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The Research of Ontology-based Digital Twin Machine Tool Modeling

The Research of Ontology-based Digital Twin Machine Tool Mod...

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international conference on Computer and Communications (ICCC)

作者： Jinsong Liu Dong Yu Xiaoxue Bi Yi Hu Haoyu Yu Beibei Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang P.R. China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang P.R. China Shenyang CASNC Technology Co. Ltd. Shenyang P.R. China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

As a core manufacturing resource, CNC machine tool acts a pivotal part in the new trend of smart manufacturing. Aiming at the intelligent demands of CNC machine tool, this paper proposes a digital twin modeling framework for CNC machine tool. Furthermore, a semantic model of CNC machine tool based on ontology is established to solve the issues of multi-source heterogeneous data fusion and insufficient semantic information, which contributes to condition monitoring service to fully perceive the running status of machine tool.

关键词： Manufacturing industries Digital twin Semantics Ontologies Market research data models Machine tools

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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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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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smart Parking System Based on mmWave Radars and Bluetooth Low Energy: Prototype Implementation

Smart Parking System Based on mmWave Radars and Bluetooth Lo...

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IEEE/IFIP international conference on Embedded and Ubiquitous computing, EUC

作者： Abdulbary Naji Aisha Alabsi Xingfu Wang Ammar Hawbani Liang Zhao Saeed Hamood Alsamhi University of Science and Technology of China School of Computer Science and Technology China Shenyang Aerospace University School of Computer Science China Insight Centre for Data Analytics National University of Ireland And IBB University Galway Ireland

smart Parking has gained so much popularity in recent years due to the increasing number of vehicles in big cities, resulting in traffic congestion in urban areas. Not only on the streets but also in places such as educational institutions, hospitals, commercial activities, special events, and entertainment uses. Finding a free parking lot in these places has evolved difficulty for the drivers. To solve such a problem, governments and researchers tried to find alternative solutions to overcome or mitigate the traffic congestion. Many solutions have been proposed, such as increasing the parking capacities, which takes much time or makes it hard to find a square area in crowded places. Most existing studies, do not consider the cost of deployment, energy, and time-to-market consideration which makes the available systems need further investigation. In this paper, we propose an intelligent parking system prototype that can be useful for the drivers to have a prior knowledge about the available parking lots in the area of interest. Our proposed system involves deploying mmWave Radar sensor nodes in each parking lot to detect the availability of parking vehicle through transmitting radio pulses periodically. The detected information can be sent to the gateway through multi-hop for further statistics and reports. We also give an intensive analysis and study about the challenges and consideration on the mmWave radar design aiming to improve the detection accuracy and avoid false-detection that occurs from objects near to the sensor. To ensure continuous operation and extend sensor life-time, we propose Bluetooth Low Energy BLE-enabled relay-feature as the communication protocol between the nodes.

关键词： Urban areas Radar detection Prototypes Radar Logic gates Ubiquitous computing Millimeter wave communication

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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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Empowering Conventional Systems : Transforming Electronics into smart Devices through Single-Board Computers and Cloud-Based IoT Integration

Empowering Conventional Systems : Transforming Electronics i...

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international conference for Convergence of Technology (I2CT)

作者： Nandini Ammanagi Shripad Kulkarni Rohan Giri Adarsh P S Bhavesh Khati Dept. of Electronics and Telecommunication V.E.S. Institute of Technology Mumbai India V.E.S. Institute of Technology Mumbai India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The rapid proliferation of the Internet of Things (IoT) has revolutionized various sectors, enabling seamless connectivity and data exchange between devices. However, the integration of IoT capabilities into conventional electronic systems remains a challenge due to their non-IoT-ready designs. This obstacle often incurs significant costs and complexities. In this context, this paper explores a transformative approach using Single-Board Computers (SBCs) and cloud-based IoT platforms. SBCs, characterized by their affordability, power, and adaptability, serve as pivotal tools in enhancing existing electronic systems. By seamlessly integrating SBCs, conventional devices can gain IoT connectivity, data processing capabilities, and intelligent functionalities. Cloud-based IoT platforms offer scalable solutions for managing and monitoring IoT devices, delivering services such as data storage, analytics, and machine learning. This paper delves into the synergy between SBCs and cloud-based IoT platforms, illustrating how this convergence can empower conventional systems, metamorphosing them into intelligent, adaptive entities. Through insightful case studies, we demonstrate the practical implementation of this approach, showcasing innovative products and services that emerge as a result[1]. Furthermore, the paper critically examines the challenges and opportunities associated with integrating SBCs and cloud-based IoT platforms into conventional systems. Addressing issues such as security, scalability, and energy efficiency, the paper offers a comprehensive analysis of the complexities involved in this transformative process. In essence, this research sheds light on the transformative potential of combining SBCs and cloud-based IoT platforms, paving the way for a future where conventional electronics evolve into smart, interconnected devices, thereby ushering in a new era of technological advancement and innovation[2].

关键词： Computers Cloud computing smart homes Energy efficiency Complexity theory Internet of Things Security

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Enhancing Cloud computing Security through AI-Based intelligent Intrusion Detection Leveraging Neural Networks and Artificial Bee Colony Optimization 8

Enhancing Cloud Computing Security through AI-Based Intellig...

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8th international conference on Digital Technologies in Education, Science and Industry, DTESI 2023

作者： Ali, Mohsin Razaque, Abdul Yeskendirova, Damelya M. Nurlybayev, Talgat A. Askarbekova, Nessibeli Y. Kashaganova, Zarina A. International Information Technology University Manas St. 34/1 Almaty050000 Kazakhstan

This paper presents a novel approach for detecting malicious network traffic by leveraging artificial neural networks, well-suited for deep packet inspection-based intrusion detection systems. Through comprehensive experimentation with diverse benign network traffic data, encompassing images, dynamic link library files, logs, music files, word processing documents, alongside malicious shell code files sourced from the exploit and vulnerability repository exploitdb, our proposed artificial neural network architecture demonstrates exceptional accuracy in distinguishing benign from malicious network traffic. The introduced neural network consistently achieves an average accuracy rate of 99%, an average area under the receiver operator characteristic curve (AUC-ROC) of 0.99, and an average false positive rate of fewer than 2% across multiple 10-fold cross-validation iterations. These findings underscore the robustness, precision, and accuracy of our classification technique. Furthermore, this innovative approach to detecting malicious network traffic holds substantial potential for enhancing intrusion detection systems in both conventional network traffic analysis and cyber-physical systems analysis, such as smart grids. Additionally, we introduce a novel intrusion detection system (IDS) that combines a multilayer perceptron (MLP) network with artificial bee colony (ABC) and fuzzy clustering algorithms. The MLP network identifies normal and abnormal network traffic packets, while the ABC algorithm optimizes linkage weights and biases during MLP training. Validation of our approach employs the CloudSim simulator and the NSL-KDD dataset, with evaluation metrics comprising mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic. The results obtained from our experiments demonstrate the superior performance of our proposed method when compared to state-of-the-art approaches in the field. © 2020 Copyright for this paper by its authors.

关键词： Malware

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Message from SafeThings 2023 Chairs: 7th ACM/IEEE Workshop on the Internet of Safe Things (SafeThings 2023)

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2023年 IX页

作者： Celik, Z. Berkay Zonouz, Saman Tian, Dave Tian, Yuan Purdue University United States Georgia Institute of Technology United States University of California Los Angeles United States

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Adaptive Window Based Sampling on The Edge for Internet of Things data Streams

Adaptive Window Based Sampling on The Edge for Internet of T...

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international conference on the Network of the Future (NOF)

作者： Taimur Hafeez Gavin McArdle Lina Xu School of Computer Science University College Dublin Dublin Ireland

ISBN: (数字)9781728180557

ISBN: (纸本)9781728180564

The Internet of Things (IoT) generates a massive amount of data. To relieve the pressure placed on cloud and network services, sampling techniques have been applied to remove the redundant data. Thanks to the recently emerged Edge computing (EC) paradigm, sampling on the edge rather than on the IoT end devices can be more intelligent. Most existing sampling algorithms, applicable to edge, do not consider the complex use cases such as predictive analytics. In this paper, we propose a real-time Adaptive Window Based Sampling (AWBS) algorithm to dynamically sample IoT time-series data on the edge. We have verified AWBS on an IoT machine health dataset from NASA using Local Outlier Factor, a well-known unsupervised abnormality detection method. The results were compared with two state-of-the-art sampling algorithms. The experimental results show that AWBS outperforms the other two algorithms by 1) effectively reducing the data to 6.91% and 2) accurately identifying the abnormal behaviour of the machine.

关键词： Vibrations Heuristic algorithms Image edge detection NASA Prediction algorithms Real-time systems Internet of Things

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intelligent Software Mining with Business Intelligence Tools for Automation of Micro services in SOA: A Use Case for analytics

Intelligent Software Mining with Business Intelligence Tools...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Divanshi Priyadarshni Wangoo IGDTUW New Delhi INDIA

ISBN: (数字)9789380544380

ISBN: (纸本)9781728129587

Business Intelligence as a platform is of great assistance to software engineers for analyzing and amassing data to search for information through queries. Object oriented business engineering advances reusability by considering reusable components at the business level. Systematic reuse is the major factor governing the success of an object-oriented software business. Software mining or mining of software engineering data is the successful mining of software engineering data. The integration of mining of software engineering data and Business Intelligence tools gives rise to automation of software services at business level. Automation is the key to Industry 4.0 that we witness today. Therefore, automation of software intelligence is the need of the hour for service delivery. Today's cloud-based applications are working on delivery of Anything as a Service (XaaS) that accounts for provision of anything as a service like database, software, microservices, security on the cloud platform. Service-oriented Architecture (SoA) is based on Software as a Service (SaaS) of the cloud computing. Software intelligence is the form of artificial intelligence which automates the process of mining software engineering data for useful business applications. Not only it integrates the amalgamation of the above fields but at the same time associates the field with industry standards. The aim of this paper is to analyze the various Business Intelligence tools available for promoting intelligence at the business level with benefits to the SoA services particularly microservices for the cloud platform. With the analysis the goal of Business Oriented Software Engineering is enhanced with the analysis and proper usage of business intelligence tools at all the levels of software development process.

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