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2021 China Automation Congress, CAC 2021

作者： Ma, Xiao Liu, Yu-Hu School of Mathematics Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

ISBN: (纸本)9781665426473

This paper presents an improved automata model for traffic flow based upon models for cellular automata and traffic flow. The proposed model is concerned with the headway and multiple velocities. The proposed model enforces the stochastic rules for moving a car on the basis of traffic in front of it. Rules for updating the velocity of car relies upon the location of the car as well as front traffic and their velocities. In particular, by using numerical simulations, the model proposed in this study can reach the steady-state more quickly for random perturbations. © 2021 IEEE

关键词： Cellular automata

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ECC-NeRF: Anti-Aliasing Neural Radiance Fields with Elliptic Cone-Casting for Diverse Camera Models

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IEEE Transactions on Circuits and Systems for Video technology 2025年 F页

作者： Qin, Haidong Yang, Tao Zhou, Xiaoshi Li, Dongdong Dai, Yanran Li, Jing Northwestern Polytechnical University School of Computer Science SAIIP National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China Xidian University School of Telecommunications Engineering Xi'an710071 China

Anti-aliasing is a crucial research topic in computer graphics, which can significantly enhance the rendering quality of neural radiance fields (NeRF). Recent studies have introduced effective anti-aliasing NeRF methods, utilizing cone-casting to replace ray-casting and modeling the 3D observation area of pixels as circular cones. The cone-casting strategy has successfully reduced blurring and aliasing in novel view rendering. However, we have observed that the light cones are not standard circular cones because the camera projection model distorts them into elliptic cones of diverse sizes and shapes. This finding motivates us to model pixel light cones as anisotropic elliptic cones and propose an elliptic cone-casting-based anti-aliasing NeRF method called "ECC-NeRF». Specifically, we first derive the elliptic cone models for common pinhole, fisheye, and panoramic cameras based on their camera projection models. Then, we integrate the proposed elliptic cone-casting into two representative cone-casting-based anti-aliasing NeRF methods: Mip-NeRF and Zip-NeRF. Our experimental evaluations on multiple datasets demonstrate that our method can achieve more accurate multi-scale anisotropic representation and better novel view rendering quality with negligible additional computation cost. © 1991-2012 IEEE.

关键词： Anti-aliasing

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Study on Valid Travel Route Selection of Integrated Passenger Transportation 4th

Study on Valid Travel Route Selection of Integrated Passenge...

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4th International Conference on Smart Vehicular technology, Transportation, Communication and applications, VTCA 2021

作者： Yang, Yao Kan, Chenyan Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest JiaoTong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China

ISBN: (纸本)9789811640384

With the vigorous development of the integrated transportation network, the choice of passenger travel route is becoming more and more diversified. Therefore, it is important to build the integrated transportation network by establishing the cost function of valid travel route to measure the route that passengers may choose. Based on passenger travel demand, an optimization model of valid route selection for integrated transportation network was established based on generalized travel cost. According to the characteristics of the model, an improved BFS algorithm based on dynamic updating generalized cost function and determining valid travel route by key nodes was designed to solve the valid route. Finally, a simulation was given to verify the effectiveness of the model and algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost functions

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Optimization Principle of Freight Train Operation Plan for Shenhua Railway 5th

Optimization Principle of Freight Train Operation Plan for S...

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5th International Conference on Smart Vehicular technology, Transportation, Communication and applications, VTCA 2022

作者： Wang, Meng Liu, Qiuqi He, Wenhui Guo, Xiuyun Southwest Jiaotong University Chengdu610031 China China National Energy Group Railway Equipment Company Beijing100000 China School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest JiaoTong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Beijing China

ISBN: (纸本)9789819908479

At present, the freight train formation plan cannot dynamically reflect the changes in freight demand in the transportation market, which makes it difficult to guide the actual transportation production work and affects the economic benefits of enterprises. This paper takes Shenhua Heavy Duty Railway as the research object, studies the optimization technology of cargo train programming under dynamic planning type transportation organization mode, and proposes the optimization principle of dynamic cargo train programming of Shenhua Railway. By analyzing the characteristics of the cargo transportation organization of Shenhua Railway, the dynamic planning type transportation organization mode of Shenhua Railway is proposed. On the basis of the concept of the dynamic freight train operation scheme of Shenhua Railway, the feasibility of preparation optimization, influencing factors, and main principles, the idea of dynamic preparation optimization research from the perspective of synergistic consideration of heavy train operation scheme and empty train deployment scheme is also elaborated. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Railroads

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Indoor 3D Reconstruction with an Unknown Camera-Projector Pair

arXiv

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arXiv 2024年

作者： Qi, Zhaoshuai Hao, Yifeng Hu, Rui Chang, Wenyou Yang, Jiaqi Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology The College of Computer Science Northwestern Polytechnical University ShaanXi Xi'An710072 China

Structured light-based method with a camera-projector pair (CPP) plays a vital role in indoor 3D reconstruction, especially for scenes with weak textures. Previous methods usually assume known intrinsics, which are pre-calibrated from known objects, or self-calibrated from multi-view observations. It is still challenging to reliably recover CPP intrinsics from only two views without any known objects. In this paper, we provide a simple yet reliable solution. We demonstrate that, for the first time, sufficient constraints on CPP intrinsics can be derived from an unknown cuboid corner (C2), e.g. a room's corner, which is a common structure in indoor scenes. In addition, with only known camera principal point, the complex multi-variable estimation of all CPP intrinsics can be simplified to a simple univariable optimization problem, leading to reliable calibration and thus direct 3D reconstruction with unknown CPP. Extensive results have demonstrated the superiority of the proposed method over both traditional and learning-based counterparts. Furthermore, the proposed method also demonstrates impressive potential to solve similar tasks without active lighting, such as sparse-view structure from motion. © 2024, CC BY.

关键词： Cameras

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An Interpretable Machine Learning-Based Analysis of Vehicle Yielding During Pedestrian-Vehicle Interactions at Unsignalized Intersections

An Interpretable Machine Learning-Based Analysis of Vehicle ...

引用

International Conference on Intelligent Transportation

作者： Ruiqi Wang Ang Ji Tao Li Zhanbo Sun Zhijian Fu National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China

The paper introduces an interpretable machine learning technique SHAP (SHapley Additive exPlanation) to analyze the vehicle yielding behaviors during pedestrian-vehicle interactions at unsignalized intersections. The study first extracts trajectory data from drone videos and then exploits machine learning methods to construct the yielding classification model. The results indicate that Random Forest (RF) outperforms Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGBoost), achieving the best classification performance with an area under the ROC curve (AUC) of 0.934. Finally, the SHAP algorithm is fused with RF to improve the model interpretability. The analysis reveals that the distances between vehicles and pedestrians make the most significant impact on vehicle yielding behavior. Furthermore, it is found that traffic-related variables exhibit non-linear and threshold effects on vehicle yielding.

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Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

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Acta Oceanologica Sinica 2022年第7期41卷 166-179页

作者： Kai Du Yi Ma Zongchen Jiang Xiaoqing Lu Junfang Yang College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao266590China First Institute of Oceanology Ministry of Natural ResourcesQingdao266061China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin150001China National Satellite Ocean Application Service Beijing100081China College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao266580China Technology Innovation Center for Ocean Telemetry Ministry of Natural ResourcesQingdao266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072China

Accurate detection of an oil spill is of great significance for rapid response to oil spill *** images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill ***,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater ***,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and *** address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various *** the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill *** on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment *** contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss *** comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three *** verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two stu

关键词： oil spills CNN classification loss function sunglint detection

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SteadySketch: Finding Steady Flows in data Streams

SteadySketch: Finding Steady Flows in Data Streams

引用

International Workshop on Quality of Service

作者： Xiaodong Li Zhuochen Fan Haoyu Li Zheng Zhong Jiarui Guo Sheng Long Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub.

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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SketchConf: A Framework for Automatic Sketch Configuration

SketchConf: A Framework for Automatic Sketch Configuration

引用

International Conference on data engineering

作者： Ruijie Miao Fenghao Dong Yikai Zhao Yiming Zhao Yuhan Wu Kaicheng Yang Tong Yang Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github.

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