检索结果-内蒙古大学图书馆

Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints

引用

SENSORS 2024年第22期24卷 7323页

作者： Ge, Maoning Ohtani, Kento Ding, Ming Niu, Yingjie Zhang, Yuxiao Takeda, Kazuya Nagoya Univ Grad Sch Informat Furo ChoChikusa Ward Nagoya 4648601 Japan Zhejiang Fubang Technol Inc Ningbo R&D Campus Block A Ningbo 315048 Peoples R China RoboSense Technol Co Ltd 701 Block B800 Naxian Rd Pudong 200131 Shanghai Peoples R China Nagoya Univ Tier IV Inc Open Innovat Ctr 1-3Mei-eki 1 ChomeNakamura Ward Nagoya 4506610 Japan

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a conditional variational autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE5 of 1.26 and minFDE5 of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics.

关键词： multi-agent trajectory prediction multimodal learning conditional variational autoencoder Gaussian Mixture Model autonomous driving

来源：评论

学校读者我要写书评

暂无评论

Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering

引用

ENERGY 2024年 302卷

作者： Yang, Mao Guo, Yunfeng Fan, Fulin Huang, Tao Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Univ Strathclyde Inst Energy & Environm Glasgow G1 1XW Scotland Politecn Torino DOE I-10129 Turin Italy

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the NRMSE by 3.82 % and NMAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system.

关键词： GAF-SAE feature integration conditional variational autoencoder Improved clustering distance Weighted double-constraint mechanism NWP wind speed correction Two-stage calibration

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection method based on temporal spatial information enhancement

引用

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035410-035410页

作者： Wang, Lin Wang, Xiangjun Li, Mingyang Liu, Feng Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Key Lab MOEMS Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Sch Precis Instrument & Optoelect Engn Tianjin 300072 Peoples R China

The proposed Anomaly Detection Method Based on Temporal Spatial Information Enhancement addresses the limitations of unsupervised techniques in detecting abnormal events beyond boundaries and limited samples. It incorporates a Serial Depth Separable Residual Block (Serial Block) as the backbone for predicting future frame. Additionally, a DenseReserve Subsample Module (DRSM) facilitates feature scale scaling, and a U-shaped Pyramid Attention Module (UPAM) guides feature fusion and enhances spatial details. During the prediction stage, reconstructed optical flow information aids in distinguishing abnormal and normal event features, with the abnormal score determined by a weighted fusion of optical flow reconstruction error and prediction error for future frames. Experimental results demonstrate the method's outstanding performance, achieving area under the curve metrics of 99.7%, 92.1%, and 78.3% on UCSD Ped2, CUHK Avenue, and ShahanghaiTech datasets, respectively. This method offers significant advancements in detecting anomalous events in complex and multi-scene surveillance videos, with improved operational speed and reduced parameters.

关键词： visual anomaly detection unsupervised paradigm conditional variational autoencoder optical flow reconstruct

来源：评论

学校读者我要写书评

暂无评论

Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering

引用

WOOD MATERIAL SCIENCE & ENGINEERING 2024年第5期19卷 1004-1014页

作者： Wang, Yuan Wang, Zhi-Gang He, Yi-Hao Avramidis, Stavros Beijing Forestry Univ Sch Technol Beijing Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing Peoples R China Beijing Forestry Univ Joint Int Res Inst Wood Nondestruct Testing & Eval Beijing Peoples R China Univ British Columbia Fac Forestry Dept Wood Sci Vancouver BC Canada Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing 100083 Peoples R China Beijing Forestry Univ Joint Int Res Inst Wood Nondestruct Testing & Eval Beijing 100083 Peoples R China

In order to address the problem that existing methods combining spectral data and machine learning for wood species identification rely on labeled samples, this study introduces unsupervised learning into the field of wood identification. It proposes a novel wood-identification model called DCVAE (deep conditional variational autoencoder)-PLCAE (pseudo-label convolutional autoencoders). Terahertz time-domain spectra of wood samples at breast height of five broadleaf and five coniferous species were obtained (40 samples of each species of wood, 400 in total). A conditional variational autoencoder was applied to augment the terahertz spectroscopy dataset. Subsequently, a pseudo-label-guided deep clustering model was developed to extract more discriminative deep features. The model was compared with three traditional clustering algorithms and four deep clustering methods. Clustering experiments and visualization results show that the comprehensive clustering performance of DCVAE-PLCAE is better than the other comparative algorithms and that the extracted low-dimensional features has a more straightforward structure. The algorithm in this study can solve the problems of fewer labeled samples and the difficulty of extracting discriminative features by traditional clustering algorithms.

关键词： Wood identification terahertz time domain spectroscopy conditional variational autoencoder pseudo-label

来源：评论

学校读者我要写书评

暂无评论

Deep neural network-based reduced-order modeling of ion-surface interactions combined with molecular dynamics simulation

引用

JOURNAL OF PHYSICS D-APPLIED PHYSICS 2023年第38期56卷 384005-384005页

作者： Kim, Byungjo Bae, Jinkyu Jeong, Hyunhak Hahn, Seung Ho Yoo, Suyoung Nam, Sang Ki Samsung Elect Co Ltd Mechatron Res Suwon 18448 Gyeonggi Do South Korea

With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma-material interactions-particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion-surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion-surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion-surface interactions. In the ROM framework, a conditional variational autoencoder (AE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as AE, sparse AE, contractive AE, denoising AE, and variational AE. From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle >80 & DEG;), diffuse reflection behavior was observed, which can substa

关键词： plasma-material interaction molecular dynamics deep learning reduced-order modeling conditional variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Application of conditional generative model for sonic log estimation considering measurement uncertainty

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 196卷 108028-108028页

作者： Jeong, Jina Park, Eungyu Emelyanova, Irina Pervukhina, Marina Esteban, Lionel Yun, Seong-Taek Kyungpook Natl Univ Dept Geol Daegu South Korea Commonwealth Sci & Ind Res Org CSIRO Energy Perth WA Australia Korea Univ Earth & Environm Sci 145 Anam Ro Seoul 02841 South Korea

Well-log data is a cost-effective means to characterize the petrophysical properties of a geological formation. Among the data, compressional- and shear-slowness (DTC and DTS, respectively) are the most reliable and have been widely applied in the interpretations. However, the availability of DTS data tends to be limited because of its high acquisition cost. This study proposes a method to reproduce or reconstruct the DTS data using other well-log data, such as gamma ray, neutron porosity, bulk density, and DTC. The developed method is based on the conditional variational autoencoder (CVAE) and effectively considers uncertainty associated with the variability of the measured data. The performance of this developed method is validated by applying the well-log data acquired from Satyr-5 and Callihoe-1 wells in the Northern Carnarvon Basin, Western Australia, and the prediction accuracy of the developed method is compared to recently developed data-driven methods (i.e., long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM)). The results reveal that the developed method produces a better DTS estimation than LSTM and bi-LSTM. Furthermore, the effectiveness of the proposed method remains unaltered regardless of whether the data contain a specific trend over the depth or amount of training data are insufficient. As a further application of the developed method, an uncertainty relative to DTS estimation is quantitatively obtained from Monte-Carlo estimation, which uses a trained probability model of the developed method. Sensitivity analysis reveals the high effectiveness of DTC in improving the performance of the CVAE method. From our results, we can conclude that the proposed CVAE-based method is an effective tool for improving the efficiency and accuracy of DTS estimation.

关键词： Well-log estimation Long short-term memory (LSTM) Bi-direction LSTM (Bi-LSTM) conditional variational autoencoder Probabilistic estimation Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

引用

JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第1期43卷 15-15页

作者： Wang, Shaohua Tang, Lihua Dou, Yinling Li, Zhaoyu Aw, Kean C. Univ Auckland Dept Mech & Mechatron Engn 5 Grafton Rd Auckland 1010 New Zealand Chengdu Vocat & Tech Coll Ind Chengdu 610031 Peoples R China China Railway Eryuan Engn Grp Co Ltd Inst Sci & Technol Chengdu 610031 Peoples R China

In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.

关键词： Rail track Damage identification Vibration-based image representation Data fusion Recurrence plot conditional variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Toward Sub-Surface Pore Prediction Capabilities for Laser Powder Bed Fusion Using Data Science

引用

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2021年第7期143卷 071016页

作者： Ertay, Deniz Sera Kamyab, Shima Vlasea, Mihaela Azimifar, Zohreh Thanh Ma Rogalsky, Allan D. Fieguth, Paul Univ Waterloo Dept Mech & Mechatron Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Shiraz Univ Dept Comp Sci & Engn & Informat Technol Molasadra AveEngn Bldg 2 Shiraz 71557138 Iran

Achieving defect-free parts is traditionally challenging in laser powder bed fusion (LPBF). The mechanical properties of additively manufactured parts are highly affected by their density;as such, research in defect detection and pore prediction has gained significant interest. The process parameters, the powder characteristics, and the process environment conditions play an important role in defect occurrence. Moreover, the laser scan path affects density, especially at scan path discontinuities. In this work, the complex interaction between the process parameters and the scan path on the occurrence of subsurface pores is investigated. In the data preparation step, a synthetic data set is generated to model the melt pool morphology along the scan path. A secondary data set containing the pore space of the resulting parts is obtained via X-ray computed tomography (CT) and is registered with the synthetic data set. Machine learning models, namely, a conditional variational autoencoder (CVAE) and a Convolutional Neural Network (CNN), are then trained based on the input features to predict pore occurrence. The performance evaluation of both CNN and CVAE models on synthetic data indicates that the scan path and process parameters can be utilized in predicting pore locations. Quantitative results show that employing offline CT images a priori in training the CVAE, without the need to have CT information in the test phase, leads the CVAE model to superior performance over the CNN.

关键词： additive manufacturing pore space analysis X-ray computed tomography deep neural networks conditional variational autoencoder advanced materials and processing laser processes modeling and simulation process planning

来源：评论

学校读者我要写书评

暂无评论

Generative population synthesis for joint household and individual characteristics

引用

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2022年 96卷

作者： Aemmer, Zack MacKenzie, Don Univ Washington Dept Civil & Environm Engn 201 More HallBox 352700 Seattle WA 98195 USA

Household surveys provide immense value in the fields of transportation and urban planning. However, even the most well-funded surveying agencies rely on sampling methods to estimate the nature of the true population, and the collected microdata is frequently aggregated, or limited in volume and detail to protect the privacy of respondents. Population synthesis provides a means to scale this microdata to represent larger regions for use in microsimulation. Despite their accuracy and widespread adoption, traditional synthesis algorithms for reweighting microdata samples scale poorly with the number of variables and geographic regions being modeled, and can suffer from non-convergence with smaller sample sizes. Several generative models have been proposed to address these shortcomings, but lack features such as sub-region modeling, and the ability to simultaneously generate both individuals and households. This work proposes an extension to recent generative approaches capable of generating synthetic populations consisting of both individual and household-level variables, that uses a two-part variational autoencoder (VAE) and conditional-VAE (CVAE) to learn a distribution of latent variables in the general population, and use them to generate new samples. This can help in synthesizing smaller, traditionally under-sampled groups. This approach is benchmarked against a state of the art open source population synthesizer. In addition, the VAE/CVAE model is tested under increasingly minimal training data. Findings indicate the VAE/CVAE model creates more accurate populations, using less time than the traditional synthesizer under small to medium dimensional datasets (4-16 variables). The VAE/CVAE also performs well with few (n = 100) training samples, with diminishing returns for additional training samples.

关键词： Population synthesis conditional variational autoencoder Generative Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：