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33rd ieee Intelligent Vehicles symposium (ieee IV)

作者： Oh, Geesung Park, Joonghoo Hwang, Kyunghun Lim, Sejoon Kookmin Univ Grad Sch Automot Engn Seoul South Korea Hyundai Motor Grp Electrificat Energy Efficiency & Drivabil Test 3 Hwaseong South Korea Kookmin Univ Coll Automot Engn Dept Automobile & IT Convergence Seoul South Korea

ISBN: (纸本)9781665488211

It is necessary to calibrate the hydraulic pressure of the shift control to develop an automatic transmission (AT), and this calibration process entails a subjective shift quality assessment by experienced engineers. An objective shift quality assessment methodology has been explored for a long time to replace the engineer. The most recent data-based assessment model has attained a nearly human-like performance. However, preparing the large number of data labels required for supervised learning of the model has limitations. This study proposes an unsupervised anomaly detection model for objective shift quality assessment to address data label shortages and high data labeling costs. The proposed anomaly detection model is trained to classify a normal shift and an abnormal shift using just normal shift data. It is possible to easily obtain many train datasets from ordinary vehicles, and data labeling is not required. On the basis of real vehicle shift data, multiple anomaly detection models composed of various deep neural networks are developed and assessed. The evaluation results show that training exclusively on normal shift data can detect abnormal shifts;the best area under receiver operating characteristic curve is 0.902.

关键词： Training Deep learning Costs Neural networks Time series analysis Supervised learning data models

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OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE SENSING BIG data PROCESSING

OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE S...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Zhang, Sheng Xue, Yong Ming, Yang Zhang, Xiaopeng Jin, Chunlin Jiang, Xingxing Zhou, Xiran China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Shandong Prov Inst Land Surveying & Mapping Jinan 250013 Shandong Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The advent of the era of Remote Sensing Big data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

关键词： workflow heterogeneous computing platform Hungarian algorithm optimal assignment

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Design of a Highway Traffic Safety Assessment System Based on Crash data Mining and Modeling 1

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6th KES International symposium on Smart Transportation Systems, KES STS 2023

作者： Li, Luo Qin, Shuolei Qi, Weiwei Guangzhou Northring Intelligent Transport Technology Co. Ltd. Guangzhou510030 China School of Civil Engineering and Transportation South China University of Technology Guangzhou510641 China

ISBN: (数字)9789819932849

ISBN: (纸本)9789819932832

With the advent of the era of traffic information technology, traffic data appears to be extremely large, and the traditional way of managing highway crashes has become difficult to adapt to the many implications of the complicated data. In this paper, we design a highway traffic safety assessment system based on crash data mining and modeling. By constructing a visualized highway traffic crash data analysis and mining platform, which is used as a basis to gather traffic crash analysis solutions and build a think tank, a safety assessment system is formed that integrates traffic crash information visualization, crash data analysis and mining technology and comprehensive solutions for crash risk prediction. The system provides crash data pre-processing solutions, crash pattern mining solutions, factor analysis solutions and crash risk prediction solutions. Based on the above solution, the basic framework and seven main functional modules of the highway traffic safety assessment system are designed and implemented. The system achieves systematic and automated data processing, pattern mining, factor analysis and risk prediction, and aims to make highway traffic safety management more efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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large SCALE FOREST PARAMETER ESTIMATION THROUGH A DEEP LEARNING-BASED FUSION OF SENTINEL-2 AND TANDEM-X data

LARGE SCALE FOREST PARAMETER ESTIMATION THROUGH A DEEP LEARN...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Carcereri, Daniel Rizzoli, Paola Ienco, Dino Bueso-Bello, Jose-Luis Gonzalez, Carolina Puliti, Stefano Bruzzone, Lorenzo German Aerosp Ctr DLR Microwaves & Radar Inst Cologne Germany Univ Trento UNITN Trento Italy Natl Res Inst Agr Food & Environm INRAE Paris France Norwegian Inst Bioecon Res NIBIO As Norway

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The estimation of forest parameters, such as canopy height model (CHM) and above ground biomass (AGB), is of utmost importance for forest monitoring, carbon-cycle modelling, disturbance analysis, resource inventorying and natural disaster prevention. In this work, we profit from the most recent advancements in deep learning research to propose a convolutional neural network (CNN) architecture for frequent forest parameter estimation at large scale. Our technique consists of a fully convolutional, multi-modal framework, which works on a single set of complementary multi-spectral and interferometric SAR data, acquired by ESA's Sentinel-2 and DLR's TanDEM-X missions, respectively. The regression performance of our framework has been tested over four tropical forest test sites in Gabon, Africa. The estimation of CHM shows promising early results when compared to state-of-the-art methods and has the advantage of requiring only a single input image pair instead of a longer time-series, as commonly done for state-of-the-art model-based techniques.

关键词： forest monitoring forest height deep learning sensor fusion Sentinel-2 TanDEM-X

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Show, Tell and Rephrase: Diverse Video Captioning via Two-Stage Progressive Training

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 7894-7905页

作者： Liu, Zhu Wang, Teng Zhang, Jinrui Zheng, Feng Jiang, Wenhao Lu, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tsinghua Univ Dept Humanities Beijing 100190 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Tencent Data Platform Shenzhen 518001 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

Describing a video using natural language is an inherently one-to-many translation task. To generate diverse captions, existing VAE-based generative models typically learn factorized latent codes via one-stage training merely from stand-alone video-caption pairs. However, such a paradigm neglects set-level relationships among captions from the same video, not fully capturing the underlying multimodality of the generative process. To overcome this shortcoming, we leverage neighbouring descriptions for the same video that are articulated with noticeable topics and language variations (i.e., paraphrases). To this end, we propose a novel progressive training method by decomposing the learning of latent variables into two stages that are topic-oriented and paraphrase-oriented, respectively. Specifically, the model learns from divergent topic sentences obtained by semantic-based clustering in the first stage. It is then trained again through paraphrases with a cluster-aware adaptive regularization, allowing more intra-cluster variations. Furthermore, we introduce an overall metric DAUM, a Diversity-Accuracy Unified Metric to consider both the precision of the generated caption set and its coverage on the reference set, which has proved to have a higher correlation with human judgment than previous precision-only metrics. Extensive experiments on three large-scale video datasets show that the proposed training strategy can achieve superior performance in terms of accuracy, diversity, and DAUM over several baselines.

关键词： Measurement Training visualization Task analysis Computer science Decoding Electronic mail Diverse video captioning conditional variational autoencoders progressive training

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Exploration and Design of Intelligent Connected Vehicle Monitoring and Supervision Platform Based on Multi-Source data 11

Exploration and Design of Intelligent Connected Vehicle Moni...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Dong, Shaoshuai Hu, Jianyao Wang, Xunji School of Automotive and Transportation Engineering Hefei University of Technology Hefei China School of Mechanical Engineering Southwest Jiaotong University Chengdu China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou China Hefei Intelligent and Connected Vehicles Innovation Center Hefei China

ISBN: (纸本)9798350333664

With the rapid advancement of Intelligent Connected Vehicle (ICV), the demand for monitoring and supervision of vehicles has become increasingly urgent. To address this need, this paper presents a design for an ICV monitoring and regulation platform that leverages multi-source data. By gathering information from on-board sensors, traffic surveillance cameras, and road monitoring devices, such as traffic participant data, vehicle information, traffic conditions and surrounding environmental data, this platform can integrate multi-source data to achieve real-time monitoring and analysis of ICV behavior, safety and operational status. It presents a comprehensive view of road conditions and the overall functioning of the ICV, helping managers to monitor ICV operations from a large to a small scale. By engaging with the ICV, this platform enables management and regulation of the ICV, thereby improving operational efficiency and safety. Additionally, it provides precise analysis and optimization suggestions for vehicle operating status, assisting management personnel in understanding vehicle performance and capabilities. The design of this platform offers a comprehensive and efficient solution for ICV monitoring and supervision, providing robust support for the advancement of Intelligent Transport Systems (ITS) and the widespread adoption of ICV. © 2023 ieee.

关键词： artificial intelligence ICV multi-source data visualization module

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LARNeXt: End-to-End Lie Algebra Residual Network for Face Recognition

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第10期45卷 11961-11976页

作者： Yang, Xiaolong Jia, Xiaohong Gong, Dihong Yan, Dong-Ming Li, Zhifeng Liu, Wei Univ Chinese Acad Sci Chinese Acad Sci Acad Math & Syst Sci Huairou 101408 Peoples R China Tencent Data Platform Shenzhen 518054 Peoples R China Chinese Acad Sci Inst Automat Key Lab Multimodal Artificial Intelligence Syst MA Beijing 101408 Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing 101408 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China

Face recognition has always been courted in computer vision and is especially amenable to situations with significant variations between frontal and profile faces. Traditional techniques make great strides either by synthesizing frontal faces from sizable datasets or by empirical pose invariant learning. In this paper, we propose a completely integrated embedded end-to-end Lie algebra residual architecture (LARNeXt) to achieve pose robust face recognition. First, we explore how the face rotation in the 3D space affects the deep feature generation process of convolutional neural networks (CNNs), and prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation. Second, on the basis of this theoretical finding, we further design three critical subnets to leverage a soft regression subnet with novel multi-fusion attention feature aggregation for efficient pose estimation, a residual subnet for decoding rotation information from input face images, and a gating subnet to learn rotation magnitude for controlling the strength of the residual component that contributes to the feature learning process. Finally, we conduct a large number of ablation experiments, and our quantitative and visualization results both corroborate the credibility of our theory and corresponding network designs. Our comprehensive experimental evaluations on frontal-profile face datasets, general unconstrained face recognition datasets, and industrial-grade tasks demonstrate that our method consistently outperforms the state-of-the-art ones.

关键词： Face recognition lie algebra pose estimation profile face

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Scaling large Vision-Language Models for Enhanced Multimodal Comprehension in Biomedical Image analysis

Scaling Large Vision-Language Models for Enhanced Multimodal...

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ieee International symposium on Biomedical Imaging

作者： Robinson Umeike Neil Getty Fangfang Xia Rick Stevens Argonne National Laboratory Lemont USA University of Alabama Tuscaloosa USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

large language models (LLMs) have demonstrated immense capabilities in understanding textual data and are increasingly being adopted to help researchers accelerate scientific discovery through knowledge extraction (information retrieval), knowledge distillation (summarizing key findings and methodologies into concise forms), and knowledge synthesis (aggregating information from multiple scientific sources to address complex queries, generate hypothesis and formulate experimental plans). However, scientific data often exists in both visual and textual modalities. Vision language models (VLMs) address this by incorporating a pretrained vision backbone for processing images and a cross-modal projector that adapts image tokens into the LLM dimensional space, thereby providing richer multimodal comprehension. Nevertheless, off-the-shelf VLMs show limited capabilities in handling domain-specific data and are prone to hallucinations. We developed intelligent assistants finetuned from LLaVA models to enhance multimodal understanding in low-dose radiation therapy (LDRT)-a benign approach used in the treatment of cancer-related illnesses. Using multilingual data from 42,673 articles, we devise complex reasoning and detailed description tasks for visual question answering (VQA) benchmarks. Our assistants, trained on 50,882 image-text pairs, demonstrate superior performance over base models as evaluated using LLM-as-a-judge approach, particularly in reducing hallucination and improving domain-specific comprehension.

关键词： visualization Analytical models Adaptation models Biological system modeling large language models Information retrieval Question answering (information retrieval) Radiation therapy Multilingual Biomedical imaging

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data Augmentation using Reverb and Noise in Deep Learning Implementation of Cough Classification

Data Augmentation using Reverb and Noise in Deep Learning Im...

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2023 ieee International symposium on Medical Measurements and Applications, MeMeA 2023

作者： Huq, Saiful Xi, Pengcheng Goubran, Rafik Valdes, Julio J. Knoefel, Frank Green, James R Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Natinoal Research Council Canada Ottawa Canada Carleton University Bruyère Research Institute Systems and Computer Engineering Ottawa Canada National Research Council Canada Digital Technologies Ottawa Canada Bruyère Research Institute Élisabeth Bruyère Hospital Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (纸本)9781665493840

The interest in automated analysis and classification of cough sounds has increased in recent years, partly due to the worldwide COVID19 pandemic. To train such classification models, a large dataset of cough sounds is needed, however, it remains challenging to find such datasets of cough sounds that have expert-labelled diagnoses of cough types. data augmentation techniques have been used to train machine learning models given such limited data. Furthermore, augmentation ensures that trained models are invariant to natural transformations of the data, measured from real environments/surroundings. This paper presents a method for classifying wet and dry coughs using a ResNet18 convolutional neural network model. Several forms of spectral data augmentation are investigated including many traditional audio data augmentation methods. A novel form of audio data augmentation is leveraged, where coughs are augmented with varying levels of reverberation and Gaussian noise, during model training. The study found that using a combination of reverb and noise augmentation provided greater improvement than either form of augmentation alone, or traditional augmentations as well, leading to an accuracy of 95%. Use of such a model that has been trained on both reverb- and noise-augmented data is recommended when classifying audio recordings, such as cough sounds, from natural environments outside of laboratory conditions. © 2023 ieee.

关键词： Classification (of information)

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Segmentation of E-Commerce data Using K-Means Clustering Algorithm

Segmentation of E-Commerce Data Using K-Means Clustering Alg...

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2023 ieee Global Conference on Information Technologies and Communications, GCITC 2023

作者： Prabhas, Vanam Lal Saini, Madan Mohith, Chinni Kumar, Ratish Tripathi, Bharat Apex Institute of Technology Chandigarh University Department of Cse Punjab India

ISBN: (纸本)9798350308167

In this era of technology and development, every day large amount of data is being produced, collected and transferred. This data can be analysed by various machine learning algorithms and used as per requirement and interest of people. Companies can increase their productivity according to the interest of customers hence customer segmentation is being needed. It allows businesses to know and understand the needs and motivations of customer, so they provide a great experience and also launches the required and needful products. Businesses may better satisfy the demands of their consumers by customising their products, services, and marketing initiatives by knowing the distinct wants and preferences of various client groups. As a result, customers are more satisfied and loyal, which eventually boosts sales and income. This paper presents the analysis of E-commerce data and segment them on the basis of RFM variables. The customers are clustered on the basis of demographics and behavioural aspects, such as age, gender, marital status, etc. Elbow method was used for finding optimal number of clusters and total 3 clusters are formed on the basis of recency, frequency, and monetary. Now Business people can apply various marketing strategies for their interests. © 2023 ieee.

关键词： Customer Segmentation data visualization Davies Bouldin Score K-Means Clustering RFM Model

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