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machine learning Detects Anomalies in OPS-SAT Telemetry 23rd

Machine Learning Detects Anomalies in OPS-SAT Telemetry

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23rd international conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Kotowski, Krzysztof Andrzejewski, Jacek Musial, Alicja Evans, David Zelenevskiy, Vladimir Bammens, Sam Laurinovics, Rodrigo Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Akademicka 16 Gliwice44-100 Poland KP Labs Konarskiego 18C Gliwice44-100 Poland European Space Agency/ESOC Robert-Bosch-Str. 5 Darmstadt64293 Germany Telespazio Germany GmbH Europaplatz 5 Darmstadt64293 Germany IrbGS ltd "Viraki" Ance Parish IrbeneLV-3601 Latvia

ISBN: (纸本)9783031359941

Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and affected by incorrect acquisition, resulting in missing parts of the signal. In this paper, we tackle this issue and propose a machine learning approach for detecting anomalies in single-channel satellite telemetry. To validate its capabilities in a practical scenario, we build a dataset capturing the nominal and anomalous telemetry data captured on board OPS-SAT—a nanosatellite launched and operated by the European Space Agency. Our extensive experimental study showed that the proposed algorithm offers high-quality anomaly detection in real-life satellite telemetry, reaching 98.4% accuracy over the unseen test set. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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GIM: Versatile GNN Acceleration with Reconfigurable Processing-in-Memory

GIM: Versatile GNN Acceleration with Reconfigurable Processi...

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IEEE international conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Chen Nie Guoyang Chen Weifeng Zhang Zhezhi He School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Alibaba Cloud Infrastructure Sunnyvale USA

Recent boost of deep learning has revolutionized many machine learning tasks, including the graph neural networks (GNNs) that are specifically designed for non-Euclidean graph data. GNNs have been widely adopted in numerous real-world applications, such as the recommendation system. However, with increasingly enlarged graph size and complexity, GNN performance on conventional computers has been severely hindered by the memory bottleneck. The challenge attracts wide investigations, and the processing-in-memory (PIM) architecture arises as one of the most promising solutions. Prior works have leveraged the ReRAM crossbars as analog dot-product engines to accelerate the vector-matrix multiplications in GNN, and achieve prominent performance improvements over modern CPUs and GPUs. Nevertheless, analog computing is known to be variation-vulnerable, which hampers the inference accuracy of GNN. Besides, the mixed-signal peripherals (e.g., ADC) are hardware-expensive and specialize in dense computations, which makes the analog crossbar-based PIM not the ideal candidate for GNN inference whose computation is of great *** this work, we propose a novel digital-PIM architecture for GNN acceleration, namely GIM. Our compact yet efficient digital computing paradigm can greatly boost computing parallelism with a minimum budget. GIM integrates dedicated optimizations on both operand- and bit-sparsity, to eliminate sparse computations thus significantly boost the performance. Meanwhile, at the software level, we implement data-layout optimizations to minimize the inter-memory communications and maximize computing parallelism. Our design derives prominent performance improvements over the modern CPU, GPU, and state-of-the-art PIM-based accelerators. Compared to modern CPU and GPU, GIM averagely achieves 24485× and 778× of speedup, and 78480× and 8906× of energy reduction. Compared to the state-of-the-art PIM-based GNN accelerators ReFlip and PIMGCN, GIM averagely achieves 9.0

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Strategies for Network Traffic Optimization: An In-Depth Analysis of Multipath Routing Algorithms in SDN

Strategies for Network Traffic Optimization: An In-Depth Ana...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Venkata Sai. R Prabu. U Satwik. Ch Sanjaya Kumar Panda V. Geetha Department of CSE Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of CSE National Institute of Technology Warangal India Department of Information Technology Puducherry Technological University Puducherry India

Demand for online services and cloud services has been increasing day to day. The usage of these services increases network traffic, which is difficult to handle by traditional networks. In the traditional networking paradigm, it is very difficult to integrate various networking devices of different versions. The programming capability in traditional networks is very less which requires manual installation even for small changes in the network. The traditional networking paradigm is a distributed architecture, in which individual networking devices will take control of the network devices which are under them. Overcoming the above limitations, a new networking paradigm called software Defined Networking (SDN) came into existence. SDN possesses a centralized architecture in which the controller will take all the routing decisions and those instructions will be forwarded to the forwarding elements called switches. Through programming, networking devices can be configured which reduces manual installation. In this article, a detailed study on multipath routing in SDN is done. It provides a detailed study regarding different ways of implementing multipath routing. Multipath routing based on optimization techniques and machine learning techniques are discussed. This chapter consists description of how load balancing can be improved and how Quality of Service (QoS) can be improved using multipath routing. Multipath routing using shortest path algorithms like the Bellman-Ford algorithm, and Depth First Search (DFS) are discussed and how deep neural networks and reinforcement learning mechanisms will be used to implement multipath routing and to improve network performance is discussed. This study helps other researchers who try to do research based on multipath routing over SDN to get an overview of multipath routing.

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Research on the application of artificial intelligence algorithms in drought prediction 22

Research on the application of artificial intelligence algor...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer engineering

作者： Mengxi Liu Guangjun Zai Guofu Wang Weiwei Shi Guanqun Zhang School of Cyber Science and Engineering ZhengZhou University China National Climate Center China Beijing SuperMap Software Co. Ltd China

ISBN: (纸本)9781450397148

With the aggravation of global warming, drought has caused more and more serious impact on society and national economy, so the accurate prediction of drought is also the most important thing to deal with the frequent drought problem. On the other hand, artificial intelligence technology has shown its advantages in various fields and has a broad application prospects, so it is gradually being used in drought prediction. In this paper, drought prediction algorithms in recent years are reviewed, and existing cases of drought prediction using different arithmetic models for different drought indices are studied. The advantages and disadvantages of different algorithms are compared, the challenges of drought prediction are summarized, and the future of drought prediction is prospected.

关键词： Drought forecasting machine learning Time series forecasting

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DTC: Addressing the long-tailed problem in intrusion detection through the divide-then-conquer paradigm

DTC: Addressing the long-tailed problem in intrusion detecti...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Chaoqun Guo Nan Wang Yuanlin Sun Dalin Zhang Dept. School of Software Engineering Beijing Jiaotong University Beijing China

Intrusion detection systems (IDS) analyze the monitored data to detect patterns or signatures that correspond to known cyberattack techniques, vulnerabilities, or deviations from established baselines. They employ different algorithms and techniques to identify potential threats. When building "Known Patterns and Signatures", it always faces the long-tailed problem, which refers to the imbalanced distribution of different types of network traffic or events in a dataset, which means that there are very few instances of certain types of intrusions compared to more common types of network traffic or benign events. Such a situation poses a great challenge to deep learning-based or machine learning-based detection models on how to handle it. Models may struggle to learn from long-tailed distributions because they tend to bias their predictions toward the majority, and perform well on common events but poorly on rare intrusions. To address this problem, different from previous methods concerning training the model on the whole samples to obtain a balanced data distribution, we first focus on dividing the whole samples into a balanced group and an imbalanced one, then, we train a detection model on the balanced group. Cycle over and over again. Specifically, We propose to use a Gaussian mixture flow filter to progressively perform sample aggregation, continuously transforming the long-tail distribution into a more balanced which allows us to train the classifier on the obtained balanced group. The separated training samples with high distribution balance make it easier to train subsequent classifiers and mitigate the head-to-tail bias. Through extensive experiments, we have achieved new state-of-the-art performance on common intrusion detection datasets such as UNSW-NB15, CIC-IDS2017, and NSL-KDD. These results demonstrate that it is possible to surpass carefully constructed balanced datasets by progressively distinguishing the head class and the tail class.

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A Research Survey on Traffic Management, Accident Detection and Emergency Notification Systems to Improve Society Health

A Research Survey on Traffic Management, Accident Detection ...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Maya P. Shelke Nihar M. Ranjan Prathamesh Ukirde Shruti Pagade Shantanu Hanchate Nikhil Mehtre Department of Information Technology JSPM's Rajarshi Shahu College of Engineering Pune India

The rapid growth of urbanization coupled with the rise in number of vehicles on roads have resulted in various issues, including traffic congestion, emergency vehicle delays, and delayed medical assistance to accident victims. To tackle these problems, we propose an Intelligent Traffic Management and Accident Detection System with Emergency Notifications using Artificial Intelligence (AI), implemented in a software-based, simulated, controlled environment. Our system employs a combination of computer vision, image processing, and machine learning techniques to collect data from strategically placed cameras along the roads and a camera mounted on top of signals. The captured image frames are sent to a central server located at the intersection, where the system's logic processes them in real-time to dynamically adjust signal timings based on the traffic density. In addition, our system provides priority for emergency first-responder vehicles to ensure their swift and safe passage through traffic. The proposed system also includes an accident detection system that classifies accidents based on their severity. If the severity exceeds a predefined threshold value, the system detects the location of the accident and sends an alert to the nearest hospital, allowing medical assistance to reach the accident site promptly. Our system aims to offer an efficient and effective solution to the challenges posed by increasing traffic. By incorporating AI, we can provide an advanced traffic management system that can optimize traffic flow and prioritize emergency vehicle movements, ensuring the safety and well-being of all commuters and accident victims.

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4th international conference on Soft Computing and its engineering Applications, icSoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th international conference on Soft Computing and its engineering Applications, icSoftComp 2022

ISBN: (纸本)9783031276088

The proceedings contain 36 papers. The special focus in this conference is on Soft Computing and its engineering Applications. The topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache Memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using machine learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio Dataset;human Activity Recognition in Videos Using Deep learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using machine learning Algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database;A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement learning Algorithms for Effective Resource Management in Cloud Computing;FedCLUS: Federated Clustering from Distributed Homogeneous Data;corpus Building for Hate Speech Detection of Gujarati Language;a software

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machine learning-based study of eye features under the emotion of anger 22

Machine learning-based study of eye features under the emoti...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer engineering

作者： Zhuoluo Huang Yafang Li Key Laboratory of Traditional Chinese Medicine Psychology and Brain Science Jiangxi Provincial Administration of Traditional Chinese Medicine China

ISBN: (纸本)9781450397148

Based on the theory of "the liver is open to the eyes", this study uses a machine learning approach to explore methods and models that can be applied to the identification of eye features for angry emotions, in the hope of providing some ideas for objective identification of angry emotions. We used the angry emotion faces in the China Affective Picture System (CAPS) as the research object. Using multiple feature point inspection, we use openface software to obtain the 3D coordinate point coordinate information of the eye features of the angry emotion images. The aggregated data were then statistically analyzed using matlab software to calculate the eye feature values to be used, and then continued to process them using the classifier function to build a face eye feature point model based on Support Vector machine (SVM), Linear Discriminant Analysis(LDA), K-Neares Neighbor, Ensemble Subspace Discriminant (ESD), Decision Tree(DT) and other classifiers, and the model was comprehensively evaluated in terms of accuracy, area under the ROC curve and other evaluation metrics. For the results of the measured classification tests, the correct rate obtained by DT classification reached 83.1%, KNN reached 84.7%, ESD reached 88.1%, SVM reached 84.7% correct rate and LDA reached 88.1%. The effect of the eye feature description parameters on the classification effect of anger emotion was verified through experiments, and the best performance of ESD and LDA classification was achieved. Based on the above results we can draw the following conclusions. "The eye is the heart's ambassador", and the classifier constructed based on the eye diagnosis information can play the role of classification, which has certain scientific value and can provide objective diagnostic guidance for clinically relevant psychosomatic problems.

关键词： machine learning Anger emotions Eye features Image processing

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AD-PU: A Novel Approach for Automated Identification of the Outliers in User Interface Testing (UAT)

AD-PU: A Novel Approach for Automated Identification of the ...

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international Automatic Testing conference, AUTOTESTCON

作者： S.M.N.S.K Seneviratne Victoria Penenco Dharshana Kasthurirathna Faculty of Graduate Studies and Research Sri Lanka Institute of Information Technology Sri Lanka UAT London Stock Exchange Group London Dept. of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

User acceptance testing (UAT) is the process that validates the system under test (SUT) behaves according to the business requirements. A User would use the graphical user interface to interact with the underlying code. With the improvement of various devices, end-user testing has become even more complex and time consuming. Every user has a unique interaction pattern. Due to this expected test coverage would not be met by focusing only on the functionality itself. This research is to address the issue of UAT test automation coverage and reduce defect leakage in production environments. The proposed automation solution is to identify more defects in the UAT static testing phase and make the system under test more user friendly before handing the product to the consumers. The proposed solution use machine learning approach to identify user behaviors that does not match the proposed new functionality. The proposed AD-PU model would evaluate non-prominent user behaviours as per perceived usefulness which would effect the decision to accept the functionality as user acceptable or not.

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dToF LIDAR System Using Addressable Multi-Channel VCSEL Transmitter, 128x80 SPAD Sensor, and ML-Based Object Detection for Adaptive Beam-Steering 44

dToF LIDAR System Using Addressable Multi-Channel VCSEL Tran...

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44th Annual IEEE Custom Integrated Circuits conference, CICC 2023

作者： Wu, Yifan Zhou, Sifan Sun, Miao Xia, Tao Qian, Jian Wang, Lei Shi, Shi Cui, Lebei Wang, Chill Li, Yuan Yu, Hengwei Lin, Zhihong Qiu, Lei Qin, Yajie Sun, Min Bai, Rui Chen, Xuefeng Chiang, Patrick Yin Zhuo, Shenglong Tongji University The College of Electronics and Information Engineering Shanghai China Fudan University Shanghai China Southeast University Shanghai China Tencent Research China PhotonIC Technologies Shanghai China

ISBN: (纸本)9798350399486

Light detection and ranging (LiDAR) technology is increasingly being deployed in vehicles for advanced driving assistance system (ADAS) and is a potential critical technology for autonomous driving in the future. Currently, hybrid solid-state LiDARs based on micro-electro- mechanical system (MEMS) or rotating mirrors are the most mature solutions [1]. Unfortunately, these hybrid LiDARs still exhibit high risk of damage due to their limited mechanical reliability in harsh environments across many years of operation. Furthermore, these solutions require highly accurate calibration due to the complex optical alignment, resulting in higher production cost. Compared with these mechanical solutions, solid-state flash LiDARs without any moving parts are easier to mass-produce, more robust, less costly, and exhibit smaller form-factor, allowing flash-LiDAR to be embedded in multiple places around the automobile. Nevertheless, the detection distance of a single-flash LiDAR is short due to the smaller optical power limited by the laser eye-safety requirement, VCSEL thermal heating, and long-term VCSEL reliability [2]. Furthermore, for some applications, the distance of only one or two nearest objects needs to be accurately measured, and therefore empty parts of the field-of-view (FOV) are of limited interest. Unfortunately, single-flash LiDARs capture the point cloud of the entire FOV within each frame, resulting in high power consumption. © 2023 IEEE.

关键词： Object detection

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