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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 729 条记录，以下是671-680 订阅

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Random Forests Based Recognition of the Clinical Labels Using Brain MRI Scans 3

Random Forests Based Recognition of the Clinical Labels Usin...

引用

3rd international conference on Frontiers of signal processing (ICFSP)

作者： Demirhan, Ayse Gazi Univ Fac Technol Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)9781538610381

There has been a great interest in the systems that predict clinical labels from the brain images automatically for the last decade since it is a very important task that helps clinicians for decision making. In this study, clinical labels of the structural brain magnetic resonance (MR) images are predicted automatically using the random forests ensemble method. Morphological measurements like volume and thickness that exhibit details of the brain structures are used as input. Structural T1-weighted MR images of the patients with a relevant clinical phenotype are used in this study. Training images are obtained from the 150 patients and the system is tested using the images of the 100 patients. 5-fold cross validation is used for the training, determining the hyperparameters of the random forests and performance evaluation. Accuracy, the area under receiver operator characteristic curves, specificity and sensitivity are used as the performance metrics of the proposed system. Results obtained from the experiments proved that random forests can be used successfully for the identification of the clinical labels using the structural brain MR images.

关键词： brain MRI scans image classification machine learning random forests clinical prediction

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The Real-time Implementation for the Parking Line Departure Warning System

The Real-time Implementation for the Parking Line Departure ...

引用

IEEE international conference on Intelligent Transportation Engineering (ICITE)

作者： Dongkyu Lee Jae-Seol Lee Sinjae Lee Seok-Cheol Kee Smart Car Research Center Chungbuk National University Cheongju Korea

The PLDWS (Parking Line Departure Warning System) is one of the important ADAS (Advanced Driving Assistance System) functions when a driver recognizes the parking situations. We proposed the new real-time PLDWS algorithm and its real-time implementation based on the NVIDIA Jetson TX2, which is an embedded board environment. We showed the processing speed that is applicable to actual production vehicle and the same performance result as PC environment. A wide-angle camera is mounted on the front, rear, left and right sides of the vehicle, AVM (Around View Monitoring) images are obtained by synthesizing one Top-View image using four camera input images that have been calibrated. Considering that the visible range of the AVM system varies depending on the mounting position of the vehicle, the actual distance of the AVM image is measured and designated as ROI (Region of Interest) to the front and rear, right and left 2 meters from the vehicle. The whole ROI was partitioned into 3×3 grid regions, and the algorithm determined whether the parking line is crossed for each partitioned region by trained deep learning classifier, and finally a warning signal generated for the system. We also discussed the issue of porting to NVIDIA Jetson TX2 board to enable implementation of the parking line departure warning system into the vehicle.

关键词： machine learning Graphics processing units Monitoring Cameras Real-time systems Data models Alarm systems

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Sparse Extreme learning machine Using Privileged Information for Classification 1

引用

3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Mukeshimana, Michele Ban, Xiaojuan Karani, Nelson Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

In human learning process, teachers play an important role of transferring knowledge and providing some additional information for better understanding. In machine learning, the role of the teacher has been mostly ignored. Introduction of a new learning paradigm named learning Using Privileged Information (LUPI) includes the elements of human teaching in machine learning. This paper proposes a learning method using privileged information based on a Sparse Extreme learning machine (ELM). Our method aims to improve the classification performance by reducing the testing error. Experimental results show improvements in training accuracy and testing error reduction over the classical methods in classification.

关键词： Extreme learning machine (ELM) Sparse ELM learning Using Privileged Information (LUPI) Support Vector machine (SVM) machine learning

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Researching on Multiple machine learning for Anomaly Detection

Researching on Multiple Machine Learning for Anomaly Detecti...

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2018 3rd international conference on Communication,Image and signal processing（CCISP 2018）

作者： Yuanyuan Sun Yongming Wang Lili Guo Zhongsong Ma Shan Jin Huiping Wang School of Cyber Security University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization

Firstly, we introduce intrusion detection system and anomaly detection. And then we do some research on machine learning techniques for anomaly detection by network dataset NSL-KDD. The machine learning algorithms such as J48, Random forest, SVM, Vote, Stacking are selected. Random Forest, Vote and stacking are ensemble learning methods. We try to test and verify performance of multiple machine learning methods on a 20 per cent NSLKDD dataset by experiment. The experiment data has two parts. First, the 20 per cent NSLKDD dataset is classified into normal and anomaly. Second, the feature of attack type is added to the 20 per cent NSL-KDD dataset, and then a new dataset is generated. It is classified into normal and other four classes of attack. The experiment is accomplished by WEKA. The result is compared on the basis of typical indexes and confusion matrix. At last,we can draw a conclusion that an appropriate ensemble classifier can achieve better classification performance than a single classifier for anomaly detection.

关键词： KDD Stacking Researching on Multiple machine learning for Anomaly Detection

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A Novel Prediction Scheme for Hot Rolled Strip Thickness Based on Extreme learning machine 1

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3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Xie, Yonghong Liu, Jingyu Zhang, Dezheng Luo, Xiong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

In order to predict the hot-rolled strip thickness, two extreme learning machine (ELM)-based thickness modeling algorithms based on clustering and differential evolution algorithm are proposed in this paper. These two kinds of modeling methods are used to predict the thickness, and the experimental results are compared with the standard ELM. The final results show that the two models proposed in this paper are better than the standard ELM model, and these two kinds of modeling methods can be selected according to different production conditions.

关键词： Extreme learning machine (ELM) Differential evolution Thickness prediction

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Mobile Data Traffic Prediction Based on Empirical Mode Decomposition and Kernel Extreme learning machine 1

引用

3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Xie, Wenxu Xiao, Wendong Wu, Chunhong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

In view of the nonlinear and non-stationary characteristics of mobile data traffic, this paper proposes a mobile data traffic prediction model based on empirical mode decomposition (EMD) and kernel extreme learning machine (KELM). It uses EMD algorithm to decompose the mobile data traffic to obtain a series of Intrinsic Mode Function (IMF) with distinct frequency component. KELM is proposed for modeling and prediction for each IMF, based on which the mobile data traffic can be predicted by aggregating the predicted results of all KELM. Real mobile data traffic is used to verify the feasibility and effectiveness of the proposed method in this paper.

关键词： Mobile data traffic prediction Empirical mode decomposition Kernel extreme learning machine

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Extreme learning machine Based Modified Deep Auto-Encoder Network Classifier Algorithm 1

引用

3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Cao, Ruimin Wang, Fengli Hao, Lina Northeastern Univ Sch Mech Engn & Automat Shenyang 126 Liaoning Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

Aiming to solving the problem of slow training speed and learning efficiency existed in the deep auto-encoder network, this paper puts forward a new kind of modified deep auto-encoder network model based on extreme learning machine (ELM-MDAE). Through training the deep auto-encoder networks with the training method of extreme learning machine, the classification accuracy and training time of ELM-MDAE are compared with traditional deep auto-encoder network utilizing the rolling bearing fault vibration dataset released by Case Western Reserve University in United States. Experiments turn out to be that the average diagnostic accuracy rate could reach to 98.42%, and the average training time is 3.70 s with the method established in this paper. Therefore, ELM-MDAE possesses a better classification ability and fewer training time.

关键词： ELM-MDAE Extreme learning machine Auto-encoder network Rapid training speed

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learning Radio Maps for UAV-aided Wireless Networks: A Segmented Regression Approach

Learning Radio Maps for UAV-aided Wireless Networks: A Segme...

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IEEE international conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Chen, Junting Yatnalli, Uday Gesbert, David EURECOM Commun Syst Dept Sophia Antipolis France Tech Univ Munich Inst Commun Engn Munich Germany

ISBN: (纸本)9781467389990

This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.

关键词： Wireless communication Buildings Gain Maximum likelihood estimation signal processing algorithms Urban areas Shadow mapping

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Bayesian learning Based Multiuser Detection for M2M Communications with Time-Varying User Activities

Bayesian Learning Based Multiuser Detection for M2M Communic...

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IEEE international conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Zhang, Xiaoxu Liang, Ying-Chang Fang, Jun UESTC Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

ISBN: (纸本)9781467389990

machine-to-machine (M2M) communication plays a significant role in supporting Internet of Thing (IoT). This paper is concerned about multiuser detection (MUD) for massive M2M supported by Low-Activity Code Division Multiple Access (LA-CDMA). In previous work, maximum likelihood (ML) and maximum a posterior probability (MAP) detectors have been developed for such system. The ML detector has exponential complexity, while the MAP detector requires perfect knowledge of user activity factor. In practice, the user activity factor may not be known and could change from time to time. To design MUD detectors addressing these problems, in this paper, we formulate multiple measurement vector (MMV) model for uplink LA-CDMA system with time-varying user activities. Since the transmitted signals have block sparse structure, we introduce the pattern coupled spare Bayesian learning (PCSBL) by using the neighbour coherence of each transmitted signal, which effectively solves the user activity factor unknown problem. Furthermore, we embed the generalized approximate message passing (GAMP) to PCSBL and develop a novel algorithm, called generalized approximate message passing pattern coupled sparse Bayesian learning (GAMP-PCSBL). The GAMP-PCSBL does not require activity factor either, and greatly reduces the computational complexity. Simulation results have shown that the proposed algorithms have superior recovery performance than the conventional algorithms.

关键词： Multiuser detection signal processing algorithms Detectors machine-to-machine communications Uplink Approximation algorithms Bayes methods

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Real-Time Terrain Classification for Rescue Robot Based on Extreme learning machine 1

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3rd international conference on Cognitive Systems and Information processing (ICCSIP)

作者： Zhong, Yuhua Xiao, Junhao Lu, Huimin Zhang, Hui Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (数字)9789811052309

ISBN: (纸本)9789811052309;9789811052293

Full autonomous robots in urban search and rescue (USAR) have to deal with complex terrains. The real-time recognition of terrains in front could effectively improve the ability of pass for rescue robots. This paper presents a real-time terrain classification system by using a 3D LIDAR on a custom designed rescue robot. Firstly, the LIDAR state estimation and point cloud registration are running in parallel to extract the test lane region. Secondly, normal aligned radial feature (NARF) is extracted and downscaled by a distance based weighting method. Finally, an extreme learning machine (ELM) classifier is designed to recognize the types of terrains. Experimental results demonstrate the effectiveness of the proposed system.

关键词： USAR RoboCup Real-time ELM Terrain classification

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