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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是651-660 订阅

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Target-Aware Prediction of Tool Usage in Sequential Repair Tasks 6th

Target-Aware Prediction of Tool Usage in Sequential Repair T...

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6th international conference on machine learning, Optimization, and Data Science, LOD 2020

作者： Nabizadeh, Nima Heckmann, Martin Kolossa, Dorothea Cognitive Signal Processing Group Ruhr University Bochum Germany Honda Research Institute Europe GmbH Offenbach Germany

ISBN: (纸本)9783030645793

Repairing a device is usually a sequential task comprising several steps, each potentially involving a different tool. learning the sequential pattern of tool usage would be helpful for various assistance scenarios, e.g. allowing a contextualized assistant to predict the next required tool in an unseen task. In this work, we examine the potential of this idea. We employ two prominent classes of sequence learning methods for modeling the tool usage, including Variable Order Markov Models (VMMs) and Recurrent Neural Networks (RNNs). We then extend these methods by also conditioning them on additional information from the repair manuals that represents the repair target. this information is present, for example, in the title of manuals. We investigate the effect of long-term dependencies and of target-awareness on the prediction performance and compare the methods in terms of accuracy and training time. the evaluation using an annotated dataset of repair manuals shows that both target-awareness and long-term dependencies have a substantial effect on the tool prediction. While the RNN has slightly more accurate predictions in most scenarios, the VMM has a lower training time and is beneficial when the prediction needs to be restricted with respect to the device category. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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A Novel Single Front Camera Based Simpler Approach for Autonomous Taxi Navigation For Smart City Deployments 6

A Novel Single Front Camera Based Simpler Approach for Auton...

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6th international conference on signal processing and Integrated Networks (SPIN)

作者： Pandey, Darpan Niwaria, Kamal RGPV RKDFIST Dept Elect & Commun Bhopal India

ISBN: (纸本)9781728113807

Intelligent public transport is an integral component of smart cities. In this paper, a simpler approach has been demonstrated for autonomous taxi navigation. A single front camera with 1920x1080 pixel resolution at 60 frames per second has been used for real-time object detection and path planning. the deep neural network has been fed with the driving input of a couple of hours. the duly trained network was then tested with 30 minutes of driving data, recorded and used separately. Two control parameters were derived viz, speed of the vehicle and the steering angle. An eight layered convolutional network with two gated recurrent and four dense layers was trained and tested on GPU based system. the factors like speed, inclination, mean squared error (MSE) were 1.79 and 2.69 percent, respectively, with 38 milli seconds of real-time response delay.

关键词： Autonomous Vehicle Deep Neural Network machine learning Road/ Lane Detection Smart City

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An Autoencoder-Based Algorithm for Fault Detection of Rotating machines, Suitable for Online learning and Standalone Applications

An Autoencoder-Based Algorithm for Fault Detection of Rotati...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Seyyed Mahdi Hosseini Maasoum Alireza Mostafavi Ali Sadighi School of mechanical engineering University of Tehran Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Development of an autoencoder-based algorithm for fault detection in rotary machines is presented in this paper. As the cornerstone of any machine learning model, feature engineering is thoroughly studied to select the best set of features for the application. then, an autoencoder architecture is employed to detect the anomalous behavior of the machine. A laboratory setup is designed and built to provide the training and test data and validate the proposed algorithm. It is also shown that the proposed method is well suited for online learning and real-time applications.

关键词： Training Time-frequency analysis machine learning algorithms Fault detection Rotating machines signal processing algorithms Labeling

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Parameterized Structured Pruning for Deep Neural Networks 6th

Parameterized Structured Pruning for Deep Neural Networks

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6th international conference on machine learning, Optimization, and Data Science, LOD 2020

作者： Schindler, Günther Roth, Wolfgang Pernkopf, Franz Fröning, Holger Institute of Computer Engineering Ruprecht Karls University Heidelberg Germany Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

ISBN: (纸本)9783030645793

As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantization and pruning are usually considered. However, unconstrained pruning usually leads to unstructured parallelism, which maps poorly to massively parallel processors, and substantially reduces the efficiency of general-purpose processors. Similar applies to quantization, which often requires dedicated hardware. We propose Parameterized Structured Pruning (PSP), a novel technique to dynamically learn the shape of DNNs through structured sparsity. PSP parameterizes structures (e.g. channel- or layer-wise) in a weight tensor and leverages weight decay to learn a clear distinction between important and unimportant structures. As a result, PSP maintains prediction performance, creates a substantial amount of sparsity that is structured and, thus, easy and efficient to map to a variety of massively parallel processors, which are mandatory for utmost compute power and energy efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Feature Extraction based Classification of Magnetic Resonance Images using machine learning 6

Feature Extraction based Classification of Magnetic Resonanc...

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6th international conference on signal processing and Integrated Networks (SPIN)

作者： Sharma, Kushaggr Sharma, Shivang Prajapat, Rahul Bhan, Anupama Goyal, Ayush Amity Univ Uttar Pradesh Amity Sch Engn & Technol Noida Uttar Pradesh India Texas A&M Univ Frank H Dotterweich Coll Engn Kingsville TX USA

ISBN: (纸本)9781728113807

Brain abnormalities are the most challenging fatality in the current scenario of health care society. Hence, accurate detection of any abnormality in the brain is highly essential for treatment planning. the clinical diagnosis of the any brain abnormality results in excess medical cost, errors and does not provide correct detection, this work analyzes the texture of images taken of the brain to find the values of various features of the images. Statistical features have been calculated of two types, first order statistics and second order statistics. the standard database has grouped the images into normal and abnormal scans and calculated features for these scans, it was determined which features are useful for categorization. the technique involves texture analysis of medical scans. GLCM and other first order statistical features are used to form a vector space of 675 features. this feature vector is subjected to Principal component analysis to find the optimal number of principal components to train SVM and KNN classifiers. Classification with success rate of 95.45 % for SVM and 77.27% for KNN is obtained for the proposed method.

关键词： Brain scans GLCM First order statistics Second order statistics KNN SVM

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Anomaly modelling in machine learning based navigation system of autonomous vehicles

Anomaly modelling in machine learning based navigation syste...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Hossein Eshraghi Babak Majidi Ali Movaghar Khatam University Tehran Iran Faculty of Computer Engineering Sharif University of Technology Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In the past few years, autonomous navigation systems are gradually introduced to the consumer vehicles and the cars using these systems are gaining popularity. the autonomous navigation systems are using machine learning and deep learning models for visual processing of complex road scenes in various scenarios. these machine learning models are trained using numerous test rides and the process of training sometimes continues as the vehicle is navigating the streets. these machine learning models are vulnerable to anomalies which can lead to various issues for the autonomous driving systems. these issues will put the safety of the vehicle in danger. In this paper, the issue of the impact of the anomalies on the deep learning models of smart vehicles is investigated and a method for anomaly modelling in the machine learning based autonomous driving systems is presented. the presented model can increase the robustness of the autonomous vehicles.

关键词： Deep learning Training Visualization Navigation Robustness Autonomous vehicles Autonomous robots

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A new Assistive Technology in Android Platform to aid Vocabulary Knowledge Acquirement in Indian Sign Language for better Reading Comprehension in L2 and Mathematical Ability 6

A new Assistive Technology in Android Platform to aid Vocabu...

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6th international conference on signal processing and Integrated Networks (SPIN)

作者： Roy, Sandipa Maiti, Arpan Kumar Ghosh, Indira Chatterjee, Indranil Ghosh, Kuntal Indian Stat Inst Ctr Soft Comp Res Cognit Syst & Cybernet Res Lab Kolkata India Indian Stat Inst Ctr Soft Comp Res Kolkata India Ali Yavar Jung Natl Inst Speech & Hearing Disabil Divyangjan Reg Ctr Kolkata India Indian Stat Inst Machine Intelligence Unit Kolkata India

ISBN: (纸本)9781728113807

A new app on android platform has been proposed and designed to help speech and hearing impaired persons to develop vocabulary knowledge in breadth as well as depth in Indian Sign Language (ISL) through the learning of relevant synonyms and antonyms and mapped into semantic connotation via sign inputs to recognize the symbols in absence of sound through some visual clues (Sign Video). the vocabulary acquirement scheme in ISL is chosen in such a way that the understanding capability and logical sense in the second language (L2) will grow automatically. Finally, the android app in different regional languages (English with ten regional languages) used in India has been implemented, thus introducing a mobile App based Indian Sign Language Dictionary with synonyms, antonyms, presently in Bengali, along with different sentence making examples. this app is so aimed that the intellectual ability, writing skill and mathematical ability of the speech and hearing impaired can flourish in tandem with learning of normal students.

关键词： ISL Vocabulary Knowledge Reading Comprehension Synonym-Antonym Mathematical ability

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Applicability Domains of machine learning in Next Generation Radio Access Networks 6

Applicability Domains of Machine Learning in Next Generation...

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6th Annual conference on Computational Science and Computational Intelligence (CSCI)

作者： Ferrus, R. Sallent, O. Perez-Romero, J. Agusti, R. Univ Politecn Catalunya UPC Dept Signal Theory & Commun Barcelona Spain

ISBN: (纸本)9781728155845

the Radio Access Network (RAN) is envisaged to undergo a significant transformation in the context of 5G and beyond mobile communications systems. One of the driving forces behind this transformation is the applicability of machine learning (ML) techniques. Taking as a reference the high-level architecture for a next generation RAN proposed by the Open RAN Alliance, this paper identifies the applicability domains where ML techniques can play a relevant role. For each domain, namely radio physical layer processing, Medium Access Control (MAC) scheduling, near-real time Radio Resource Management (RRM), RAN data analytics and RAN operational automation, the paper discusses the specific functionalities that can benefit from the application of ML and analyses the key considerations and challenges that need to be addressed when developing ML-based solutions, given the particular characteristics of the mobile radio environment.

关键词： Next Generation Radio Access Network machine learning Radio Resource Management RAN Management

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Expression of Concern for: Breast Cancer Prediction in machine learning Techniques using Blockchain

Expression of Concern for: Breast Cancer Prediction in Machi...

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IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： R Anand D Aneeshsa Manisha Srinidhi M R.K.V thanuja Department of Information Technology KCG College Technology Chennai India

ISBN: (数字)9781665425544

ISBN: (纸本)9781665425551

Breast cancer (BC) is one all around the huge average dangerous enhancements among women around the globe, watching out for by a long shot the greater part of latest disease cases and harm related passings per all things considered evaluations, making it a genuine public awfulness in the flow society. Lately, AI has been thoroughly used in locale and achieved positive execution. Our exploration gives a absolute manual for affectability appraisal of model cutoff focuses concerning execution in district of carcinoma organizes by anticipating end inside the condition of dataset credits. BC is that the by and large standard frightful headway among women besides the subsequent driving clarification for defilement passing. Early finding of underhandedness is major. the model got by an intrusive methodology is in like manner acceptably digitized and used for computationally based reasonable. Using AI systems for resolute can on a very basic level revive and for a goliath increase can make the fascinating everything thought about more moderate. the assessment of dataset by guiltless baysien check to a few information looks like, variable ID, uni-variate evaluation, bi-variate and multi-variate examination, missing worth solutions, etc the key goal is to sharp appraisal model to dismantle carcinoma seasons of patients. Moreover, talk about the introduction from the given clinical office dataset with evaluation request report and see the disorder network. the data endorsing, data cleaning/planning and data portrayal are finished with everything considered given dataset. Henceforth, planned AI appraisal want to engineering data from need and therefore the outcome shows that the authority of the forthcoming AI rejects activity is stood and best exactness from accuracy, anamnesis and F1grade

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machine learning Based Monitoring of the Pneumatic Actuators' Behavior through signal processing Using Real-World Data Set 6th

Machine Learning Based Monitoring of the Pneumatic Actuators...

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6th international conference on Future Data and Security Engineering (FDSE)

作者： Kovacs, Tibor Ko, Andrea Corvinus Univ Budapest Fovam Ter 8 Budapest Hungary

ISBN: (纸本)9783030356538;9783030356521

Application of machine learning in smart manufacturing could enrich condition-based maintenance, enabling to monitor more complex signals describing the behavior of an equipment. Predicting equipment malfunction or failure could not only reduce manufacturing cost but could also improve product quality and supply reliability, aspects equally important these days. this research describes a machine learning based method to diagnose the condition of pneumatic actuators by processing complex signals from this type of equipment. It is using real-world data from a Hungarian multinational manufacturer of electrical components. the "Manufacturing Data Life Cycle" conceptual framework by Tao et al. [1] was followed for data processing, including data cleaning, pre-processing and classification by hierarchical clustering. the method was able to identify those signal patterns that indicate abnormal behavior of the equipment. Data volume was a challenge for this work, overcome by using parallel and GPU execution of computations.

关键词： machine learning Big data processing Clustering Condition-based maintenance Fault detection signal pattern identification

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