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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是91-100 订阅

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Toward Kinesthetic Empathy in Developmental Disorder Prediction: A key to raise the Dance/Movement Therapy to a medical test

Toward Kinesthetic Empathy in Developmental Disorder Predict...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yukari Sakiyama Hiroharu Kawanaka Haruhiko Takase Yoshihiko Hirabayashi Atsushi Inoue Dep. Of Early Childhood Education Mukogawa Women’s University Nishinomiya Japan Graduate School of Engineering Mie University Tus Japan Graduate School of Regional Innovation Mie University Tus Japan

This paper introduces the concept of Kinesthetic Empathy (KE) as a key to attribute critical states of emotion throughout observing movement and dancing of a patient in Dance/Movement Therapy. While this concept of KE has been well recognized and indeed played important roles in profiling activities, its quantitative aspects still remain open. We are now looking into the quantitative aspects of KE so that this may be a key to make the current profiling activities more consistently measurable for its further advancement, such as a medical testing procedure to predict developmental disorder and digital transformation – the IT assisted profiling and beyond. We attempt this introduction by (1) describing the current qualitative aspects of KE through examples, and (2) introducing our current digitization project that aims at some data Sciences and Machine learning, and (3) raising DMT to a medical test for developmental disorder prediction at an early stage such as infants and toddlers.

关键词： Pediatrics Digital transformation Current measurement Medical treatment Machine learning Medical tests Testing

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How Can Machine learning Models Be Used for Subjective Assessment of Safety and Comfort: Application on Free Lane Change Maneuver

How Can Machine Learning Models Be Used for Subjective Asses...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Batuhan Durukal Mert Eren Ömer Çetin Egemen Karabıyık Namık Zengin Sarp Kaya Yetkin AVL Research and Engineering Istanbul Turkey

Subjective evaluation plays a key role in autonomous driving feature validation for safety, comfort, and driving quality. As for being objective, it is also important to evaluate the autonomous features with key performance indicators (KPI) depending on physical parameters before stepping into the delivery phase. To provide better driving experience for autonomous features, calibration parameters need to be tuned carefully while considering safety and comfort. Calibration parameters can be evaluated in terms of safe, unsafe, comfortable, or uncomfortable states through questions that allow the evaluation of the passenger’s feelings during real-world testing which includes predefined scenarios and environments. In this paper, we proposed a method that performs the rating of the free lane change maneuver in terms of safety and comfort by employing the machine learning algorithms to model the passenger feedback according to the questionnaire for the subjective evaluation of the test maneuver execution. After trying several machine and deep learning regression techniques, we have shown that Extreme Gradient Boosting (XGB) regressor can be used to model drive feeling accurately for validation and calibration purposes. The constituted evaluation model can be utilized to improve quality of the autonomous driving, optimize calibration parameters and achieve user acceptance.

关键词： Deep learning Machine learning algorithms Key performance indicator Boosting data models Safety Calibration

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Modelling the relationships between local landscape features and habitat suitability of Genji-firefly Luciola cruciata using Random Forests

Modelling the relationships between local landscape features...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Tomoya Ogami Shinji Fukuda Hirofumi Kakudo Graduate School of Agriculture Tokyo University of Agriculture and Technology Tokyo Japan Division of Environmental and Agricultural Engineering Tokyo University of Agriculture and Technology Tokyo Japan Program in Architecture Civil and Environmental Engineering Faculty of Engineering and Design Kagawa University Kagawa Japan

Genji-firefly has been one of the key symbols of rural environmental conservation. The species lives underwater as larvae, in the soil when they pupate, and emerges on the ground as adults. Because the habitat conditions of larvae and adults differ greatly, the habitat requirement of Genji-firefly must be clarified over a wide range from instream to the surrounding riparian environment. In this study, we developed a habitat suitability model for Genji-firefly by relating the number of individuals to local landscape features surveyed in a small river in an agricultural watershed. For habitat modelling, Random Forests, a well-known highly predictive machine learning method, was used to model species distributions and to extract quantitative information on their ecological characteristics. As a result, a good agreement was found between observed and modelled number of individuals, although some issues still remain for the test data. We were able to extract local landscape features that characterize the habitat suitability of adult firefly. In future, we plan to deepen our habitat suitability assessment by taking into account uncertainties such as noise originated from absent data.

关键词： Uncertainty Biological system modeling Computational modeling Vegetation Forestry Predictive models Feature extraction

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Explainability of intelligent Transportation Systems using Knowledge Compilation: a Traffic Light Controller Case

Explainability of Intelligent Transportation Systems using K...

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international conference on intelligent Transportation

作者： Salomón Wollenstein-Betech Christian Muise Christos G. Cassandras Ioannis Ch. Paschalidis Yasaman Khazaeni Division of Systems Engineering Boston University Boston MA USA School of Computing Queen’s University Kingston ON Canada IBM Research AI Cambridge MA USA

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical stateaction data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.

关键词： data structures Boolean functions Computational modeling Transportation Tools Task analysis Reinforcement learning

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Automatic Generation of Road Feature Identification Models from Point Cloud data Using HD Maps

Automatic Generation of Road Feature Identification Models f...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Ryuichi Imai Kenji Nakamura Yoshinori Tsukada Noriko Aso Jin Yamamoto Faculty of Engineering and Design Hosei University Shinjuku City Tokyo Japan Faculty of Information Technology and Social Sciences Osaka University of Economics Osaka City Osaka Japan Faculty of Business Administration Setsunan University Neyagawa City Osaka Japan Dynamic Map Platform Chuo City Tokyo Japan Faculty of Engineering and Design Hosei University Graduate School Shinjuku City Tokyo Japan

A large amount of point cloud data has been measured and accumulated at various locations for Japanese public works by introducing mobile mapping systems and terrestrial laser scanners. However, since point cloud data are a huge set of points that hold position coordinates, their use is limited in the unprocessed state. For this reason, we have been proposing a method for constructing a product model that structures point cloud data by assigning the meaning of a planimetric feature to the points using a plan of completion drawing that shows the completed shape of a construction object and 3D map data with high precision for autonomous driving. However, the applicable scope of the existing method is limited to the plan of completion drawing and the already developed sections of road maps. Therefore, a method of extracting road features from point cloud data using deep learning has been proposed. However, it requires manual preparation of a huge amount and highly qualified training data. In this study, we propose a method to automatically generate training data for constructing a road feature identification model from point cloud data automatically extracted using road maps. The usefulness of the proposed method has been confirmed through demonstration experiments.

关键词： Point cloud compression Solid modeling Three-dimensional displays Shape Roads Training data Measurement by laser beam

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learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for automated Driving

Learning-Based Shape Estimation with Grid Map Patches for Re...

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international conference on intelligent Transportation

作者： Hendrik Königshof Christoph Stiller Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

3D object detection serves as a crucial basis of visual perception, motion prediction, and planning for automated driving. To apply an algorithm for this purpose, the detection of all types of road users in real-time is an essential condition. In this paper, we propose an approach that projects the 3D points of image-based bounding box proposals into so-called grid map patches. These patches are used to estimate the exact dimensions of the 3D box with the help of a lightweight CNN. The complete proposed processing chain is parallelized and implemented on a GPU. This makes our approach the fastest stereo-based 3D object detector on the KITTI benchmark while still achieving results that are within the range of the best image-based algorithms.

关键词： Three-dimensional displays Encoding Two dimensional displays Shape Object detection Estimation Training data

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Specific Energy Consumption Prediction Framework in Hydrogen Liquefaction Process Using Deep Neural Network Based on Quantum Mechanics

Specific Energy Consumption Prediction Framework in Hydrogen...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Eunseo Oh Jaeuk Jang Sangmin Park Hyunsoo Lee Industrial Engineering Kumoh National Institute of Technology Gumi South Korea

Energy supply methods are being diversified due to competition for resources due to depletion of fossil fuels and continued high oil prices. Hydrogen energy has the advantage of low carbon dioxide emission and the ability to generate a lot of energy per weight. However, gaseous hydrogen has a low hydrogen storage density per unit volume, so a liquefaction process is required. Liquid hydrogen has a lower pressure than hydrogen in gaseous state, so the risk of explosion is low and transportation cost is reduced because it is easy to transport in large quantities. Therefore, the hydrogen liquefaction process is important and can improve energy efficiency. Specific Energy Consumption (SEC), commonly used as a performance indicator in the hydrogen liquefaction process, is the sum of energy consumed in the entire process to liquefy 1 kg of hydrogen. However, since the uncertainty of the attribute values lowers the accuracy of prediction, in order to overcome this issue, this study proposes a framework for predicting the SEC value according to the attribute value using a Deep Neural Network (DNN) based on quantum mechanics. The attribute values corrected through quantum mechanics more closely simulate the attribute of the data and contribute to improving the prediction accuracy.

关键词： Deep learning Energy consumption Uncertainty Oils Hydrogen Neural networks Quantum mechanics

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Developing a data-driven Modularized Model of a Plug-in Hybrid Electric Bus (PHEB) for Connected and automated Vehicle Applications

Developing a Data-driven Modularized Model of a Plug-in Hybr...

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international conference on intelligent Transportation

作者： Zhouqiao Zhao Zhensong Wei Guoyuan Wu Matthew J. Barth Bourns College of Engineering Center for Environmental Research and Technology (CE-CERT) University of California Riverside CA USA

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Shared Electric Connected and automated Vehicles have the potential to improve transportation safety, mobility, and energy efficiency. A plug-in hybrid electric architecture is well suited for developing connected and automated vehicle (CAV) applications, allowing for vehicle dynamics management and powertrain control. In this paper, we developed a data-driven modularized modeling approach for a plug-in hybrid electric bus (PHEB), thereby allowing for a wide range of connected and automated vehicle applications. Instead of using an end-to-end learning approach to model the PHEB, our modularized modeling approach considers the physical connection of each component of PHEB, which provides various signals and dynamics of each subsystem for testing use or controller design. The plug-and-play (PnP) feature allows us to customize the bus model and update each individual module in a flexible manner. The modules include human driver behavior, energy management system, internal combustion engine, electric motor(s), transmission, and powertrain dynamics. For each module, a Long Short-term Memory (LSTM) network is utilized to learn each modules' behavior and dynamics using the data from extensive dynamometer-in-the-loop (DiL) testing.

关键词： Vehicle dynamics Torque data models Ice Mathematical model Vehicles Electric motors

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Identifying High Risk Driving Scenarios Utilizing a CNN-LSTM Analysis Approach*

Identifying High Risk Driving Scenarios Utilizing a CNN-LSTM...

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international conference on intelligent Transportation

作者： Rongiie Yu Haoan Ai Zhen Gao Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China College of Transportation Engineering Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

High risk driving scenarios are critical for the deployment of highly automated vehicles virtual test. In this study, we have proposed a deep learning method to identify high risk scenarios from the field operation test (FOT) data. The proposed method tries to overcome the shortcomings of existing relevant studies for their limited utilizations of video data and mainly based upon instant kinematic indicators, which has led to high false alarm rate issue. In this study, a combined video analysis method (Convolutional Neural Network, CNN) and temporal feature analysis model (Long Short-Term Memory, LSTM) was proposed. To be specific, we used CNN-LSTM and Convolutional Neural Networks and Long Short-Term Memory (Resnet-LSTM) to perform the classifications for high risk scenarios and non-conflict scenarios. The empirical analyses have been conducted using commercial vehicle FOT data. And the results showed that the overall model performance (AUC index) in the test set could reach 0.91 with 83% accuracy rate. Finally, the future works have been discussed from the aspects of further extractions of video data and investigations of LSTM modelling results.

关键词： Feature extraction data models data mining Accidents Logic gates Mathematical model Training

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Use Information You Have Never Observed Together: data Fusion as a Major Step Towards Realistic Test Scenarios

Use Information You Have Never Observed Together: Data Fusio...

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international conference on intelligent Transportation

作者： Maximilian Bäumler Linda Dziuba-Kaiser Zian Yin Matthias Lehmann Günther Prokop Faculty of Transport and Traffic Sciences Dresden Institute of Automobile Engineering Dresden Germany Technische Universität Dresden Dresden Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Scenario-based testing is a major pillar in the development and effectiveness assessment of automated driving systems. Thereby, test scenarios address different information layers and situations (normal driving, critical situations and accidents) by using different databases. However, the systematic combination of accident and / or normal driving databases into new synthetic databases can help to obtain scenarios that are as realistic as possible. This paper shows how statistical matching (SM) can be applied to fuse different categorial accident and traffic observation databases. Hereby, the fusion is demonstrated in two use cases, each featuring several fusion methods. In use case 1, a synthetic database was generated out of two accident data samples, whereby 78.7% of the original values could be estimated correctly by a random forest classifier. The same fusion using distance-hot-deck reproduced only 67% of the original values, but better preserved the marginal distributions. A real-world application is illustrated in use case 2, where accident data was fused with over 23,000 car trajectories at one intersection in Germany. We could show that SM is applicable to fuse categorial traffic databases. In future research, the combination of hot-deck-methods and machine learning classifiers needs to be further investigated.

关键词： databases Accidents data integration Vegetation Sociology Testing Law enforcement

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