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Semantic Descriptor for Intelligence Services

Semantic Descriptor for Intelligence Services

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Edgar Ramos Timon Schneider Marie-J. Montpetit Ben De Meester Ericsson Research Finland Aalto University Finland Concordia University Montreal Canada IDLab - imec Ghent University Ghent Belgium

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

The exposition and discovery of intelligence especially for connected devices and autonomous systems have become an important area of the research towards an all-intelligent world. In this article, it a semantic description of functions is proposed and used to provide intelligence services mainly for networked devices. The semantic descriptors aim to provide interoperability between multiple domains' vocabularies, data models, and ontologies, so that device applications become able to deploy them autonomously once they are onboarded in the device or system platform. The proposed framework supports the discovery, onboarding, and updating of the services by providing descriptions of their execution environment, software dependencies, policies and data inputs required, as well as the outputs produced, to enable application decoupling from the AI functions.

关键词： Artificial intelligence Semantics data models Biological system modeling Interoperability Hardware Couplings

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Towards Trajectory Prediction-Based UAV Deployment in smart Transportation Systems

Towards Trajectory Prediction-Based UAV Deployment in Smart ...

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international conference on Computer Communications and Networks (ICCCN)

作者： Fan Liang Xing Liu Nuri Alperen Kose Kubra Gundogan Wei Yu Dept. of Computer Science Sam Houston State University USA Dept. of Computer and Information Sciences Towson University USA

$A$ smart transportation system (i.e., intelligent transportation system) refers to a transportation critical infrastructure system that integrates advanced technologies (e.g., networking, distributed computing, big data analytics, etc.) to improve the efficiency, safety, and sustainability of the transportation system. However, the rapid increase in the number of vehicles on roads and significant fluctuations in the flow of traffic can cause the coverage holes of Road Side Units (RSUs) and local traffic overload in smart transportation systems, which can negatively affect the performance of systems and causes accidents. To address these issues, deploying Unmanned Aerial Vehicles (UAVs) as mobile RSUs is a viable approach. Nonetheless, how to deploy UAVs to the optimal position in the smart transportation system remains an unsolved issue. This paper proposes a Vehicle Trajectory-based Dynamic UAV Deployment Algorithm (VTUDA). The VTUDA utilizes vehicle trajectory prediction information to improve the efficiency of UAV deployment. First, we deploy a distributed Seq2Seq-GRU model to the UAVs and train the model. We leverage the well-trained model to predict vehicle trajectory. VTUDA then uses the predicted information to make informed decisions on the optimal location to position the UAVs. Further-more, VTUDA considers both the condition of communication channels and energy consumption during the deployment process to ensure that UAVs are deployed to optimal positions. Our experimental results confirm that the proposed VTUDA can effectively improve the deployment of UAVs. The experimental results also demonstrate that VTUDA can significantly enhance vehicle access and communication quality between vehicles and UAVs.

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A multidimensional human-centric framework for environmental intelligence: Air pollution and noise in smart cities

A multidimensional human-centric framework for environmental...

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international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Andreas Bardoutsos Gabriel Filios Ioannis Katsidimas Thomas Krousarlis Sotiris Nikoletseas Pantelis Tzamalis Computer Engineering and Informatics Department University of Patras Patras Greece Inlecom Innovation Athens Greece

ISBN: (数字)9781728143514

ISBN: (纸本)9781728198040

For the important problem of increasing levels of air pollution and noise in urban and rural areas, we propose a holistic, multi-dimensional approach to gather, monitor and analyze heterogeneous data sources of air pollutants and noise indicators, into an integrated, intelligent computational system. Although several interesting approaches have been developed for monitoring pollution and noise, however the challenge remains for even more detailed, precise, large scale *** overcome the limitations of current systems, we envision an integrated approach to human-centric environmental intelligence, bringing together modern IoT technology and the human factor. In particular, our approach emphasizes selected behavioural and health aspects, and the complementary use of sensing technology with citizen engagement and crowdsourcing methods. The proposed system will collect diverse data from heterogeneous sources, such as mobile and static wireless sensor networks, crowdsourcing, citizen questionnaires and social media analytics, to continuously combine objective estimations with subjective perception of air quality and noise. With the use of advanced AI and Deep Learning algorithms, our system will be able to estimate air pollutants concentration and noise levels in micro-scale with adequate precision over large urban-scale environments. Furthermore, tracking of behavioral and psychological users' input, as well as personal exposure to pollution, will allow studying the impact of air quality and noise on the users' daily habits and the interplay of ambient conditions with behavioural factors, towards an active engagement of citizens in a hybrid techno-social manner. A reference architecture for the realization of this human-centric environmental intelligence approach is presented. Also, a planned implementation at the city of Patras, Greece is discussed. To the best of our knowledge, this is one of the first holistic, multifaceted approaches to a surveillance system

关键词： Air pollution Monitoring Urban areas Sensors Wireless sensor networks

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Hardware Accelerators for Edge Enabled Machine Learning

Hardware Accelerators for Edge Enabled Machine Learning

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IEEE Region 10 international conference TENCON

作者： Arjun Suresh Bhargava N Reddy CH Renu Madhavi RV College of Engineering Bengaluru India PES University Bengaluru India

ISBN: (数字)9781728184555

ISBN: (纸本)9781728184562

The proliferation of IoT devices in recent years has resulted in an exponential increase in data being transmitted over the internet. The traffic is slated for further increase in the coming years and will result in excessive network congestion and high latency. To alleviate this problem, an alternate approach needs to be considered. A prominent option would be to move the computing domain to the edge device. This option is constrained due to reduced computing, storage and power available on the edge. A novel approach combining both software and hardware solutions is required to perform analytics at the edge. This paper proposes an architecture for analysing data on the edge, combining hardware and software solutions. The proposed methodology explores machine learning algorithms for edge computing combined with the use of hardware accelerators to achieve truly intelligent edge devices. A qualitative and quantitative comparison of performance of various algorithms on CPU, GPU, FPGA platforms is carried out. A machine learning model for predicting Remaining Useful Life (RUL) for a multivariate time series dataset is developed and its deployment on the edge is discussed. The results of the experiments carried out are promising and hold potential for further research.

关键词： Servers Computational modeling Cloud computing Performance evaluation Machine learning Field programmable gate arrays Hardware

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New Method of Traffic Flow Forecasting Based on QPSO Strategy for Internet of Vehicles

New Method of Traffic Flow Forecasting Based on QPSO Strateg...

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IEEE international conference on smart Internet of Things (smartIoT)

作者： De-gan Zhang Jing-yu Du Ting Zhang Hong-rui Fan The Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China The Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin

ISBN: (数字)9781728165141

ISBN: (纸本)9781728165158

We propose a new method of traffic flow forecasting based on quantum particle swarm optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing method is applied to the quantum particle swarm method to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network can be used to obtain the required data. In addition, in order to compare the performance of the methods, a comparison study with other related methods such as QPSO-RBF is also performed. Our method can reduce prediction errors and get better and more stable prediction results.

关键词： Neural networks Simulated annealing Clustering algorithms Neurons Biological system modeling Genetics Linear programming

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

引用

14th international conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

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COSMOS smart Intersection: Edge Compute and Communications for Bird's Eye Object Tracking

COSMOS Smart Intersection: Edge Compute and Communications f...

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IEEE Annual conference on Pervasive computing and Communications Workshops (PerCom)

作者： Shiyun Yang Emily Bailey Zhengye Yang Jonatan Ostrometzky Gil Zussman Ivan Seskar Zoran Kostic Columbia University New York City Winlab Rutgers University New Jersey

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

smart-city intersections will play a crucial role in automated traffic management and improvement in pedestrian safety in cities of the future. They will (i) aggregate data from in-vehicle and infrastructure sensors; (ii) process the data by taking advantage of low-latency high-bandwidth communications, edge-cloud computing, and AI-based detection and tracking of objects; and (iii) provide intelligent feedback and input to control systems. The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) enables research on technologies supporting smart cities. In this paper, we provide results of experiments using bird's eye cameras to detect and track vehicles and pedestrians from the COSMOS pilot site. We assess the capabilities for real-time computation, and detection and tracking accuracy - by evaluating and customizing a selection of video pre-processing and deep-learning algorithms. Distinct issues that are associated with the difference in scale for bird's eye view of pedestrians vs. cars are explored and addressed: the best multiple-object tracking accuracies (MOTA) for cars are around 73.2, and around 2.8 for pedestrians. The real-time goal of 30 frames-per-second - i.e., a total of 33.3 ms of latency for object detection for vehicles will be reachable once the processing time is improved roughly by a factor of three.

关键词： Cloud computing Cameras Object detection Urban areas Training Feature extraction Sensors

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Thinking on Continuously Promoting Enterprise Cloudification in Jiangsu Province

Thinking on Continuously Promoting Enterprise Cloudification...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Dianwei Wang Ou Bai Xingxing Zhang Tao Yu General Manager’s Office China Electronics Standardization Institute Huadong Branch Suzhou Jiangsu China iSESOL Information and Technology Co. Ltd Institute of Intelligent Manufacturing Shanghai China General Manager’s Office Jiangsu CESI Technology Development Co. Ltd Suzhou Jiangsu China Software Development iSESOL Information and Technology Co. Ltd Shanghai China

ISBN: (数字)9781728199481

ISBN: (纸本)9781728199498

Enterprise cloudification is a significant means to improve industrial Internet application level and smart manufacturing capability. Recently, Jiangsu Province attaches great importance to promoting enterprise cloudification. Based on the practice and data, current development of enterprise cloudification in Jiangsu is studied. Then, the suggestions to promote enterprise cloudification in Jiangsu are proposed after analyzing the problems and challenges what are faced.

关键词： Industries Cloud computing conferences Ecosystems Safety Information technology smart manufacturing

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smart Traffic Congestion model in IoT-A Review

Smart Traffic Congestion model in IoT-A Review

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K. Ramesh A Lakshna P.N. Renjith Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9781728163871

ISBN: (纸本)9781728163888

Rising incidents of traffic congestion among increasing usage of vehicles have become a high concern in an urban area. Internet of Things (IoT) is the technology to support the monitoring and controlling the road traffic using sensors and cloud-based prediction algorithms. Many researchers are working on this from a different point of view. The purpose of this study is to compare and analyze the methodologies proposed by different researchers and highlight the open challenges in smart traffic. To minimize the traffic congestion in a certain area by diverting or redirecting the upcoming vehicles into the shortest path or alternate path. To predict and prevent the traffic in smart cities, Sensor-based techniques are started using normal traffic cameras in which IoT plays an important role. Some other techniques using signals from vehicles through Wi-Fi, Bluetooth, Zigbee from the smart devices used in vehicles and data used to analyze the traffic pattern by vehicle count. In location-based input, the proximity of dense urban population has the highest amount of traffic flow was found. Traffic prediction and rerouting reduces the level of traffic flow and air pollution (gasoline emission) and provides the traffic free urban roadways.

关键词： Cloud computing Traffic congestion intelligent sensors Predictive models Internet of Things Prediction algorithms data models

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analytics on Health of Mobile Software Ecosystem Based on the Internal Operating Mechanism

Analytics on Health of Mobile Software Ecosystem Based on th...

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IEEE international conference on Services computing (SCC)

作者： Jianmao Xiao Shizhan Chen Shiping Chen Chao Gao Hongyue Wu Xiao Xue Zhiyong Feng College of Intelligence and Computing Tianjin University Tianjin China CSIRO Data61 Australia

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

In addition to publishing and downloading mobile apps, Mobile App Store (MAS) has become the most important ecosystem on mobile smart devices, i.e., Mobile Software Ecosystem (MSECO). However, most of the existing work focus on the analysis of a single entity in MSECO, and rarely analyze the interaction between the entities (such as users, developers, etc.) in MSECO as well as the comprehensive effect of each entity to the entire ecosystem health. In this paper, we propose a method based on computational experiments to simulate and analyze the complex and dynamic interaction of various entities and, how these entities impact on health of MSECO. Firstly, a requirement-driven MSECO model named R-MSECO is established to break down the entire MSECO, which includes user-app interaction, developers-requirements interaction and macro-control of mobile platform three sub-models. Secondly, the health measurement method is proposed to measure the health of MSECO. Finally, we simulate the impact of the dynamic interaction of various entities on the health of MSECO based on computational experiments. The experimental results show that our method is helpful for mobile users to better understanding of the current state of MSECO as well as can provide a reference for developers and platform managers to make development and operation decisions, which is of great significance for the continued healthy development of MSECO.

关键词： Publishing Current measurement Ecosystems Service computing Software Mobile applications smart devices

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