- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - A Fast Point Cloud Segmentation Algorithm Based on Region Growth
摘要： Point cloud segmentation is a key prerequisite for object classification recognition. We propose a fast region growing algorithm by using the neighborhood search, filter sampling, Euclidean clustering and region growth. Segmentation experiment on point cloud data in indoor environment demonstrated that segmentation accuracy and efficiency were improved by the proposed algorithm.
关键词： regional growth,Euclidean clustering,point cloud segmentation
更新于2025-09-16 10:30:52
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[IEEE 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Atlanta, GA, USA (2019.7.14-2019.7.17)] 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Tropical Cyclone Maximum Wind Estimation from Infrared Satellite Data with Integrated Convolutional Neural Networks
摘要： Tropical cyclone (TC) maximum wind is an important parameter for estimating TC risks such as wind potential damage and storm surge. Previous work has shown that the estimation of TC maximum wind through a series of empirical rules based on the cloud characteristics shown in the satellite cloud image. Deep learning like convolutional neural networks (CNNs) has this ability of extracting and understanding these cloud features like the eye, the spiral rainbands that closely associated with its maximum wind. However, CNNs are used for object recognition and classification, CNS has less application in regression. We proposed an integrated architecture based on Convolutional Neural Network for the estimation of the TC maximum wind with higher accuracy. More specifically, it includes input layer, convolutional layers, activation functions and pooling layers for training and capturing non-linear relationships between cloud image and its wind, and a fully connection for the estimation task. We evaluate the state of the art for regression between infrared image and its TC maximum wind, discussing the necessity of different components. It demonstrates an improvement on the ability to estimate the TC intensity.
关键词： infrared cloud image,Convolutional Neural Networks (CNNs),TC Maximum wind
更新于2025-09-16 10:30:52
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Automated Method for Detection of Missing Road Point Regions in Mobile Laser Scanning Data
摘要： The paper proposes a method supported by MATLAB for detection and measurement of missing point regions (MPR) which may cause severe road information loss in mobile laser scanning (MLS) point clouds. First, the scan-angle thresholds are used to segment the road area for MPR detection. Second, the segmented part is mapped onto a binary image with a pixel size of ε through rasterization. Then, MPR featuring connected 1-pixels are identi?ed and measured via image processing techniques. Finally, the parameters regarding MPR in the image space are reparametrized in relation to the vehicle path recorded in MLS data for a better understanding of MPR properties on the geodetic plane. Tests on two MLS datasets show that the output of the proposed approach can e?ectively detect and assess MPR in the dataset. The ε parameter exerts a substantial in?uence on the performance of the method, and it is recommended that its value should be optimized for accurate MPR detections.
关键词： occlusion,image processing,missing points,point cloud,mobile laser scanning
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Baseband Unit Aggregation Based on Deep Reinforcement Learning in Cloud Radio Access Networks
摘要： We propose a deep reinforcement learning based baseband unit aggregation policy. The proposed policy is able to guarantee users’ quality of service while keeping BBU pool energy-efficient. Simulation results show that up to 80% less migration traffic can be achieved compared with benchmark heuristics with only 11% higher power consumption.
关键词： Machine Learning,Cloud RAN,BBU Aggregation
更新于2025-09-16 10:30:52
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[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - The Weld Extraction Algorithm for Robotic Arc Welding Based on 3D Laser Sensor
摘要： A weld extraction algorithm combining 2D image and 3D point cloud data is proposed in this paper, based on 3D laser sensor and a six-DOF industrial robot. Firstly, 3D point cloud data and 2D depth image can be obtained through the 3D laser sensor. Secondly, the weld feature points can be extracted while the weld can be accurately positioned, using 2D image processing method. Thirdly, the 3D weld information can be extracted by 2D weld mapping to 3D space through certain rules. Finally, the feasibility of the proposed algorithm is verified by experiments. The results strongly prove that the algorithm can be used to obtain the 3D weld information of type 1 weld in the bicycle frame, which completely meets the needs of industrial production.
关键词： 2D image processing,3D point cloud,Bicycle frame,Weld extraction
更新于2025-09-16 10:30:52
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Evaluation of Factors, Influencing the Accuracy of the Digital Model, Obtained by Laser Scanning
摘要： Measurements in terrestrial laser scanning (TLS) are not perfect and are subject to errors caused by various factors affecting the quality of the capture process and the resulting final product. Careful consideration of all these factors and errors provides a good basis for assessing the quality of the data and the information received. The accuracy of the 3D model obtained from laser scanning is influenced by the density of the measurements and the modelling methods. 3D modelling algorithms allow accuracy to be improved, but modelling software cannot solve all the problems, and it is impossible to achieve high quality 3D modelling without taking into account the factors that affect the accuracy of the measurements. The investigation of error sources in TLS measurements is rather complicated due to the large number of influencing factors that are interconnected. In addition to angular and longitudinal measurements, most scanning systems also offer a measure of the intensity of the reflected signal. Because the TLS is a non-reflective geodetic technology, it means that the measurement results are highly dependent on the reflectivity of the materials. The energy of the reflected signal depends on the following physical factors: object material properties, surface colour of the object, surface temperature, surface humidity, illumination. From the experimental studies it has been confirmed that the illumination and humidity of the scanned surfaces have a significant impact on their reflecting ability and the density of the received point cloud. Evaluation of digital model accuracy is made with а plane approximation and comparison with control points. Areas with different point densities were created in order to analyse the accuracy of 3D model and to determine the optimum scanning density.
关键词： accuracy,illumination,humidity,terrestrial laser scanning,TLS,point cloud,3D model,reflectivity
更新于2025-09-16 10:30:52
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Highly Sensitive, Selective and Portable Sensor Probe using Germanium-Doped Photosensitive Optical Fiber for Ascorbic Acid Detection
摘要： Infrastructure as a service (IaaS) allows users to rent resources from the Cloud to meet their various computing requirements. The pay-as-you-use model, however, poses a nontrivial technical challenge to the IaaS cloud service providers: how to fast provision a large number of virtual machines (VMs) to meet users’ dynamic computing requests? We address this challenge with VMThunder, a new VM provisioning tool, which downloads data blockson demand during the VM booting process and speeds up VM image streaming by strategically integrating peer-to-peer (P2P) streaming techniques with enhanced optimization schemes such as transfer on demand, cache on read, snapshot on local, and relay on cache. In particular, VMThunder stores the original images in a share storage and in the meantime it adopts a tree-based P2P streaming scheme so that common image blocks are cached and reused across the nodes in the cluster. We implement VMThunder in CentOS Linux and thoroughly test its performance. Comprehensive experimental results show that VMThunder outperforms the state-of-the-art VM provisioning methods, with respect to scalability, latency, and VM runtime I/O performance.
关键词： IaaS,image streaming,Cloud computing,virtual machine provisioning
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Development and Mass Production of Bifacial Q.ANTUM p-Cz PERC Cells
摘要： Cloud service providers are typically faced with three significant problems when running their cloud data centers, i.e., rising electricity bills, growing carbon footprints, and unexpected power outages. To mitigate these issues, running cloud data centers in smart microgrids (SMGs) is a good choice, since SMGs can enhance the energy efficiency, sustainability, and reliability of electrical services. Thus, in this paper, we investigate the problem of energy management for cloud data centers in SMGs. To be specific, we would minimize the time average expected energy cost (including electricity bill, battery depreciation cost, the total generation cost of conventional generators, and revenue loss due to the unfinished workloads) with the consideration of three practical factors, i.e., the ramping constraints of backup generators, the charging and discharging efficiency parameters of batteries, and two kinds of data center workloads. A stochastic programming is formulated by integrating the constraints associated with workload allocation, electricity buying/selling, battery management, backup generators, and power balancing. To solve the stochastic programming problem, an online algorithm is designed, and the algorithmic performance is analyzed. Simulation results show the advantages of the designed algorithm over other baselines.
关键词： energy cost,uncertainty,smart microgrids,Cloud data centers
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Luminescence of Polyamide-6 ?± and ?3 forms (Invited)
摘要： This paper focuses on energy efficiency aspects and related benefits of radio-access-network-as-a-service (RANaaS) implementation (using commodity hardware) as architectural evolution of LTE-advanced networks toward 5G infrastructure. RANaaS is a novel concept introduced recently, which enables the partial centralization of RAN functionalities depending on the actual needs as well as on network characteristics. In the view of future definition of 5G systems, this cloud-based design is an important solution in terms of efficient usage of network resources. The aim of this paper is to give a vision of the advantages of the RANaaS, to present its benefits in terms of energy efficiency and to propose a consistent system-level power model as a reference for assessing innovative functionalities toward 5G systems. The incremental benefits through the years are also discussed in perspective, by considering technological evolution of IT platforms and the increasing matching between their capabilities and the need for progressive virtualization of RAN functionalities. The description is complemented by an exemplary evaluation in terms of energy efficiency, analyzing the achievable gains associated with the RANaaS paradigm.
关键词： 5G,LTE-advanced,RAN-as-a-service,Energy efficiency,power model,radio access networks,cloud-RAN,wireless communication
更新于2025-09-16 10:30:52
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A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter
摘要： Diameter at breast height (DBH) is one of the most important parameter in forestry. With increasing use of terrestrial and airborne laser scanning in forestry, new exceeding possibilities to directly derive DBH emerge. In particular, high resolution point clouds from laser scanners on board unmanned aerial systems (UAS) are becoming available over forest areas. In this case study, DBH estimation from a UAS point cloud based on modeling the relevant part of the tree stem with a cylinder, is analyzed with respect to accuracy and completeness. As reference, manually measured DBHs and DBHs from terrestrial laser scanning point clouds are used for comparison. We demonstrate that accuracy and completeness of the cylinder fit are depending on the stem diameter. Stems with DBH > 20 cm feature almost 100% successful reconstruction with relative differences to the reference DBH of 9% (DBH 20–30 cm) down to 1.8% for DBH > 40 cm.
关键词： forest inventory,cylinder,diameter at breast height,forestry,point cloud,Unmanned Aerial Systems,LiDAR,DBH
更新于2025-09-16 10:30:52