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A Compact 28 GHz Millimeter Wave Antenna for Future Wireless Communication

Computers, Materials & Continua 2022年第7期72卷 301-314页

作者： Shahid Khan Adil Bashir Haider Ali Abdul Rauf Mohamed Marey Hala Mostafa Ikram Syed LCOMS ASECUniversity de LorraineMetz57070France Department of Electrical and Computer Engineering Comsats University IslamabadAbbottabad Campus22060Pakistan Electrical Engineering Department Bahauddin Zakariya UniversityMultan60800Pakistan Department of Electronics Technology University of TechnologyNowshera24100Pakistan Department of Electrical Engineering National University of Sciences and TechnologyH-12Islamabad44000Pakistan Smart Systems Engineering Laboratory College of EngineeringPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Software Engineering Military College of SignalsNational University of Sciences and TechnologyIslamabad44000Pakistan

This article presents a novel modified chuck wagon dinner bell shaped millimeter wave(mm-wave)antenna at 28 *** proposed design has ultra-thin Rogers 5880 substrate with relative permittivity of *** design consists of T shaped resonating elements and two open ended side *** desired 28 GHz frequency response is achieved by careful parametric modeling of the proposed *** maximum achieved single element gain at the desired resonance frequency is 3.45 *** efficiency of the proposed design over the operating band is more than 88%.The impedance bandwidth achieved for−10 dB reference value is nearly 2.9 *** proposed antenna is transformed into four element linear array which increases the gain up to 10.5 *** fabricated prototype is tested for the measured *** is observed that measured results closely match the simulated *** considering its simple structure and focused radiation patterns,the proposed design is well suited for IoT(Internet of Things),mmWave microwave sensing,5G and future RF(Radio Frequency)frontends.

关键词： RF front-ends mmWave partial ground plane 28 GHz IoT 5G antenna

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Multi-phase-field simulation of austenite peritectic solidification based on a ferrite grain

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Chinese Physics B 2021年第1期30卷 572-577页

作者： Chao Yang Jing Wang Junsheng Wang Yu Liu Guomin Han Haifeng Song Houbing Huang School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Software Center for High Performance Numerical Simulation China Academy of Engineering PhysicsBeijing 100088China

A multi-phase-field model is implemented to investigate the peritectic solidification of Fe-C alloy. The nucleation mode of austenite is based on the local driving force, and two different thicknesses of the primary austenite on the surface of the ferrite equiaxed crystal grain are used as the initial conditions. The simulation shows the multiple interactions of ferrite, austenite, and liquid phases, and the effects of carbon diffusion, which presents the non-equilibrium dynamic process during Fe-C peritectic solidification at the mesoscopic scale. This work not only reveals the influence of the austenite nucleation position, but also clarifies the formation mechanism of liquid phase channels and molten pools. Therefore, the present study contributes to the understanding of the micro-morphology and micro-segregation evolution mechanisms of Fe-C alloy during peritectic solidification.

关键词： multi-phase-field simulation morphology evolution peritectic solidification carbon diffusion Fe-C alloy

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SymAware: A software Development Framework for Trustworthy Multi-Agent systems with Situational Awareness

arXiv

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arXiv 2024年

作者： Casablanca, Ernesto Zhang, Zengjie Marchesini, Gregorio Haesaert, Sofie Dimarogonas, Dimos V. Soudjani, Sadegh School of Computing Newcastle University Newcastle United Kingdom Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for software systems Germany

Developing trustworthy multi-agent systems for practical applications is challenging due to the complicated communication of situational awareness (SA) among agents. This paper showcases a novel efficient and easy-to-use software framework for multi-agent simulation, named SymAware which provides a rich set of predefined data structures to compute, store, and communicate SA for agents. It also provides an abstract interface for the agents to compute their control inputs taking into account the awareness of the situation, knowledge, and risk of surrounding agents. Besides, utilizing a cluster of specialized components, SymAware hides the heavy computation of physical rendering and communication interfacing of simulation engines behind the control threads, resulting in high implementation efficiency in bridging the gap between conceptual prototyping and practical applications. Three multiagent case studies are used to validate the efficacy and efficiency of this software framework. © 2024, CC BY.

关键词： software design

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Simulative Investigations of Crowd Evacuation by Incorporating Reinforcement Learning Scheme 6

Simulative Investigations of Crowd Evacuation by Incorporati...

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6th International Conference on Algorithms, Computing and systems, ICACS 2022

作者： Sinpan, Nitinun Sasithong, Pruk Chaudhary, Sushank Poomrittigul, Suvit Leelawat, Natt Wuttisittikulkij, Lunchakorn Department of Electrical Engineering Chulalongkorn University Thailand Wireless Communication Ecosystem Research Unit Thailand Department of Software Engineering and Information System Pathumwan Institute of Technology Thailand Disaster and Risk Management Information Systems Research Unit Department of Industrial Engineering Chulalongkorn University Thailand

ISBN: (纸本)9781450397407

Safe and effective evacuation is very essential to decrease casualties in an emergency event. The social force model is used to simulate the movement of evacuee and leader crowd evacuation with leader in an experimental area. The a-star algorithm is used to find the evacuation routes of each evacuee from the initial location to the exit. In this paper, we proposed the application of reinforcement learning to train the agent to become an evacuation leader via the Unity ML-Agents Toolkit. This model is tested in three scenarios: an evacuation without a leader, an evacuation with a leader, and an evacuation with randomly located agent. They are considered for comparison purposes to show the impact of an agent that tries to tell an exit to all evacuees in the experimental area. The experimental results show that the proposed crowd evacuation agent could effectively evacuate all evacuees in the experimental area. © 2022 ACM.

关键词： Reinforcement learning

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Yolo Based License Plate Detection Of Triple Riders And Violators

Yolo Based License Plate Detection Of Triple Riders And Viol...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： R G Nandhakumar K. Nirmala Devi N. Krishnamoorthy S. Shanthi V R Pranesh S Nikhalyaa Department of Computer Science and Engineering Kongu Enginering College Perundurai Erode Tamilnadu India Department of Software Systems and Engineering School of Information and Engineering Vellore Institutte of Technology Vellore India

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can’t continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m*m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model’s detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. Keywords—Deep learning; Object detecti

关键词： Head Road accidents Databases Computational modeling Object detection Road safety Safety

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2 18

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Murugan, S. Palani Peter, M. Chandrakumar Raj, P. Anand Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Tamil Nadu Nagapattinam India Periyar Maniammai Institute of Science & Technology Department of Software Engineering Tamil Nadu Thanjavur India E.G.S. Pillay Engineering College Department of Computer Science and Business Systems Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AOMDV Protocol Intelligent Gen Algorithm MANET Network Simulator NS-2

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Application Specified Soft-Error Failure Rate Analysis Using Sequential Equivalence Checking Techniques

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Tsinghua Science and technology 2020年第1期25卷 103-116页

作者： Tun Li Qinhan Yu Hai Wan Sikun Li School of Computer Science National University of Defense TechnologyChangsha 410073China the Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China. School of Software Tsinghua UniversityBeijing 100084China

Soft errors have become a critical challenge as a result of technology scaling. Existing circuit-hardening techniques are commonly associated with prohibitive overhead of performance, area, and power. However,evaluating the influence of soft errors in Flip-Flops(FFs) on the failure of circuit is a difficult verification ***, we proposed a novel flip-flop soft-error failure rate analysis methodology using a formal method with respect to application behaviors. Approach and optimization techniques to implement the proposed methodology based on the given formula using Sequential Equivalence Checking(SEC) are introduced. The proposed method combines the advantage of formal technique-based approaches in completeness and the advantage of application behaviors in accuracy to differentiate vulnerability of components. As a result, the FFs in a circuit are sorted by their failure rates, and designers can use this information to perform optimal hardening of selected sequential components against soft errors. Experimental results of an implementation of a SpaceWire end node and the largest ISCAS’89 benchmark sequential circuits indicate the feasibility and potential scalability of our approach. A case study on an instruction decoder of a practical 32-bit microprocessor demonstrates the applicability of our method.

关键词： soft error failure rate analysis Sequential Equivalence Checking(SEC) application specified

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A New Application of Machine Learning: Detecting Errors in Network Simulations 22

A New Application of Machine Learning: Detecting Errors in N...

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Proceedings of the Winter Simulation Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University Oxford OH USA

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

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Consistency Check for Multimodal Transportation Data: A Case Study of Vision and LiDARA 9

Consistency Check for Multimodal Transportation Data: A Case...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Liu, Zhengyu Chen, Zhenwu Yang, Xiangmin Peng, Lei Software Engineering Institute University of Science and Technology of China HeFei230026 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Research Center for Automotive Electronics ShenZhen518055 China Shenzhen Urban Transport Planning Center Co. Ltd. Innovation Center for ITS ShenZhen518063 China Shenzhen Shenzhentong Co. Ltd. Dept. Research and Development ShenZhen518131 China

ISBN: (纸本)9798350390025

This study proposes a novel consistency verification method for multimodal transportation data, targeting spatial feature issues caused by multisource heterogeneous data in the transportation field. We unify different sources of data and point cloud data into a single 3D space through image data dimensionality increase and point cloud registration, addressing data inconsistencies caused by perspective differences. Through the application of data dimensionality augmentation and point cloud registration, our method effectively resolves spatial feature discrepancies between various data sources, significantly improving the accuracy of consistency verification. Experimental results indicate that the model trained with our method exhibits superior precision in consistency verification of multisource heterogeneous data compared to traditional approaches. © 2024 IEEE.

关键词： Spatio-temporal data

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Attribute expansion relation extraction approach for smart engineering decision-making in edge environments

Attribute expansion relation extraction approach for smart e...

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作者： Cui, Mengmeng Zhang, Yuan Hu, Zhichen Bi, Nan Du, Tao Luo, Kangrong Liu, Juntong School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing China School of Competitive Sports Shandong Sport University Jinan China School of Continuing Education and Training Shandong Sport University Jinan China Department of Physical Education Chengdu Technological University Chengdu China Sports Department Chengdu University of Information Technology Chengdu China

In sedimentology, the integration of intelligent engineering decision-making with edge computing environments aims to furnish engineers and decision-makers with precise, real-time insights into sediment-related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision-making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment-related knowledge in the realm of intelligent engineering decision-making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi-source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute-extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. Af

关键词： Knowledge graph

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