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共 3149 条记录，以下是81-90 订阅

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data-Driven Maneuver Modeling using Generative Adversarial Networks and Variational Autoencoders for Safety Validation of Highly automated Vehicles 21

Data-Driven Maneuver Modeling using Generative Adversarial N...

引用

21st IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Krajewski, Robert Moers, Tobias Nerger, Dominik Eckstein, Lutz Rhein Westfal TH Aachen Inst Automot Engn Automated Driving Dept Aachen Germany Forsch Gesell Kraftfahrwesen mbH Aachen Automated Driving Dept Aachen Germany

ISBN: (纸本)9781728103235

Scenario-based validation is a promising approach for the safety validation of highly automated driving systems. By modeling relevant driving scenarios, utilizing simulations and selecting insightful test cases, the testing effort is reduced. However, current methods can't automatically create intuitive models of the vehicle trajectories in a scenario. We propose to use unsupervised machine learning to train neural networks to solve the modeling problem. The models learn a small set of intuitive parameters without the need for labeled data and use them to generate new realistic trajectories. The neural networks, which base on the InfoGAN and beta-VAE architectures, are adapted from the image domain to the time series domain. Although our methods are generally applicable, our experiments focus on lane change maneuvers on highways. To train the networks, we use more than 5600 measured lane change trajectories extracted from the highD dataset. Our results show that the networks learn to describe lane change maneuvers by up to four intuitive parameters. Furthermore, the networks are able to map existing lane change trajectories to values of the learned parameters and generate new, previously unseen, realistic trajectories. We compare both architectures among themselves and to a polynomial model, and show respective advantages.

关键词： Trajectory Mathematical model Neural networks Safety data models Road transportation Gallium nitride

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Trading Cryptocurrency with Deep Deterministic Policy Gradients 21st

Trading Cryptocurrency with Deep Deterministic Policy Gradie...

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21st international conference on intelligent data engineering and automated learning

作者： Tummon, Evan Raja, Muhammad Adil Ryan, Conor Avaya Galway Ireland Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

ISBN: (纸本)9783030623616;9783030623623

The volatility incorporated in cryptocurrency prices makes it difficult to earn a profit through day trading. Usually, the best strategy is to buy a cryptocurrency and hold it until the price rises over a long period. This project aims to automate short term trading using Reinforcement learning (RL), predominantly using the Deep Deterministic Policy Gradient (DDPG) algorithm. The algorithm integrates with the BitMEX cryptocurrency exchange and uses Technical Indicators (TIs) to create an abundance of features. Training on these different features and using diverse environments proved to have mixed results, many of them being exceptionally interesting. The most peculiar model shows that it is possible to create a strategy that can beat a buy and hold strategy relatively effortlessly in terms of profit made.

关键词： Reinforcement learning Cryptocurrency Trading

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learning data Conditions for Resolution Improvement Using UAV data 11

Learning Data Conditions for Resolution Improvement Using UA...

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Joint 11th international conference on Soft Computing and intelligent Systems and 21st international Symposium on Advanced intelligent Systems (SCIS-ISIS)

作者： Matsui, Kai Shirai, Hikaru Kageyama, Yoichi Yokoyama, Hiroshi Akita Univ Grad Sch Engn Sci Akita Japan Akita Univ Ctr Informat Technol & Management Akita Japan

ISBN: (纸本)9781728197326

This study investigates a resolution improvement method using the visible range and its band ratio of remote sensing data. In this paper, we examined the conditions of learning data to improve the accuracy of the resolution improvement method through the band ratio of unmanned aerial vehicle data. Results obtained using the dataset with a large standard deviation had greater accuracy than those using the dataset with a small standard deviation for 72.2 % of the band ratio in the test data.

关键词： remote sensing unmanned aerial vehicle machine learning resolution improvement

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A study on Feature Values as a Speaker Identification Method 11

A Study on Feature Values as a Speaker Identification Method

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Joint 11th international conference on Soft Computing and intelligent Systems and 21st international Symposium on Advanced intelligent Systems (SCIS-ISIS)

作者： Nakamura, Etsuro Kageyama, Yoichi Shirasu, Motonari Akita Univ Grad Sch Engn Sci Akita Japan Japan Business Syst Inc Serv & Support Div Tokyo Japan

ISBN: (纸本)9781728197326

Automatic speaker assignment can enhance the efficiency of automated systems used for creating meeting minutes. Therefore, various methods of speaker identification have been widely utilized;however, in such methods, it is necessary to optimally arrange the equipment. In this paper, we propose a method for identifying speakers using an omnidirectional camera and a microphone. In this process, tens of minutes of image and voice data were used for model training, and the speaker was successfully identified. In the speaker discrimination experiments, the proposed method was able to identify the speaker with an average success rate of more than 70.0% using the lip height and product of the lip height feature and lip width feature as the lip movement features.

关键词： lip speaker identification machine learning

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Driving style Recognition based on Ride Comfort Using a Hybrid Machine learning Algorithm 21

Driving Style Recognition based on Ride Comfort Using a Hybr...

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21st IEEE international conference on intelligent Transportation Systems (ITSC)

作者： del Campo, Ines Asua, Estibalitz Martinez, Victoria Mata-Carballeira, Oscar Echanobe, Javier Univ Basque Country UPV EHU Fac Sci & Technol Dept Elect & Elect Leioa 48940 Spain

ISBN: (纸本)9781728103235

Driving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the improvement of fuel efficiency are essential goals of current research in driving style characterization. However, the comfort perspective has still hardly been investigated, despite its importance for the future of driving automation. This paper proposes a driving style classification method, focused on global comfort of the driver and the passengers, but which can also be integrated into the above safety-efficiency viewpoint. Although human comfort in vehicles is affected by different factors, the amplitude and frequency of accelerations are recognized as key signals for assessing driving comfort. The proposed DS classification approach is based on a hybrid machine learning method that combines an unsupervised clustering method with a data-driven extreme learning machine (ELM) algorithm. Hierarchical clustering is used to explore the relevance of the acceleration components in relation to ride comfort, while a single layer ELM topology is implemented to model the DS classifier. The method has been evaluated using experimental data obtained with an instrumented car equipped with in-vehicle sensors and measurement units. The obtained clustering results are consistent with comfort standard indicators, while the data driven algorithm provides encouraging results: more than 95% classification rate using unseen data.

关键词： Machine learning algorithms Acceleration Clustering algorithms Machine learning Vibrations Automobiles

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A Deep-learning Approach for the Detection of Overexposure in Automotive Camera Images 21

A Deep-Learning Approach for the Detection of Overexposure i...

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21st IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Jatzkowski, Inga Wilke, Daniel Maurer, Markus TU Braunschweig Inst Control Engn Braunschweig Germany

ISBN: (纸本)9781728103235

To bring autonomous driving onto public roads, autonomous vehicles must be able to make safe driving decisions. In order to achieve this, they need to be self-aware, meaning they have to be aware of their current capabilities at all times. One step towards self-awareness is the assessment of the quality of the available sensor data and the estimation of its impact on the processing chain. Knowing when sensor data is compromised will contribute to safer driving decisions of the vehicle. In this contribution, we present a novel, deep-learning approach for overexposure detection in camera images as one step towards sensor data quality monitoring.

关键词： Training Cameras data integrity Meteorology Robots Roads Lighting

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Energy Efficiency and Emission Testing for Connected and automated Vehicles Using Real-World Driving data 21

Energy Efficiency and Emission Testing for Connected and Aut...

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21st IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Chang, Yan Yang, Weiqing Zhao, Ding Univ Michigan Transportat Res Inst Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728103235

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is critical to developing the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits.

关键词： Testing Fuel economy standards Acceleration Unsupervised learning Couplings Transportation

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Machine learning Algorithms Implementation into Embedded Systems with Web Application User Interface 21

Machine Learning Algorithms Implementation into Embedded Sys...

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21st IEEE international conference on intelligent engineering Systems (INES)

作者： Zidek, Kamil Pitel', Jan Hosovsky, Alexander Tech Univ Kosice Dept Ind Engn & Informat Fac Mfg Technol Seat Presov Bayerova 1 Presov Slovakia

ISBN: (纸本)9781479976775

The paper presents research in usability of web technologies for implementation of machine learning and clustering algorithms into embedded systems. The research work is divided into two main parts. The first part is devoted to designing backend system with fast C++ application for learning execution model. The second part of application is frontend based web application with PHP and AJAX to provide interface for virtual laboratory access via internet. This solution is implemented and tested on selected embedded systems (Orange PI Lite, Raspberry PI3).

关键词： web application machine learning clustering virtual laboratory

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Detect students' Academic Emotions in Classroom: Measurement, Self-perception and Manifested Behaviors 25

Detect Students' Academic Emotions in Classroom: Measurement...

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25th international conference on Computers in Education (ICCE) - Technology and Innovation - Computer-Based Educational Systems for the 21st Century

作者： Liu, Yan Hu, Menghua Gu, Xiaoqing East China Normal Univ Dept Educ Informat Technol Shanghai Peoples R China

ISBN: (纸本)9789869401265

The emotions students experience in the classroom are likely to influence their learning in school. For purposes of helping student improve their learning, this study seeks to further the development of the use of automated analysis of observed data to understand students' emotions and during their learning activities. This study looks at linking behavioral data about emotions with self-report data about emotions with the intent to determine if observed behaviors are able to predict student's emotions. The ICT-supported learning Emotion Scale was used for 57 students to measure their learning emotion while learning in an ICT environment, meanwhile, classroom observations were conducted for 57 students for six weeks in 4 classrooms, paying close attention to student emotional behaviors and emotional changes. The results found that three key groups of behavioral indicators happened frequently. One of the key findings was that students who volunteered to answer questions in class were observed to have an observable "enjoy" emotion 84.79% of the time. The discussion highlighted that the bridge function of students' emotion engagement between classroom observation and questionnaire data.

关键词： Emotional engagement learning analytics Behavioral indicators Observations and questionnaire

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Deep learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review 21

Deep Learning Approaches to Aircraft Maintenance, Repair and...

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21st IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Rengasamy, Divish Morvan, Herve P. Figueredo, Grazziela P. Univ Nottingham Inst Aerosp Technol Nottingham England Univ Nottingham Adv Data Anal Ctr Sch Comp Sci Nottingham England

ISBN: (纸本)9781728103235

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maintenance, repair and overhaul (MRO). Challenges however are found when dealing with such large amounts of data. Identifying patterns, anomalies and faults disambiguation, with acceptable levels of accuracy and reliability are examples of complex problems in this area. Experiments using deep learning techniques, however, have demonstrated its usefulness in assisting on the analysis aircraft health data. The purpose of this paper therefore is to conduct a survey on deep learning architectures and their application in aircraft MRO. Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory, Convolutional Neural Networks and Deep Belief Networks. For each architecture, we review their main concepts, the types of problems to which these architectures are employed to, the type of data used and their outcomes. We also discuss how research in this area can be advanced by identifying current research gaps and outlining future research opportunities.

关键词： Aircraft Maintenance engineering Fault diagnosis Engines Aerospace electronics Temperature measurement

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