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检索条件"主题词=detection algorithms"

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detection of low, small, and slow targets in the air based on improved YOLOv5

Detection of low, small, and slow targets in the air based o...

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algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Jinheng Li Bifeng Cui Yanxu Zhu Zhibin Liu Beijing University of Technology Beijing China

At present, the drone industry is very mature and has been widely used in many fields. While it brings us convenience, it also brings many hidden dangers. Since the target of the UAV is relatively small, the target detection effect of simply using YOLOv5 is poor. Based on this situation, a low-altitude UAV detection algorithm based on improved YOLOv5 is proposed. Targeted improvements have been made to YOLOv5's Backbone, Neck, and Loss. Experiments show that the new method achieves an accuracy of 0.918 and a mAP@.5:.95 of 0.529, which is significantly improved compared with the original.

关键词： Industries High performance computing Object detection Feature extraction Neck detection algorithms Artificial intelligence

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Security Situation Assessment Model of DDoS Attack Based on Progressive Fuzzy C Clustering Algorithm

Security Situation Assessment Model of DDoS Attack Based on ...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Yao Hu Bibo Tu Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

As Software Defined Network (SDN) is widely used, the risk of Distributed Denial of Service (DDoS) attacks on SDN is increasing. The attack traffic generated by DDoS attacks puts huge load pressure on the SDN network, affecting normal network services. In severe cases, the entire SDN network may break down and property damage may be caused. Therefore, the detection of attack mode is very necessary and of great significance. Therefore, this paper proposes an attack detection method based on Fuzzy C-means clustering algorithm (FCM). A DDoS attack detection and defense system is designed, which includes data collection, attack detection and attack defense. Finally, experiments are conducted to verify the effectiveness of the proposed DDoS attack detection algorithm and defense strategy. The experimental results show that the error rate and error rate of FCM fuzzy clustering algorithm are only 4.15% and 3.75 % , which has obvious advantages compared with other commonly used detection methods.

关键词： Error analysis Prevention and mitigation Clustering algorithms Network security Denial-of-service attack Prediction algorithms Indexes Computer crime Software defined networking detection algorithms

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A Power Theft detection Algorithm based on Electricity Usages

A Power Theft Detection Algorithm based on Electricity Usage...

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algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Qiu Yan Zhang JiYuLei Dai Bin Liu ZhouBo Wu Honbo Zhou Electric Power Research Institute Guizhou Power Grid Co. Ltd Guiyang China GuiZhou UbiLink Co. Ltd. Guiyang China

Power theft detection has been a difficult issue for a long time, as part of the construction of the smart grid operation and maintenance management system (SOMS) of power facilities of village and town power supply stations (VTPS), a new algorithm based on user's electricity usage is proposed in this study. First, the regular curves of user power usage are computed and categorized based on the k-means clustering algorithm, then the Fréchet distance algorithm is used to calculate the discrepancies between the categorized curves and the target curve to identify suspicious users. Experiments shows the accuracy is 88.9%, which is a promising result.

关键词： Machine learning algorithms Power supplies High performance computing Clustering algorithms Smart grids detection algorithms Artificial intelligence

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Active Quickest detection When Monitoring Multi-streams with Two Affected Streams

Active Quickest Detection When Monitoring Multi-streams with...

引用

IEEE International Symposium on Information Theory

作者： Qunzhi Xu Yajun Mei Georgia Institute of Technology

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We study the multi-stream quickest detection problem under the active learning setup, It is assumed that there are p local streams in a system and s ≤ p unknown local streams are affected by an undesired event at some unknown time, but one is only able to take observations from r of these p local streams at each time instant. The objective is how to adaptively sample from these p local streams and how to use the observed data to raise a correct global alarm as quickly as possible. In this paper, we develop the first asymptotic optimality theory in the active quickest detection literature for the case when s = r = 2. To be more concrete, we propose to combine three ideas to develop efficient active quickest detection algorithms: (1) win-stay, lose-switch sampling strategy; (2) local CUSUM statistics for local monitoring; and (3) the SUM-Shrinkage technique to fuse local statistics into a global decision. We show that our proposed algorithms are asymptotically optimal in the sense of minimizing detection delay up to the second order subject to the false alarm constraint. Numerical studies are conducted to validate our theoretical results.

关键词： Fuses Delays detection algorithms Monitoring Information theory

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Relationship between RSSI and Human Posture in Wireless Body Area Network

Relationship between RSSI and Human Posture in Wireless Body...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Masato Yoshimi Takumi Seike Takahiro Ito Hidetomo Shibamura Hirokazu Tanaka Graduate School of Information Sciences Hiroshima City University Hiroshima Japan Strategic Technology Center TIS Inc. Tokyo Japan Tsukurubai LLC Fukuoka Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

This paper elucidates the relationship between the received signal strength indicator (RSSI) and human posture using Body Area Network (BAN). With the popularization of wearable devices, it has become possible to acquire not only vital and environmental sensor data but also behavioral and positional information. The role of BAN expands to include the acquisition of physical information in addition detecting vital data. In this paper, we clarify the relationship between RSSI, sensor values, and human posture through experiments using SmartBAN, a BAN standard which can obtain time-synchronized measurements from multiple sensor nodes. This research is conducted with the premise of applying machine learning techniques to the acquired data, aiming to develop more accurate posture detection algorithms. To our best knowledge, this paper is the first to simultaneously measure and evaluate time-synchronized sensor values and RSSI using the SmartBAN system. The short-term experimental results showed a significant correlation between the temporal changes in human posture and RSSI.

关键词： Wireless communication Wireless sensor networks Correlation Machine learning Body area networks Received signal strength indicator Wearable devices detection algorithms Standards Consumer electronics

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Batch Hop-Constrained s-t Simple Path Query Processing in Large Graphs

Batch Hop-Constrained s-t Simple Path Query Processing in La...

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International Conference on Data Engineering

作者： Long Yuan Kongzhang Hao Xuemin Lin Wenjie Zhang Nanjing University of Science and Technology China Shanghai Jiao Tong University China The University of New South Wales Australia

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Hop-constrained s-t simple path (HC-s-t path) enu-meration is a fundamental problem in graph analysis. Existing solutions for this problem focus on optimizing the processing performance of a single query. However, in practice, it is more often that multiple H C-s-t path queries are issued simultaneously and processed as a batch. Therefore, we study the problem of batch H C-s-t path query processing in this paper and aim to compute the results of all queries concurrently and efficiently as a batch. To achieve this goal, we first propose the concept of H C-s path query which can precisely characterize the common computation among different queries. We then devise a two-phase H C-s path query detection algorithm to identify the common H C-5 path queries for the given H C-s-t path queries. Based on the detected HC-s path queries, we further devise an efficient HC-s-t path enumeration algorithm in which the common computation represented by H C-s path queries are effectively shared. We conduct extensive experiments on real-world graphs and the experimental results demonstrate that our proposed algorithm is efficient and scalable regarding processing multiple HC-s-t path queries in large graphs at billion-scale.

关键词： Query processing Scalability Batch production systems Data engineering Computational efficiency detection algorithms

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EEG-Based Mental Fatigue detection Using CNN-LSTM

EEG-Based Mental Fatigue Detection Using CNN-LSTM

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Mingxin Su Weimin Li Fulai Peng Weidong Zhou Rui Zhang Yiming Wen Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China School of Microelectronics Shandong University Jinan China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

In this study, a multi-channel Electroencephalogram (EEG) mental fatigue detection algorithm is proposed based on the Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) network. The CNN-LSTM deep network model is used to distinguish three different mental fatigue states: awake, mild fatigue and severe fatigue. The model is validated on a self-collected dataset collected by the 2-BACK experimental paradigm. The data set contains fatigue data for a total of 10 healthy adults without adverse habits. The average recognition accuracy of the model is 97.12%. The model yields a sensitivity of 97.80% and a specificity of 99.28%. Results show that our proposed CNN-LSTM model can distinguish the three different mental fatigue states effectively.

关键词： Sensitivity Fatigue Brain modeling Electroencephalography Convolutional neural networks detection algorithms Long short term memory

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Towards Machine Learning Support for Embedded System Tests 24

Towards Machine Learning Support for Embedded System Tests

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24th Euromicro Conference on Digital System Design (DSD)

作者： Scharoba, Stefan Basener, Kai-Uwe Bielefeldt, Jens Wiesbrock, Hans-Werner Huebner, Michael Brandenburg Univ Technol Cottbus Senftenberg Chair Comp Engn Cottbus Germany Kostal Ind Elekt GmbH Hagen Germany ITPower Solut GmbH Berlin Germany

ISBN: (纸本)9781665427036

The correctness of embedded systems needs to be ensured by a high number of tests. Large amounts of data reflecting the system behavior are collected during these test runs. Automated test evaluations are often limited to checking very specific requirements which can hardly cover all possible kinds of erroneous behaviors. Manual examinations can compensate this by implicit knowledge of experienced test engineers but are very time-consuming and therefore costly. This paper shows how machine learning can support the evaluation of embedded system tests. Assessment of new test runs is based on available data from previous tests and aims at identifying those that deviate from usual behavior. Moreover the paper presents a generic approach that helps to find the most suitable detection algorithm in the given context. A case study proves the effectiveness of this approach. Quantitative comparisons show that our exploration is able to find solutions that outperform state of the art methods.

关键词： Knowledge engineering Embedded systems Systematics Machine learning Manuals Prediction algorithms detection algorithms

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Channel Estimation Using Multi-stage Compressed Sensing for Millimeter Wave MIMO Systems 93

Channel Estimation Using Multi-stage Compressed Sensing for ...

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IEEE 93rd Vehicular Technology Conference (VTC-Spring)

作者： Hadji, Baghdad Aissa-El-Bey, Abdeldjalil Fergani, Lamya Djeddou, Mustapha Univ Sci & Technol Houari Boumediene LISIC Lab Algiers Algeria IMT Atlantique Lab STICC UMR CNRS 6285 F-29238 Brest France Ecole Mil Polytech BP 17 Bordj El Bahri 16111 Algeria

ISBN: (纸本)9781728189642

Millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) combination technologies have attracted extensive attention from both academia and industry for meeting future communication challenges and requirements. As a viable option to deal with the trade-off between hardware complexity and system performance, hybrid analog/digital architectures are regarded as efficient mmWave MIMO transceivers. While acquiring channel state information (CSI) is a challenging task to design the optimal beamformers/combiners, especially in mmWave communications due to a lot of challenges. Fortunately, the sparse nature of the channel allows to leverage the compressed sensing (CS) tools and theories. However, the critical challenge to develop a CS-based formulation for estimating the mmWave channel is the codebook design (sensing matrices) and its pilot symbol numbers. In this paper, we proposed a multi-stage CS-based algorithm to estimate the channel explicitly using pilot and data symbols which enable increasing the number of measurements to enhance the estimation accuracy and maximize the spatial diversity by reducing the overlapping between training beams. Simulations confirmed that our proposed method has the best results compared to the existing methods based on codebook schemes.

关键词： Millimeter-wave channel estimation Multi-stage compressed sensing MmWave MIMO transceiver detection algorithms Greedy algorithms

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Video monitoring over anti-decubitus protocol execution with a deep neural network to prevent pressure ulcer 43

Video monitoring over anti-decubitus protocol execution with...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Danilovich, Ivan Moshkin, Vyacheslav Reimche, Alexander Tevelevich, Mikhail Mikhaylovskiy, Nikolay NTR Labs Moscow 129594 Russia Tomsk State Univ Tomsk 634050 Russia Ab Ovo Med Tomsk 634062 Russia

ISBN: (纸本)9781728111797

Video monitoring of the patient position in the intensive care units is complicated by the obstacles covering the patient body. Conventional posture detection algorithms do not work in this case. A reformulation of the posture detection problem for the case as an object detection/image classification problem and the use of recent deep learning techniques allowed us to achieve 94.5% accuracy on a pre-clinical test classifying 4 postures using imagery from an off-the-shelf camera and edge processing, which is a 60% improvement over the result previously known in literature. This in turn allowed us to build a ready for the clinical trials system based on inexpensive off-the-shelf cameras.

关键词： Deep learning Protocols Image edge detection Clinical trials Cameras Biology detection algorithms

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