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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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Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active learning 23

Accelerating the Training of Convolutional Neural Networks f...

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23rd IEEE international conference on intelligent Transportation Systems (ITSC)

作者： Chen, Weitao Salay, Rick Sedwards, Sean Abdelzad, Vahdat Czarnecki, Krzysztof Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781728141497

Semantic segmentation is an important perception function for automated driving (AD), but training a deep neural network for the task using supervised learning requires expensive manual labelling. Active learning (AL) addresses this challenge by automatically querying and selecting a subset of the dataset to label with the aim to iteratively improve the model performance while minimizing labelling costs. This paper presents a systematic study of deep AL for semantic segmentation and offers three contributions. First, we compare six different state-of-the-art querying methods, including uncertainty-estimate, Bayesian, and out-of-distribution methods. Our comparison uses the state-of-the-art image segmentation architecture DeepLab on the Cityscapes dataset. Our results demonstrate subtle differences between the querying methods, which we analyze and explain. We show that the differences are nevertheless robust by reproducing them on architecture-independent randomly generated data. Second, we propose a novel way to aggregate the output of a query, by counting the number of pixels having acquisition values above a certain threshold. Our method outperforms the standard averaging approach. Finally, we demonstrate that our findings remain consistent for whole images and image crops.

关键词： Semantic Segmentation

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Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images 23rd

Renal Cell Carcinoma Detection and Subtyping with Minimal Po...

引用

23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Gao, Zeyu Puttapirat, Pargorn Shi, Jiangbo Li, Chen School of Computer Science and Technology Xian Jiaotong University XianShaanxi710049 China National Engineering Lab for Big Data Analytics Xian Jiaotong University XianShaanxi710049 China

ISBN: (纸本)9783030597214

Cancerous region detection and subtyping in whole-slide images (WSIs) are fundamental for renal cell carcinoma (RCC) diagnosis. The main challenge in the development of automated RCC diagnostic systems is the lack of large-scale datasets with precise annotations. In this paper, we propose a framework that employs a semi-supervised learning (SSL) method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based (Min-Point) annotation. The predicted results are efficiently utilized by a hybrid loss training strategy in a classification model for subtyping. The annotator only needs to mark a few cancerous and non-cancerous points in each WSI. The experiments on three significant subtypes of RCC proved that the performance of the cancerous region detector trained with the Min-Point annotated dataset is comparable to the classifiers trained on the dataset with full cancerous region delineation. In subtyping, the proposed model outperforms the model trained with only whole-slide diagnostic labels by 12% in terms of the testing f1-score. We believe that our "detect then classify" schema combined with the Min-Point annotation would set a standard for developing intelligent systems with similar challenges. © 2020, Springer Nature Switzerland AG.

关键词： intelligent systems

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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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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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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...

引用

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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A Sim2Real Deep learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird’s Eye View

A Sim2Real Deep Learning Approach for the Transformation of ...

引用

international conference on intelligent Transportation

作者： Lennart Reiher Bastian Lampe Lutz Eckstein Institute for Automotive Engineering (ika) RWTH Aachen University Aachen Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Accurate environment perception is essential for automated driving. When using monocular cameras, the distance estimation of elements in the environment poses a major challenge. Distances can be more easily estimated when the camera perspective is transformed to a bird's eye view (BEV). For flat surfaces, Inverse Perspective Mapping (IPM) can accurately transform images to a BEV. Three-dimensional objects such as vehicles and vulnerable road users are distorted by this transformation making it difficult to estimate their position relative to the sensor. This paper describes a methodology to obtain a corrected 360° BEV image given images from multiple vehicle-mounted cameras. The corrected BEV image is segmented into semantic classes and includes a prediction of occluded areas. The neural network approach does not rely on manually labeled data, but is trained on a synthetic dataset in such a way that it generalizes well to real-world data. By using semantically segmented images as input, we reduce the reality gap between simulated and real-world data and are able to show that our method can be successfully applied in the real world. Extensive experiments conducted on the synthetic data demonstrate the superiority of our approach compared to IPM. Source code and datasets are available at https://***/ika-rwth-aachen/Cam2BEV.

关键词： Cameras Image segmentation Neural networks Task analysis Semantics Roads Vehicle dynamics

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Machine-learning Models for Software Quality: a Compromise Between Performance and Intelligibility

Machine-Learning Models for Software Quality: a Compromise B...

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23rd IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Lounis, Hakim Gayed, Tamer Fares Boukadoum, Mounir Univ Quebec Dept Informat UQAM Montreal PQ H3C 3P8 Canada

ISBN: (纸本)9780769545967

Building powerful machine-learning assessment models is an important achievement of empirical software engineering research, but it is not the only one. Intelligibility of such models is also needed, especially, in a domain, software engineering, where exploration and knowledge capture is still a challenge. Several algorithms, belonging to various machine-learning approaches, are selected and run on software data collected from medium size applications. Some of these approaches produce models with very high quantitative performances;others give interpretable, intelligible, and "glass-box" models that are very complementary. We consider that the integration of both, in automated decision-making systems for assessing software product quality, is desirable to reach a compromise between performance and intelligibility.

关键词： machine-learning software product quality assessment models metrics maintainability reusability

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data-Driven Prognosis Applied to Complex Vacuum Pumping Systems

Data-Driven Prognosis Applied to Complex Vacuum Pumping Syst...

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23rd international conference on Industrial, engineering and Other Applications of Applied intelligent Systems

作者： Martin, Florent Meger, Nicolas Gahehet, Sylvie Becourt, Nicolas Univ Savoie Polytech Savoie LISTIC Domaine Univ BP80439 F-74944 Annecy Le Vieux France Alcatel Vacuum technol F-74009 Annecy Le Vieux France

ISBN: (纸本)9783642130212

This paper presents a method to address system prognosis It also details a successful application to complex vacuum pumping systems The proposed approach relies on an automated data-driven learning process as opposed to hand-built models that at e based on human expertise Mote precisely using historical vibratory data we first model the behavior of a system by extracting a given type of episode rules, namely First. Local Maximum episode lutes (FLM-rules) A subset, of the extracted FLM-rules is then selected in older to further predict pumping system failures in a datastream context;The results that, we got for production data are very encouraging as we predict failures with a good tune scale precision We are now deploying our solution for a customer of the semi-conductor market

关键词： data mining episode rules predictive maintenance prognosis vibratory data

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