior. Specifically, we analyzed the behavior of the 2,800 malware within a realistic network topology and systematically collected Windows event logs, which serve as a comprehensive record of the malware's actions. These event logs are precious as they are organized in a timestamped format, providing a chronological list of system activities, such as event descriptions, process and file details, and registry modifications. These are pivotal in comprehending the malware's functionality and repercussions on the compromised system. Furthermore, by incorporating the MITRE ATT&CK framework, we leveraged the event logs to correlate the malware's mode of operation, delve into its Command and Control operations, and investigate its persistence mechanisms, enabling a structured and practical approach to malware analysis. The AAU_MalData dataset, organized in a JSON format data structure, is offered to the research community first as a proof of concept to demonstrate the toolset's feasibility for dynamic m

关键词： Malware Monitoring Virtualization Virtual machining Virtual machine monitors Vectors Computer security detection algorithms Performance evaluation Dynamic malware analysis sandbox Elasticsearch MITRE ATT&CK Sysmon

来源：评论

学校读者我要写书评

暂无评论

Research on Dynamic Community detection Method Based on an Improved Pity Beetle Algorithm

引用

IEEE ACCESS 2022年 10卷 43914-43933页

作者： Wang, Yan-Jiao Song, Jia-Xu Sun, Peng Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China China United Telecommun Co Ltd Jilin Branch Changchun 130000 Peoples R China

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

关键词： Heuristic algorithms Clustering algorithms Optimization Image edge detection detection algorithms Costs Statistics Community detection dynamic network evolutionary clustering Pity Beetle algorithm

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

引用

IEEE ACCESS 2024年 12卷 36622-36635页

作者： Altabaji, Wassem I. A. E. Umair, Muhammad Tan, Wooi-Haw Foo, Yee-Loo Ooi, Chee-Pun Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia

Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

关键词： Plant diseases detection algorithms Deep learning Convolutional neural networks Agriculture Classification algorithms Crop yield Transfer learning Hyperparameter optimization convolutional neural networks transfer learning image classification

来源：评论

学校读者我要写书评

暂无评论

Urban Interior Boundaries Delimitation Respecting to Human Mobility

引用

IEEE ACCESS 2024年 12卷 159635-159647页

作者： Wei, Yu Zhao, Xi Taiyuan Univ Technol Coll Comp Sci & Technol Coll Data Sci Taiyuan 030024 Peoples R China Xi An Jiao Tong Univ Sch Management Key Lab Minist Educ Proc Control & Efficiency Engn Xian 710049 Peoples R China

Administrative divisions are regional divisions of the state for the purpose of hierarchical administration. With the development of urbanization process, a concern arising from the changing urban dynamics is that whether current administrative division accords with urban development? Following the concept of urban space formed by human activities, existing studies have sought proxies for human activities and then delineated urban boundaries formed by human activities from a data-driven perspective. However, their lack of exploration into the implicit human activity patterns behind the proxies results in weak interpretability and rationality of methods and results. This paper studies the delineation of urban interior boundaries respecting to human mobility patterns. Specifically, this paper first uses human mobility as a proxy to construct a network that represents spatial interaction in urban regions. Then we explore the region-based human crowd mobility patterns to reveal the mechanisms behind the formation of urban space through human activities. Finally, we employ the community detection technology to naturally delimit the urban interior boundaries formed by human mobility, and make a comparison with the official urban boundaries. Taking Xi'an in Shaanxi Province of China as an example, we conduct the delineation on the spatial interaction network based on the real mobility information of 24,770,715 mobile phone users in the whole city. We find that human mobility can establish a stable correlation between regions (or capture the objective correlations between regions), and the human crowd patterns are applicable for mining unusual urban regions from the perspective of anomaly detection, which are of great significance for understanding the urban spatial structure based on empirical evidence. In addition, in the final delineation result, some unexpected communities that are closely linked due to human activities appear from the results, and these findings help t

关键词： Urban areas Entropy Correlation Trajectory Cognition Reliability detection algorithms Uncertainty Spatial databases Space exploration Administrative divisions a data-driven perspective urban interior boundaries human mobility patterns community detection

来源：评论

学校读者我要写书评

暂无评论

Adaptive R-Peak detection Algorithm Based on Brown Exponential Smoothing Model

引用

IEEE ACCESS 2022年 10卷 114355-114363页

作者： Hao, Zihao Zhang, Xiaoming Lai, Zhengxi North Univ China Natl Key Lab Elect Measurement Technol Taiyuan 0351 Peoples R China North Univ China Minist Educ Key Lab Instrumentat Sci & Dynam Measure Taiyuan 030051 Peoples R China Shenzhen ImyFit Technol Co Ltd Shenzhen 518107 Peoples R China

ECG is one of the most effective medical tests for heart disease diagnosis, and R-peak detection is the first step in ECG interpretation. For wearable ECG signals, the difficulty of R-peak detection mainly lies in the interference of dynamic strong noise, and the limited hardware computational resources limit the use of some complex algorithms. Therefore, an adaptive threshold R-peak detection algorithm based on Brown's exponential smoothing model is proposed in this paper. The algorithm selects features based on the morphological characteristics and occurrence law of R-peaks, updates the threshold parameters using the Brown exponential smoothing model, optimizes the smoothing coefficients in it using the relative error least squares method, corrects the smoothing ability of the observation error and the response speed to the change of the observation, so that the updated threshold can be more consistent with the R-peak detection. Finally the algorithm achieves 99.6% precision, 99.7% recall and 99.65% F1 score on the self-constructed ECG dataset, and compares with other R-peak detection algorithms to determine the superiority of the proposed algorithm in some performance metrics. It is demonstrated experimentally that the algorithm can adapt well to the strong noise environment and obtain satisfactory R-peak detection accuracy.

关键词： Electrocardiography Smoothing methods Adaptation models Interference detection algorithms Heuristic algorithms Biomedical monitoring Wearable ECG signal R-peak detection brown exponential smoothing model adaptive threshold

来源：评论

学校读者我要写书评

暂无评论

A Passive Islanding detection Method for Distribution Power Systems With Multiple Inverters

引用

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第5期10卷 5727-5737页

作者： Song, Guanhong Cao, Bo Chang, Liuchen Univ New Brunswick Emera & NB Power Res Ctr Smart Grid Technol Fredericton NB E3B 5A3 Canada

The high penetration of grid-connected distributed energy resources (DERs) leads to a need for smart inverters to enhance their performance and minimize the negative impacts of these resources on grid behavior. However, the interconnection of these smart inverters may interfere with their anti-islanding algorithms, which creates detection difficulties. Advanced islanding detection techniques are therefore required to detect and disrupt islanding operations under such an environment with system's disturbances and interferences. Even though various islanding detection methods have been developed in recent literature to identify islanding operations with high detection accuracy and minimized nondetection zone, these methods mainly focus on a single inverter system, which is incompatible in modern power system with high penetration of grid-connected inverters. In this article, an advanced passive islanding method is proposed for a single-phase distribution power system taking account of the interferences and disturbances brought by these grid-connected inverters. The effectiveness of the proposed passive islanding method is validated through experiments in a laboratory platform. Also, the experimental results have verified that the proposed methods can effectively terminate the islanding operation even with rich inverters' interferences.

关键词： Inverters Islanding Power systems Harmonic analysis Power system stability Resonant frequency Power system harmonics detection algorithms distributed power generation islanding

来源：评论

学校读者我要写书评

暂无评论

YOLOv5-Fog: A Multiobjective Visual detection Algorithm for Fog Driving Scenes Based on Improved YOLOv5

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Wang, Hai Xu, Yansong He, Youguo Cai, Yingfeng Chen, Long Li, Yicheng Sotelo, Miguel Angel Li, Zhixiong Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Univ Alcal Dept Comp Engn Alcal Henares Madrid 28801 Spain Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Opole Univ Technol Fac Mech Engn PL-45758 Opole Poland

With the rapid development of deep learning in recent years, the level of automatic driving perception has also increased substantially. However, automatic driving perception under adverse conditions, such as fog, remains a significant obstacle. The existing fog-oriented detection algorithms are unable to simultaneously address the detection accuracy and detection speed. Based on improved YOLOv5, this work provides a multiobject detection network for fog driving scenes. We construct a synthetic fog dataset by using the dataset of a virtual scene and the depth information of the image. Second, we present a detection network for driving in fog based on improved YOLOv5. The ResNeXt model, which has been modified by structural re-parameterization, serves as the model's backbone. We build a new feature enhancement module (FEM) in response to the lack of features in fog scene images and use the attention mechanism to help the detection network pay more attention to the more useful features in the fog scenes. The test results show that the proposed fog multitarget detection network outperforms the original YOLOv5 in terms of detection accuracy and speed. The accuracy of the Real-world Task-driven Testing Set (RTTS) public dataset is 77.8%, and the detection speed is 31 frames/s, which is 14 frames faster as compared with the original YOLOv5.

关键词： Feature extraction detection algorithms Atmospheric modeling Autonomous vehicles Training Meteorology Object detection 2-D object detection autonomous driving complex traffic conditions fog

来源：评论

学校读者我要写书评

暂无评论

Predictive Braking With Brake Light detection-Field Test

引用

IEEE ACCESS 2022年 10卷 49771-49780页

作者： Pirhonen, Jesse Ojala, Risto Kivekas, Klaus Tammi, Kari Aalto Univ Dept Mech Engn Espoo 02150 Finland Univ Helsinki Fac Agr & Forestry Helsinki 00014 Finland

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.

关键词： Brakes Control systems Safety Cruise control Radar detection algorithms Cameras Advanced driver assistance systems automotive applications intelligent vehicles machine vision

来源：评论

学校读者我要写书评

暂无评论

Continuous Seizure detection Based on Transformer and Long-Term iEEG

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第11期26卷 5418-5427页

作者： Sun, Yulin Jin, Weipeng Si, Xiaopeng Zhang, Xingjian Cao, Jiale Wang, Le Yin, Shaoya Ming, Dong Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Brain Sci & Neural Engn Tianjin 300072 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Huanhu Hosp Dept Neurol Tianjin 300072 Peoples R China

Automatic seizure detection algorithms are necessary for patients with refractory epilepsy. Many excellent algorithms have achieved good results in seizure detection. Still, most of them are based on discontinuous intracranial electroencephalogram (iEEG) and ignore the impact of different channels on detection. This study aimed to evaluate the proposed algorithm using continuous, long-term iEEG to show its applicability in clinical routine. In this study, we introduced the ability of the transformer network to calculate the attention between the channels of input signals into seizure detection. We proposed an end-to-end model that included convolution and transformer layers. The model did not need feature engineering or format transformation of the original multi-channel time series. Through evaluation on two datasets, we demonstrated experimentally that the transformer layer could improve the performance of the seizure detection algorithm. For the SWEC-ETHZ iEEG dataset, we achieved 97.5% event-based sensitivity, 0.06/h FDR, and 13.7 s latency. For the TJU-HH iEEG dataset, we achieved 98.1% event-based sensitivity, 0.22/h FDR, and 9.9 s latency. In addition, statistics showed that the model allocated more attention to the channels close to the seizure onset zone within 20 s after the seizure onset, which improved the explainability of the model. This paper provides a new method to improve the performance and explainability of automatic seizure detection.

关键词： Electroencephalography Feature extraction Transformers Recording Brain modeling detection algorithms Sensitivity Seizure detection iEEG transformer self-attention deep learning epilepsy

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：