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Multi-static Active Sonar Target Recognition Method Based on Bionic signal

Multi-static Active Sonar Target Recognition Method Based on...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jiheng Liu Zemin Zhou Xinwu Zeng College of Meteorology and Oceanography National University of Defense Technology Changsha China

In modern sonar systems, automatic recognition of underwater targets has always been one of the key technologies in research. In recent years, classification and recognition methods based on machine learning have been widely used in underwater acoustic field where good results have been achieved. Compared with monostatic active sonar, multi-static active sonar can simultaneously acquire the forward, lateral, and backscattering information of the target, and can obtain more accurate and stable target recognition result. Furthermore, the transmit waveform of active sonar effort the performance in complex ocean environment. As all known, the signals transmitted by cetaceans have the characteristics of strong anti-jamming ability, high positioning accuracy, et al. Accordingly, the performance of multi-static active sonar target recognition based on bionic signal is investigated in this paper. Besides, the machine learning methods are applied to the recognition of echo signals, so that further good results and conclusions are obtained.

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Network traffic classification based on class weight based K-NN classifier (CWK-NN) 2

Network traffic classification based on class weight based K...

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2nd international conference on Big Data and Cyber-Security Intelligence, BDCSIntell 2019

作者： Hijazi, Mohamad Khalife, Jawad Verdejo, Jesus Diaz Al-Ghor, Hussein School of Arts and Sciences Lebanese International University Nabatieh Lebanon School of Arts and Sciences Lebanese International University Beirut Lebanon School of IT and Telecom. Eng University of Granada Granada Spain Faculty of Technology Lebanese University Saida Lebanon

— Network traffic identification is the first and most important step in network management and security. Numerous methods introduced by researchers. One solution depends on processing both packet header and payload, which is costly from both time and processing aspects. Another solution depends on the flow of statistical information such as packet header length, flow duration. Blind classifiers are not accurate yet very fast and do not violate privacy. machine learning fills the gap between accuracy and time by using the blind classifier method and comparing the results with grand truth then adapting and increasing the accuracy. K-NN is used widely for its effectiveness and simplicity. However, a major drawback of K-NN is its dependency on the training set, being a lazy classification algorithm with no classification model to build. In this work, we aim first at assessing the KNN algorithm in traffic classification. Then we introduce a new deficiency, related to the training samples distribution in the n-dimensional space we measure and propose an enhancement for K-NN adapting to the new problem and outperforming native K-NN classifier. We weight the classes, not the instance, based on the intersections of class clusters in the dataset. Finally, we propose a new Class Weight based K-NN Classifier (CWKNN), an enhanced K-NN algorithm that can easily adapt to the newly explored training set deficiency. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Classification (of information)

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Short-Term Traffic Flow Prediction of Airspace Sectors Based on Multiple Time Series learning Mechanism 2nd

Short-Term Traffic Flow Prediction of Airspace Sectors Based...

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2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

作者： Zhang, Zhaoning Liu, Kexuan Lu, Fei Li, Wenya Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9783030190859

Firstly, by analyzing the original radar data of the aircraft in the airspace system, the historical operation information of each sector is extracted, and the traffic flow correlation between different routes of the same sector is considered. According to the characteristics of busy sector traffic flow data, a multi-dimensional data model of traffic flow with multiple related routes in the sector is constructed. Secondly, based on the data model, a traffic flow forecasting algorithm based on multi-time series machine learning is proposed. The main core idea of the algorithm is to use the time series clustering method to reduce the dimensionality of multi-dimensional traffic flow data, and then introduce the machine learning method for concurrent training. The training result obtains the optimal classifier group through competition. Finally, the multi-optimal machine learning integrated prediction method is designed to predict traffic flow. Taking the typical busy sector in China as an example, the proposed prediction method is verified. The research results show that the prediction results are better than the traditional single time series machine learning method, and the stability of the prediction results is good, which can fully reflect the dynamics and uncertainty of short-term traffic flow between sectors in each airspace, in line with the actual situation of air traffic. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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Prioritization of responsive maintenance tasks via machine learning-based inference 2

Prioritization of responsive maintenance tasks via machine l...

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2nd international conference on Smart Infrastructure and Construction: Driving Data-Informed Decision-Making, ICSIC 2019

作者： Konstantinou, E. Parlikad, A.K. Wong, A. Broom, C. Institute of Manufacturing University of Cambridge United Kingdom RedBite Solutions United Kingdom

ISBN: (纸本)9780727764669

Maintenance task prioritization is essential for allocating resources. It is estimated that almost 1/3 of the maintenance cost is wasted to unnecessary activities. Task prioritization is based on risk assessment that takes into account the probability of failure and the criticality of asset (or consequence of failure). The criticality analysis is defined by the asset owner based on several parameters, among them safety, downtime cost, productivity, whilst the probability of failure is determined based on deterioration models, regular manual inspections, or sensors. The criticality of assets varies significantly between organizations, due to differences between their key performance indicators and maintenance objectives. Currently, the quantitative evaluation of the criticality of assets is a very complicated procedure for organisations. It depends on elaborate weighted score methods and extensive data collection efforts. However, the data required are not always available. This paper proposes an innovative method that exploits the advances in mobile communications, social networking, Internet of Things and machine learning to address this shortcoming. This approach brings building elements and assets online using asset tags with an online 'asset profile' linked to it. Users of assets are able to scan these tags using a mobile phone app to not only see the information about those assets, but also enter 'comments' describing issues and problems on the profiles. Natural language processing (NLP) is then applied to these comments to estimate the criticality of assets. The proposed method is validated with historical data provided by the Estate Management, of the University of Cambridge. © international conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making.

关键词： learning algorithms

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Deep reinforcement learning with vehicle heterogeneity based traffic light control for intelligent transportation system 2

Deep reinforcement learning with vehicle heterogeneity based...

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2nd IEEE international conference on Industrial Internet Cloud, ICII 2019

作者： Kumar, Neetesh Rahman, Syed Shameerur ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9781728129778

Existing vehicle traffic light signal control system suffers from many issues such as long waiting time, waste of fuel consumption and increase in carbon emission. This causes the lot of stress to the drivers and delay in arrivals of emergency vehicles and other vehicles including priority vehicles. The above mentioned issues can be limited to an extend through the replacement of the existing inefficient (fixed) traffic control system with an efficient dynamic traffic light control system. The recent advancements in sensor technology enable us to capture the real-time traffic information which can be used to dynamically control the traffic light duration. Existing works either split the traffic signal into fixed duration or just extract very little real-time traffic data which might be inefficient in most of the traffic scenarios. Hence, in this paper, we proposed a deep reinforcement learning model with vehicle heterogeneity (DLVH) for traffic control system. The DLVH model can dynamically change the traffic signal phase and duration based on the traffic information. We have evaluated DLVH via realistic simulation on Gwalior city map of India using an open-source simulator i.e., Simulation of Urban Mobility (SUMO) which proves the effectiveness of the model. © 2019 IEEE.

关键词： Traffic signals

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Interpreting deep learning models for entity resolution: An experience report using LIME 2

Interpreting deep learning models for entity resolution: An ...

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2nd international Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2019 - In conjunction with the 2019 ACM SIGMOD/PODS conference

作者： Di Cicco, Vincenzo Firmani, Donatella Koudas, Nick Merialdo, Paolo Srivastava, Divesh Università Roma Tre Italy University of Toronto Canada ATandT Labs - Research United States

ISBN: (纸本)9781450368025

Entity Resolution (ER) seeks to understand which records refer to the same entity (e.g., matching products sold on multiple websites). The sheer number of ways humans represent and misrepresent information about real-world entities makes ER a challenging problem. Deep learning (DL) has provided impressive results in the field of natural language processing, thus recent works started exploring DL approaches to the ER problem, with encouraging results. However, we are still far from understanding why and when these approaches work in the ER setting. We are developing a methodology, Mojito, to produce explainable interpretations of the output of DL models for the ER task. Our methodology is based on LIME, a popular tool for producing prediction explanations for generic classification tasks. In this paper we report our first experiences in interpreting recent DL models for the ER task. Our results demonstrate the importance of explanations in the DL space, and suggest that, when assessing performance of DL algorithms for ER, accuracy alone may not be sufficient to demonstrate generality and reproducibility in a production environment. © 2019 ACM.

关键词： Lime

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machine learning based Reduced Kernel PCA for Nonlinear Process Monitoring

Machine Learning based Reduced Kernel PCA for Nonlinear Proc...

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2019 international conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Dhibi, Khaled Fezai, Radhia Mansouri, Majdi Kouadri, Abdelmalek Bouzrara, Kais Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

Process monitoring is an essential part of industrial systems. It requires higher product quality and safety operations. Therefore, a nonlinear data-driven approach based reduced KPCA (RKPCA) for statistical monitoring of industrial processes is developed. RKPCA is a novel machine learning tool which merges dimensionality reduction and supervised learning. The use of classical KPCA for modeling and monitoring purposes can impose a high computational load when a large number of measurements are recorded. The main idea of the proposed RKPCA approach is to reduce the number of observations (samples) in the data matrix using the Euclidean distance between samples as dissimilarity metric so that only one observation is kept in case of redundancy. The Tennessee Eastman Process (TEP) is used to evaluate the fault detection abilities of the proposed RKPCA technique. The performance of the proposed method is evaluated and compared to the classical KPCA in terms of false alarms rates (FAR), missed detection rates (MDR) and computation times (CT). © 2019 IEEE.

关键词： Fault detection

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The Epistle to Cangrande Through the Lens of Computational Authorship Verification 2nd

The Epistle to Cangrande Through the Lens of Computational A...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and processing, ICIAP 2019

作者： Corbara, Silvia Moreo, Alejandro Sebastiani, Fabrizio Tavoni, Mirko Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa56124 Italy Dipartimento di Filologia Letteratura e Linguistica Università di Pisa Pisa56126 Italy

ISBN: (纸本)9783030307530

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. © 2019, Springer Nature Switzerland AG.

关键词： machine learning

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

2nd International Conference on Recent Trends in Image Proce...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

ISBN: (纸本)9789811391804

The proceedings contain 173 papers. The special focus in this conference is on Recent Trends in Image processing and Pattern Recognition. The topics include: Execution and Performance Evaluation of Cognitive and Expressive Event on a robotic Arm;multiple Decompositions-Based Blind Watermarking Scheme for Color Images;skeleton Joint Difference Maps for 3D Action Recognition with Convolutional Neural Networks;Novel Quality Metric for Image Super Resolution Algorithms - Super Resolution Entropy Metric (SREM);shape Descriptor Based on Centroid with Chord Lengths for Image Retrieval;learning Deep Feature Representation for Face Spoofing;ExNET: Deep Neural Network for Exercise Pose Detection;background Subtraction and Kalman Filter Algorithm for Object Tracking;A Blind Color Image Watermarking Using BRISK Features and Contourlet Transform;let Vehicles Talk with Images for Smart Vehicular Communication System;gray Level Face Recognition Using Spatial Features;application of Gabor Filter for Monitoring Wear of Single Point Cutting Tool;performance Analysis of Log-Gabor Based Multimodal Systems and Its Fusion Techniques;Temporal Super-Pixel Based Convolutional Neural Network (TS-CNN) for Human Activity Recognition in Unconstrained Videos;Content Based Video Retrieval Using SURF, BRISK and HARRIS Features for Query-by-image;An Empirical Study: ELM in Face Matching;optimal Selection of Bands for Hyperspectral Images Using Spectral Clustering;Classification of Natural Flower Videos Through Sequential Keyframe Selection Using SIFT and DCNN;Hyperspectral Remote Sensing Image Analysis with SMACC and PPI Algorithms for Endmember Extraction;real Time Hand Gesture Recognition for Differently-Abled Using Deep learning;a Novel Foreground Segmentation Method Using Convolutional Neural Network;image Fusion Technique Using Gaussian Pyramid.

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Detection of Sarcasm from Consumer Sentiments on Social Media About Luxury Brands 2nd

Detection of Sarcasm from Consumer Sentiments on Social Medi...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Haripriya, V. Patil, Poornima G. Department of MCA Jain Deemed-to-be University Bangalore India Department of MCA Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9789811391866

Social media sites act as a platform for customers to express their opinions/sentiments on brands and products. The opinion of the customers in social media in case of luxury brands plays a great role in improving the sales by building a better brand strategy. Most of the existing analysis used by the luxury brand industry ignores the importance of sarcasm analysis. A common type of sarcasm that is given in the form of opinion is positive sentiments, which contain a negative meaning. This paper studies the scope of Lexicon based approach, K-means and Naïve Bayes for analyzing the sarcastic opinion and analyzing the impact of these algorithms in recognition of sarcasm, which has a negative context for analyzing the luxury, brand data. © 2019, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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