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40th IEEE International conference on distributed Computing Systems (ICDCS)

作者： Liang, Rui Xu, Shengyong Huazhong Agr Univ Coll Engn Wuhan Peoples R China

ISBN: (纸本)9781728170022

In recent years, 3D reconstruction technology has developed rapidly. It is a promising field to apply 3D reconstruction technology to the measurement of plant configuration parameters. The main content of this paper is the 3D reconstruction technology for rape roots and the measurement methods for their key traits. Firstly, we set up a set of low-cost image sequence acquisition device of rape roots. We collected image data with common consumption level camera and used the method of SfM to carry out 3D reconstruction of rape roots. Then we proposed a series of algorithms to measure the surface area, volume, number of primary lateral roots and length of taproot based on the huge point cloud data obtained from 3D reconstruction. Finally, we designed a set of nondestructive measurement system for key traits of rape roots. The total volume of root, the number of primary lateral roots and the length of taproot were measured manually. Compared with the results of manual measurement, the accuracy of the main algorithm proposed in this paper is not less than 95% Our contribution is to provide a 3D reconstruction method that is easy to operate, and to provide a high-precision measurement method for the key traits of rape roots, which has an important value for quantitative analysis of rape roots phenotype.

关键词： rape roots 3D reconstruction point cloud data processing nondestructive measurement

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distributed processing of k Shortest Path Queries over Dynamic Road Networks 20

Distributed Processing of k Shortest Path Queries over Dynam...

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ACM SIGMOD International conference on Management of Data (SIGMOD)

作者： Yu, Ziqiang Yu, Xiaohui Koudas, Nick Liu, Yang Li, Yifan Chen, Yueting Yang, Dingyu Yantai Univ Yantai Peoples R China York Univ N York ON Canada Univ Toronto Toronto ON Canada Wilfrid Laurier Univ Waterloo ON Canada Alibaba Grp Hangzhou Peoples R China MNR Key Lab Urban Land Resources Monitoring & Simulat Shenzhen Peoples R China

ISBN: (纸本)9781450367356

The problem of identifying the k-shortest paths (KSPs for short) in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner (i.e., the (i + 1)th shortest path is generated based on the ith shortest path), restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

关键词： dynamic graph k-shortest paths distributed computing

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Object Pose Estimation for Robotic Grasping based on Multi-view Keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots image color analysis Robot kinematics Pose estimation Robot vision systems

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Performance-sensitive Data Distribution Method for distributed Stream processing Systems 20

Performance-sensitive Data Distribution Method for Distribut...

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4th High Performance Computing and Cluster Technologies conference, HPCCT 2020 and the 3rd International conference on Big Data and Artificial Intelligence, BDAI 2020

作者： Chen, Yanqiu Zheng, Linjiang Liu, Weining College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9781450375603

In the distributed stream processing, data skew and dynamics can result in imbalanced load distribution at downstream tasks and affect the throughput of the systems. Efficient data distribution method is urgently needed to solve the problem of imbalanced load distribution to improve system throughput. Existing researches cannot better balance the semantic correctness and the throughput of distributed stream processing systems, and it is hard to apply to distributed clusters with different node performance. In this paper, we propose a performance-sensitive data distribution method. Firstly, we propose a load balancing framework that considers the performance of nodes. Then we present a data redistribution algorithm, which aims to balance the execution delay between parallel nodes and follows the principle of "Less Migration key, Less Partition key"(LMLP). The proposed algorithm can reduce the extra cost caused by the data redistribution process and it can be applied to distributed clusters with different performance. Experimental results show that our method is better than the similar methods in terms of load imbalance degree, complete latency, routing table size, etc. And we also use real data to prove that our method can obtain higher throughput and resource utilization. © 2020 ACM.

关键词： Sensitive data

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AIS Data-Based Efficient Ship Trajectory Clustering

AIS Data-Based Efficient Ship Trajectory Clustering

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ruli Xu Ryan Wen Liu Jiangtian Nie Xianjun Deng Zehui Xiong Wenchao Jiang Han Yu Dusit Niyato School of Navigation Wuhan University of Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

High-quality ship trajectory clustering is an important fundament for learning meaningful knowledge from massive historical trajectories, which are commonly extracted from the shipborne Automatic Identification System (AIS) data. However, clustering of massive trajectory data easily suffers from two limitations, i.e., low accuracy and high computational cost. In this work, we propose to develop an efficient ship trajectory clustering method based on the virtual maritime traffic network, which is constructed using the massive AIS data. In particular, we first adopt the popular Douglas-Peucker (DP) algorithm to simplify the ship trajectories to obtain the first type of feature points, where the moving ships change the course significantly. The second type of features points, where exist the large number of ship encounters, is also obtained through estimating the ship density map. The density-based clustering method is employed to remove the unwanted outliers in these feature points, contributing to the extraction of important waypoints. The virtual maritime traffic network is then constructed using the extracted way-points. To obviously shorten the computational cost, the virtual network is finally used to cluster the massive ship trajectories by introducing the concepts of base cluster and stream cluster. Two famous metrics, i.e., Silhouette Coefficient (SC) and Davies-Bouldin Index (DBI), will be exploited to evaluate the trajectory clustering results for our method and several competing methods. Experimental results on realistic AIS data have demonstrated the superior clustering performance of our method in terms of computational efficiency, SC and DBI metrics.

关键词： Measurement Clustering methods Clustering algorithms Feature extraction Trajectory Computational efficiency Indexes

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AUGMIX: A SIMPLE DATA processing METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY 8

AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNES...

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8th International conference on Learning Representations, ICLR 2020

作者： Hendrycks, Dan Mu, Norman Cubuk, Ekin D. Zoph, Barret Gilmer, Justin Lakshminarayanan, Balaji DeepMind Google

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AUGMIX, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AUGMIX significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half. © 2020 8th International conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： image classification

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RETRACTED: image Recognition and Simulation Based on distributed Artificial Intelligence (Retracted Article)

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COMPLEXITY 2021年第1期2021卷

作者： Fan, Tao Shanghai Univ Engn Sci Dept Elect & Elect Engn Shanghai 201620 Peoples R China

This paper studies the traditional target classification and recognition algorithm based on Histogram of Oriented Gradients (HOG) feature extraction and Support Vector Machine (SVM) classification and applies this algorithm to distributed artificial intelligence image recognition. Due to the huge number of images, the general detection speed cannot meet the requirements. We have improved the HOG feature extraction algorithm. Using principal component analysis (PCA) to perform dimensionality reduction operations on HOG features and doing distributed artificial intelligence image recognition experiments, the results show that the image detection efficiency is slightly improved, and the detection speed is also improved. This article analyzes the reason for these changes because PCA mainly uses the useful feature information in HOG features. The parallelization processing of HOG features on graphics processing unit (GPU) is studied. GPU is used for high parallel and high-density calculations, and the calculation of HOG features is very complicated. Using GPU for parallelization of HOG features can make the calculation speed of HOG features improved. We use image experiments for the parallelized HOG feature algorithm. Experimental simulations show that the speed of distributed artificial intelligence image recognition is greatly improved. By analyzing the existing digital image recognition methods, an improved BP neural network algorithm is proposed. Under the premise of ensuring accuracy, the recognition speed of digital images is accelerated, the time required for recognition is reduced, real-time performance is guaranteed, and the effectiveness of the algorithm is verified.

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Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

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CIRCUITS SYSTEMS AND SIGNAL processing 2020年第2期39卷 543-570页

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Simultaneous localization and map construction (SLAM) tasks have been proven to benefit greatly from the depth information of the environment. In this paper, we first present an unsupervised end-to-end learning framework for the task of monocular depth and camera motion estimation from video sequences. The difference between our work and the existing unsupervised methods is that we not only use image reconstruction for supervising but also exploit the pose estimation method used in traditional SLAM approaches to enhance the supervised signal and add extra training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task superior to the supervised methods and outperforms the previous state-of-the-art unsupervised approach by 13.5% on KITTI dataset. For the pose estimation task, our method performs comparably to the supervised methods that use ground-truth pose data for training. Besides, our unsupervised learning framework can significantly accelerate the initialization process of the traditional ORB-SLAM system and effectively improve the accuracy of environmental mapping in strong lighting and weak texture scenes.

关键词： Monocular depth estimation Pose estimation Unsupervised learning Visual SLAM system

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Research on parallel K-Nearest Neighbor Query Optimization Method for Highway Big Data

Research on Parallel K-Nearest Neighbor Query Optimization M...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Min Li School of Computer Engineering Jingchu University of Technology Jingmen Hubei China

ISBN: (纸本)9781728190198

Because the information in the field of engineering construction involves remote sensing recognition, intelligent transportation, etc. The rapid growth of various types of information makes highway data continue to develop toward the trend of big data in terms of scale, type, and structure, showing the characteristics of multi-source heterogeneous. This article discusses efficient data retrieval and query methods for specific highway basic databases and simulated highway spatial large data sets. In order to better manage spatial objects and maintain their positional relationship with grid cells, it is proposed to combine inverted index with grid index. In order to break the dependence of the rim radius on the previous traversal results, the serial rim access K nearest neighbor (KNN) algorithm is appropriately improved, and the rim radius is updated by increasing the mesh side length, combined with multi-threading technology Achieve access to multiple rims to improve the query performance of highway big data. Test data by modifying the size, spatial grid sides and these K values, the average performance are improved.

关键词： Road transportation Transportation industry Query processing Wheels Transportation Big Data parallel processing

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Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven parallel Robot 6

Reconstructing 3-D Graphical Model Using an Under-Constraine...

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6th International conference on Signal processing and Intelligent Systems (ICSPIS)

作者： Zare, Soroush Ghanatian, Mohammad Yazdi, Mohammad Reza Hairi Masouleh, Mehdi Tale Univ Tehran Sch Mech Engn Human & Robot Interact Lab Tehran Iran Univ Tehran Sch Mech Engn Tehran Iran Univ Tehran Sch Elect & Comp Engn Human & Robot Interact Lab Tehran Iran

ISBN: (纸本)9781728186290

In this paper, a novel application of an under-constrained cable-driven parallel robot is introduced. By this application, the 3D model of any object with any size limited to the workspace of the robot can be reconstructed, which is a definite asset for creating the 3D model in such an easy and cost-efficient way despite other methods like a laser scanner. The most significant problem of an under-constrained cable-driven robot is the orientations of the end-effector which cannot be controlled regardless of its position, i.e., the orientation and position of the end-effector are not independent. In this paper, the foregoing problem was solved by using a stabilizer to capture a video in prescribed orientations. The results demonstrate that the end-effector can be controlled with high precision by a PID controller to reconstruct the 3D model of an object based on the captured videos of a camera which is mounted on the end-effector of the robot. In this paper, the object is a social robot called Sanbot that has a shiny surface, which decreases the 3D model reconstruction precision. However, the results reveal that the 3D model of the Sanbot robot has a very clear view, and the experimental results confirm the efficiency of the proposed method. This method provides an easy way to reconstruct the 3D model of an object which cannot be reconstructed by other methods.

关键词： Cable-driven parallel robot image processing 3D reconstruction

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