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共 729 条记录，以下是641-650 订阅

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Putting Together Wavelet-based Scaleograms and Convolutional Neural Networks for Anomaly Detection in Nuclear Reactors 19

Putting Together Wavelet-based Scaleograms and Convolutional...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Thanos Tagaris George Ioannou Maria Sdraka Georgios Alexandridis Andreas Stafylopatis Institute of Communication and Computer Systems National Technical University of Athens Zografou Campus Athens Greece

ISBN: (纸本)9781450372534

A critical issue for the safe operation of nuclear power plants is to quickly and accurately detect possible anomalies and perturbations in the reactor. Defects in operation are principally identified through changes in the neutron flux, as captured by detectors placed at various points inside and outside of the core. While wavelet-based analysis of the measured signals has been thoroughly used for anomaly detection, it has yet to be coupled with deep learning approaches. To this end, this work presents a novel technique for anomaly detection on nuclear reactor signals through the combined use of wavelet-based analysis and convolutional neural networks. In essence, the wavelet transform is applied to the signals and the corresponding scaleograms are produced, which are subsequently used to train a convolutional neural network that detects possible perturbations in the reactor core. The overall methodology is experimentally validated on a set of simulated nuclear reactor signals generated by a well-established relevant tool. The obtained results indicate that the trained network achieves high levels of accuracy in failure detection, while at the same time being robust to noise.

关键词： Wavelet Transformation Anomaly Detection Nuclear Power Plants Convolutional Neural Networks Scaleograms Deep learning signal processing

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An Ideology for the Prediction of Critical Haplotype Blocks of Variants in Genes (Cyp2c9 And Vkorc1) for Warfarin (Anticoagulant) Drug Dosage to Treat Heart Patients Efficiently by Using Ml (machine learning) and Data Stream Mining Techniques 19

An Ideology for the Prediction of Critical Haplotype Blocks ...

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Proceedings of the 2019 3rd international conference on Advances in Image processing

作者： Hina Saeeda Muhammad Adil Abid College of information science and engineering Ocean University of China Qingdao China Computer Science & Technology Shandong University Qingdao China

ISBN: (纸本)9781450376754

Now a day's on time treatment of heart diseases is a very critical part of medical diagnoses. So far there are total 50 SNP (Single Nucleotide Polymorphism) diagnosed that are responsible for the heart problems. But it is very hard to study all of the SNP together because of their different base pairs' locations or changes in base pairs positions (variations in genetic code A C G T). These all 50 SNP are present in all individuals with different variations, it is a tough job to calculate all the changes in this SNP set as there are total of (50^50) positions to calculate which is making it a huge data set. For acquiring a data set of all these positions, we will need some good Data Stream Mining (data mining techniques) to find out all the possible locations of all the variants responsible for the heart problems. In this research paper, we are giving a short analysis and introduction to the problem of heart patients drug dosage associated with anticoagulant (Warfarin) and its risks, solution for the challenge of calculating all variants of two genes (CYP2C9 and VKORC1) and advantages of the proposed solution in the future.

关键词： machine learning Single Nucleotide Polymorphism genes VKORC1 data stream mining data mining CYP2C9 warfarin Haplotype Blocks

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A Shilling Attack Model Based on TextCNN

A Shilling Attack Model Based on TextCNN

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Dongfang Hu Bin Xu Jun Wang Linfeng Han Jiayi Liu Henan Xuji Instrument Co. Ltd Xuchang China China Electric Power Research Institute Beijing China State Grod Shanxi Marketing Service Center Taiyuan China

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 - value as the evaluation index.

关键词： Analytical models Data models Feature extraction Recommender systems Detection algorithms machine learning algorithms Classification algorithms

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A multimodal-signals-based gesture recognition method for human machine interaction

A multimodal-signals-based gesture recognition method for hu...

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IEEE international conference on Unmanned Systems (ICUS)

作者： Xiaochuan Zhao Jie Huang Junzhe Zheng Yanlin Ma Hongyi Tang Institute of Computer Applied Technology of China Weapon Industry Beijing China The Ohio State University Columbus USA

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

As the capacity for machines to extend human capabilities continues to grow, the communication channels used also need to expand. Allowing machines to translate gestural command into robot movements can make the communication between humans and machines more similar to interpersonal communication. Yet for operating requirements, instantaneity and precision of the interaction must reach the application level in realistic scenarios, and the accuracy of results cannot be guaranteed by thejudgement of a single motion sensor. Therefore, exploring a gesture detection technology that integrates multisensor information is very necessary. The presented work takes a step towards real-time gesture detection by fusing multiple physiological signals with wearable motion sensors. An algorithm is presented for processing and extracting motion signal acquired via inertial measurement unit (IMU) and electromyography (EMG) with a high error-tolerant way of wearing, and it is applied to the gesture recognition model established in reasonable threshold and logical judgment. This enables real-time gesture detection of 24 various assembled movements such as rotating palms while bending arms, clenching fist when unwinding upper limb. The result of involving intentional behavior by analyzing electroencephalogram (EEG) is also added to the gesture recognition process for eliminating unconscious actions. Together, these pipelines offer efficient gesture vocabulary suitable for remotely controlling robots. Experiments evaluate classifier performance and interface efficacy. The system successfully detected 95.6% of 360 commands, and the average processing time was 25.4ms among all the 15 trials of the experiment.

关键词： Electromyography Real-time systems Electroencephalography Robots Robot sensing systems Time series analysis Lenses

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Temporal Consistency Based Deep Face Forgery Detection Network 1

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3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Peng, Chunlei Zhang, Wenbo Liu, Decheng Wang, Nannan Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Xi’an710071 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi’an710071 China State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Xi’an710071 China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

With the rapid development of deep learning techniques as well as increasingly more visual information being made publicly available on the Internet, image translation methods have achieved great progress and encouraging performance. The manipulation and fabrication of visual information has become accessible and difficult to distinguish by the naked eye, which will have adverse effects on cloud and communication security. Thus, face forgery detection techniques have recently attracted increasing attention. Most recent works regard the face forgery detection problem as the typical image classification task, ignoring the exploration of inherent properties of forgery visual information itself. In this paper, we first explore the inherent limitation of fake videos, and find that the temporal consistency could help distinguish fake faces from real faces. A temporal consistency based deep face forgery detection network is proposed to directly detect fake videos when given multiple consistent video frames. The proposed method effectively considers the frame consistency property and achieves promising detection performance. Experimental results on the face forgery detection dataset demonstrate the superior performance of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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A Modeling Attack Resistant R-XOR APUF Based on FPGA 3

A Modeling Attack Resistant R-XOR APUF Based on FPGA

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IEEE 3rd international conference on signal and Image processing (ICSIP)

作者： Dan, Fukui Xu, Yehan Li, Zheng Wen, Jing Liu, Ben Chen, Shuai Li, Bing Southeast Univ Shenzhen Res Inst Nanjing Jiangsu Peoples R China Southeast Univ Sch Microelect Nanjing Jiangsu Peoples R China Southeast Univ Sensors Technol Network Res Ctr Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538663967

A Physically Unclonable Functions (PUFs) extracts the manufacturing variations of integrated circuits for key generation and authentication. It can be used to address the security issue in traditional non-volatile memory (NVM)-based key generation and authentication system. However, the powerful modeling attack based on machine learning has become a new threat of Strong PUFs-based authentication scheme. In this paper, we proposed a novel reconfigurable XOR Arbiter Physical Unclonable Functions (R-XOR APUFs) to resist this modeling attack. In this paper, the R-XOR APUF consist of multiplexers and inverters. The structure of generating two responses is configured according to challenges. The response of R-XOR APUFs is generated by XORing the two response. Therefore, R-XOR APUFs does not have a uniform model and effectively resists machine learning-based modeling attacks. The experiment results reveal that the uniqueness of R-XOR APUFs is 42.15% (the idea value is 50%) and the prediction rate of R-XOR APUFs reduces from 95% to 55% (the idea value is 50%) compared to the traditional APUFs.

关键词： physical unclonable functions modelling attack reconfigurable machine-learning

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An AI Driven Real-time 3-D Representation of an Off-shore WT for Fault Diagnosis and Monitoring 19

An AI Driven Real-time 3-D Representation of an Off-shore WT...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Amin Amini Jamil Kanfound Tat-Hean Gan Brunel Innovation Centre Brunel University London Kingston Ln London Uxbridge United Kingdom Brunel Innovation Centre Brunel University London Kingston Ln London Uxbridge United Kingdom TWI Ltd Granta Park Great Abington Cambridge U.K

ISBN: (纸本)9781450372534

In recent years, digital twin (DT) has been an efficient and reliable representation tool for Industry 4.0 in general and fault diagnosis and monitoring, especially in machinery consisting of several components working in tandem that introduce a large amount of data to be processed. Having a cloud-based and platform-independent system that can interpret Big Data in real-time and represent the processed information in a user-friendly manner offers the opportunity not only for improving fault diagnosis and troubleshooting automation, but also offering a better representation of data for operators, technicians and engineers. This not only decreases the repair costs by enabling constant components' state of health awareness, but also increases the efficacy of the apparatus by lowering the downtimes due to faults and *** this paper (WindTwin), we propose a real-time, interactive and dynamic 3-D neural-network model that provides important information of a small-scale wind turbine (WT) machinery setup including the state of health of different components (bearings, shafts) based on monitoring sensors (vibrations and temperature) and operations conditions (rotation speed). The system features real-time data processing of six sensors with eight channels including two single axis-accelerometer, one tri-axial accelerometer, one acoustics (microphone), one temperature and one light sensor (for rotational speed) with sampling data collection rate of 51.2 kHz achieved through an edge developed machine learning algorithm based on Artificial Neural Network (ANN) algorithm using single hidden-layer feed forward neural network (SLFN) and gradient-based backpropagation (BP) training algorithm. The system also provides platform-independent (desktop and mobile companion app) user interfaces thanks to its cloud-based data processing integration. The prototype system proved to have on average 83.33 % accuracy for the vibration-based condition monitoring model across the three diffe

关键词： digital twin cloud-computing machine learning condition monitoring big data digitization artificial intelligence

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Arabic Dialect Identification in Social Media

Arabic Dialect Identification in Social Media

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international conference on Computer Applications & Information Security (ICCAIS)

作者： Reem AlYami Rabeah AlZaidy Information and Computer Science Department King Fahd University of Petroleum & Minerals

ISBN: (数字)9781728142135

ISBN: (纸本)9781728142142

Although the Arabic language is spoken in twenty-two countries by more than 250 million speakers, it is still considered by Natural Language processing NLP practitioners as a low resource language. Formal sources of Arabic texts are typically written in Modern Standard (or Written) Arabic (MSA), which is a form that is used in formal writing and taught in schools to Arabic speakers. However, informal communication among Arabic speakers is through informal local diglossic dialects. A diglossic language is one where the speakers of the same language have varying dialects. In Arabic, there are multiple dialects in different regions of the Arab world: Gulf, Levantine and North Africa. Users commonly communicate in social media using their local dialect rather than the formal MSA. This introduces a core NLP problem for Arabic, which is dialect identification. It is essential to identify the specific dialect prior to performing tasks such as parsing, tokenizing and other downstream tasks such as semantic inferences. processing massive amounts of data written in these local dialects requires this identification step to improve accuracies, especially for automatic text comprehension tasks. Although Arabic dialects share a majority of common words, it is not uncommon for the same word to have different meanings across dialects. In addition to improving NLP task accuracies, Arabic Dialect Identification ADI enables a finer-grained demographic identification for mining texts related to consumer reports, health forums, entertainment and tourism reviews, and many others which ultimately lead to improved services for each demographic. The problem of ADI has been addressed by several studies such as (Al-Walaie & Khan, 2017), and (Harrat et al., 2019). Some works focus mainly on curating data sets for the problem such as the Sham dataset proposed by (Abu Kwaik et al., 2018). In this work we focus on both tasks: we curate an Arabic dialect dataset for two variants of Arabic (Saudi A

关键词： Task analysis Support vector machines Natural language processing machine learning Training Computational modeling Data models

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machine learning and DSP Algorithms for Screening of Possible Osteoporosis Using Electronic Stethoscopes 2018

Machine Learning and DSP Algorithms for Screening of Possibl...

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3rd international conference on Biomedical Imaging, signal processing (ICBSP)

作者： Scanlan, Jamie Li, Francis F. Umnova, Olga Rakoczy, Gyorgy Lovey, Nora Univ Salford Acoust Res Ctr Salford M5 4WT Lancs England Warrington Hosp Rheumatol Dept Warrington WA5 1QG Cheshire England

ISBN: (纸本)9781450364775

Osteoporosis is a prevalent but asymptomatic condition that affects a large population of the elderly, resulting in a high risk of fracture. Several methods have been developed and are available in general hospitals to indirectly assess the bone quality in terms of mineral material level and porosity. In this paper we describe a new method that uses a medical reflex hammer to exert testing stimuli, an electronic stethoscope to acquire impulse responses from tibia, and intelligent signal processing based on artificial neural network machine learning to determine the likelihood of osteoporosis. The proposed method makes decisions from the key components found in the time-frequency domain of impulse responses. Using two common pieces of clinical apparatus, this method might be suitable for the large population screening tests for the early diagnosis of osteoporosis, thus avoiding secondary complications. Following some discussions of the mechanism and procedure, this paper details the techniques of impulse response acquisition using a stethoscope and the subsequent signal processing and statistical machine learning algorithms for decision making. Pilot testing results achieved over 80% in detection sensitivity.

关键词： Osteoporosis diagnosis screening test impulse response signal processing stiffness resonant frequencies machine learning electronic-stethoscopes patient screening acoustic signals impulse response

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TAG: Nucleus Detection in Colorectal Adenocarcinomas Histology Images using Local Texture, Appearance, and Gradient Features

TAG: Nucleus Detection in Colorectal Adenocarcinomas Histolo...

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international conference on Imaging, signal processing and Communication (ICISPC)

作者： Jansen Keith L. Domoguen Jessie James P. Suarez Prospero C. Naval Computer Vision & Machine Intelligence Group University of the Philippines Diliman 1 Quezon City Philippines

This work considers the problem of detecting nuclei in H&E stained images. The high intra-class and inter-image variability of nuclei of the images itself calls for a more robust method that is able to handle this high variability. Recently, deep learning has become the most popular method in computer vision with many systems achieving state-of-the-art results for a wide array tasks. However, as a trade-off, deep learning methods require tons of data and computational resources that some may not posses. Thus, this work proposes a traditional computer vision pipeline along with TAG, a local feature-based approach that combines texture, appearance, and gradient features which is used in the task of pixel-per-pixel prediction in the virtue of semantic segmentation. Based from the results, the method was shown to be effective despite the challenges of high variability in the dataset.

关键词： Feature extraction Training Computer vision Task analysis Shape Image color analysis Deep learning

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