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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Maximum Bayes Boundary-Ness Training For Pattern Classification 19

Maximum Bayes Boundary-Ness Training For Pattern Classificat...

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proceedings of the 2019 2nd international conference on signal processing and machine learning

作者： Masahiro Senda David Ha Hideyuki Watanabe Shigeru Katagiri Miho Ohsaki Graduate School of Science and Engineering Doshisha University Kyoto Japan Advanced Telecommunications Research Institute International Kyoto Japan

ISBN: (纸本)9781450372213

The ultimate goal of pattern classifier parameter training is to achieve its optimal status (value) that produces Bayes error or a corresponding Bayes boundary. To realize this goal without unrealistically long training repetitions and strict parameter assumptions, the Bayes Boundary-ness-based Selection (BBS) method was recently proposed and its effectiveness was clearly demonstrated. However, the BBS method remains cumbersome because it consists of two stages: the first generates many candidate sets of trained parameters by carefully controlling the training hyperparameters so that those candidate sets can include the optimal target parameter set; the second stage selects an optimal set from candidate sets. To resolve the BBS method's burden, we propose a new one-stage training method that directly optimizes a given classifier parameter set by maximizing its Bayes boundary-ness or increasing its accuracy during Bayes error estimation. We experimentally evaluate our proposed method in terms of its accuracy of Bayes error estimation over four synthetic or real-life datasets. Our experimental results clearly show that it successfully overcomes the drawbacks of the preceding BBS method and directly creates optimal classifier parameter status without generating too many candidate parameter sets.

关键词： Pattern recognition classification class boundary uncertainty

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A Comparative Study of Hand Gestures Recognition Based on MobileNetV2 and ConvNet Models 6

A Comparative Study of Hand Gestures Recognition Based on Mo...

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6th international conference on Image and signal processing and their Applications, ISPA 2019

作者： Bousbai, Khalil Merah, Mostefa University of Abd El-hamid Ibn Bdis Stud. of Elctronics and Embededd Systems Mostaganem Algeria Mostaganem Algeria

ISBN: (纸本)9781728131566

Hand gestures offer humans a natural way to interact with computers for a variety of applications. However, factors such as the complexity of hand gesture patterns, size differences, hand posture, and environmental lighting can affect the performance of hand gesture recognition algorithms. Recent advances in deep learning have greatly improved the performance of image recognition systems. In particular, the deep convolutional neural network has demonstrated superior performance in terms of image representation and classification, compared to conventional machine learning methods. This article proposes a two-technique comparison, a proposed deep-convolution neural network and transfer learning with the pre-trained model mobilnetv2 , for hand gestures recognition of American sign language. Both models are trained and tested using 1815 images segmented by colour and with black background, and its static hand gestures for five volunteers, which incorporate variations in features such as scale, lighting, and noise. The results show that,the proposed CNN model achieved a classification recognition accuracy of 98.9%. with a 2% improvement over the convolutional neural network model enriched using transfer learning techniques 97.06%. © 2019 IEEE.

关键词： Lighting

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Automated Detection of Sewer Pipe Defects Based on Cost-Sensitive Convolutional Neural Network 19

Automated Detection of Sewer Pipe Defects Based on Cost-Sens...

引用

proceedings of the 2019 2nd international conference on signal processing and machine learning

作者： Yuhan Chen Shangping Zhong Kaizhi Chen Shoulong Chen Song Zheng College of Mathematics and Computer Science Fuzhou University Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (纸本)9781450372213

Regular inspection and repair of drainage pipes is an important part of urban construction. Currently, many classification methods have been used for defect diagnosis using images inside pipelines. However, most of these classification models train the classifier with the goal of maximizing accuracy without considering the unequal error classification cost in defect diagnosis. In this study, the authors analyze the characteristics of sewer pipeline defect detection and design an automated detection framework based on the cost-sensitive deep convolutional neural network (CNN). The method makes the CNN network cost sensitive by introducing learning theories at the structural and loss levels of the network. To minimize misclassification costs, the authors propose a new auxiliary loss function Cost-Mean Loss, which allows the model to obtain the original parameters of the network to maximize the accuracy and improve the performance of the model by minimizing total misclassification costs in the learning process. Theoretical analysis shows that the new auxiliary loss function can be applied to the classification task to optimize the expected value of misclassification costs. The inspection images collected from multiple drainage pipes were used to train and test the network. Results show that after the cost-sensitive strategy was added, the defect detection rate decreased from 2.1% to 0.45%. Moreover, the model with Cost-Mean Loss has better performance than the original model.

关键词： Sewer pipe inspection Cost-sensitive learning Defect detection Deep learning Cost-Mean Loss

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Research on Spatial Conceptual Modeling of Natural Language processing Based on Deep learning Algorithms 2

Research on Spatial Conceptual Modeling of Natural Language ...

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2nd international conference on Computer Information Science and Application Technology, CISAT 2019

作者： Wang, Jiaqi Software Engineering School of Software Engineering Beihang University 100089 China

From the point of view of computer science, especially artificial intelligence, the task of natural language understanding is to establish a computer model. This computer model can give the result of understanding natural language like human. An important aspect of natural language understanding lies in how to express knowledge to the computer, how to express knowledge, and how to establish the connection and reasoning between knowledge, that is, how to apply the brain's association, reasoning and selection process to the model of language processing. The physical structure and logical structure of modern computers are very clear, but what we need is a set of feasible formal thinking mechanism to enable machines to process natural language information. Deep learning is one of the areas of machine learning that is close to AI. It is analyzed by simulating human brain learning nerves. Deep learning is derived from the study of artificial neural networks and is a structure for learning deep nonlinear networks. By presenting complex function approximations, the input data is distributed and represented, and the ability of the data samples to focus on the essential characteristics of the data set is revealed. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

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learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation learning 19

Learning How to Avoiding Obstacles for End-to-End Driving wi...

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proceedings of the 2019 2nd international conference on signal processing and machine learning

作者： Enwei Zhang Hongtu Zhou Yongchao Ding Junqiao Zhao Chen Ye The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering P. R. China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering P. R. China

ISBN: (纸本)9781450372213

Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of environment and obstacles and generation of proper behaviors. We make the use of CARLA, a simulator for autonomous driving research, and collect massive human drivers' reactions to obstacles on road subjecting to given driving commands, i.e. follow, go straight, turn left and turn right for about 6 hours. A behavior-Cloning neural network architecture is proposed with the modified loss that enlarge the effects of errors for steer, which indicates the benefit to high an accuracy. We found the data augmentation of the image is crucial to the training of the proposed network. And a reasonable limit allows avoiding unexpected stop. The experiments demonstrate 3 obstacle avoidance cases: for the same type as the training dataset, other automobile and two-wheeled vehicles. Finally, the CARLA benchmark is also tested.

关键词： imitation learning obstacle avoidance end-to-end autonomous vehicles

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Street Lamp Fault Diagnosis System Based on Extreme learning machine 2

Street Lamp Fault Diagnosis System Based on Extreme Learning...

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2nd international Symposium on Application of Materials Science and Energy Materials, SAMSE 2018

作者： Lee, Yanming Zhang, Hongyi Rosa, Jimmi School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen China School of Engineering and Computer Science Victoria University of Wellington New Zealand

In view of the construction of urban lighting system needs a lot of manpower deployment, especially for its fault diagnosis problem management. This paper proposes a fault model detection and diagnosis subsystem based on the extreme learning machine for street lamps system. The subsystem is part of the event rule response system which is based on the complex event processing technology framework. The system can handle a large amount of sensor data, perform filtering, carry out complex data processing and decision making. The experimental results show that the proposed street lamp fault diagnosis system based on extreme learning machine can diagnose the street lamp fault effectively and respond to it automatically. © 2019 Institute of Physics Publishing. All rights reserved.

关键词： Decision making

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AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving

AI Approach In Monitoring The Physical And Psychological Sta...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Sujeevan Shanmugarajah Janani Tharmaseelan Luckman Sivagnanam Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Strengthening Reconciliation Processes in Sri Lanka GIZ GmBh Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude.

关键词： Sleep machine learning Electrocardiography Feature extraction Software Automobiles Vehicles

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Pavement Crack Detection based on yolo v3 2

Pavement Crack Detection based on yolo v3

引用

2nd international conference on Safety Produce Informatization, IICSPI 2019

作者： Nie, Mingxin Wang, Cheng School of Information Engineering Wuhan University of Technology Key Laboratory of Fiber Optic Sensing Technology and Information Processing Ministry of Education Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781728145662

In recent years, with the rapid development of the economy, road construction has entered the stage of coexistence of construction and conservation. Even road maintenance has become a major aspect of road construction. The government invests a lot of money in road maintenance every year. Therefore, it's very important to detect cracks on the road surface in order to reduce the cost of maintenance. Aiming at the problems of poor real-time performance and low accuracy of traditional pavement crack detection, and using the advantages of deep learning network in target detection, a method based on yolo v3 for pavement crack detection is designed. The collected pictures are manually marked, and the network model is obtained through yolo v3 network training. Finally, the cracks are detected and verified by the obtained model. The accuracy of crack detection in this work reached 88%, and the crack detection speed was also improved compared with the traditional identification method. © 2019 IEEE.

关键词： Crack detection

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Multi-loss joint optimization for person re-identification 2

Multi-loss joint optimization for person re-identification

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Ren, Mengxue Lu, Shuhua Peoples Publ Secur Univ China Beijing 102623 Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Due to the rise of deep learning, person re-identification has become a research hotspot in computer vision. For most person re-identification algorithm, softmax function is used as loss function which could increase the distance of inter-classes, but has a bad convergence performance for the distance of intra-classes. Therefore, a person re-identification model based on multi-loss optimization is proposed by adding center loss. Center loss has the function of reducing intraclass distance, which makes up for the shortcoming of softmax loss. Two models are selected for comparative experiment to prove the effectiveness of our method. One is the re-ranking person re-identification model with k-reciprocal coding, which is named IDE_ResNet-50+ Jaccard. The other is the person re-identification model without k-reciprocal coding, which is named IDE_ResNet-50. The experiments perform on the Market-1501 dataset, and the result shows that our method has a better result than the original model, which gains an increase of 1.25% and 0.63% in mAP and rank-1 accuracy for IDE_ResNet-50+Jaccard model. For the IDE_ResNet-50 model, the accuracy of mAP and rank1 increased by 1.86% and 0.18%, respectively.

关键词： Person re-identification Center loss Multi-loss Deep learning softmax loss

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Weakly supervised segmentation from extreme points 4th

Weakly supervised segmentation from extreme points

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4th international Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the 1st international Workshop on Hardware Aware learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the 2nd international Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd international conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019

作者： Roth, Holger Zhang, Ling Yang, Dong Milletari, Fausto Xu, Ziyue Wang, Xiaosong Xu, Daguang NVIDIA Bethesda United States

ISBN: (纸本)9783030336417

Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network’s predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis. © Springer Nature Switzerland AG 2019.

关键词： Deep learning

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