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检索条件"任意字段=8th IEEE International Enterprise Distributed Object Computing Conference"

共 1278 条记录，以下是161-170 订阅

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RetinaNet object Detector Based on Analog-to-Spiking Neural Network Conversion 8

RetinaNet Object Detector Based on Analog-to-Spiking Neural ...

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8th international conference on Soft computing & Machine Intelligence (ISCMI)

作者： Miquel, Joaquin Royo Tolu, Silvia Scholler, Frederik E. T. Galeazzi, Roberto Tech Univ Denmark Dept Elect Engn Automat & Control Grp DK-2800 Lyngby Denmark

ISBN: (纸本)9781728186832

the paper proposes a method to translate a deep convolutional neural network into an equivalent spiking neural network towards the fulfillment o f robust object detection i n a resource-constrained platform. the aim is to provide a conversion framework that is not restricted to shallow network structures and classification problems a s i n s tate-of-the-art conversion libraries. the results show that models of higher complexity, such as the RetinaNet object detector, can be converted through rate encoding of the activations with limited loss in performance.

关键词： Spiking Neural Networks object Detection Spiking-RetinaNet Neuromorphic computing

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Development of Seven Segment Display Recognition using TensorFlow on Raspberry Pi 8

Development of Seven Segment Display Recognition using Tenso...

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8th ieee international conference on computing, Engineering and Design, ICCED 2022

作者： Surya Gunawan, Teddy Mohammad Reduwan, Haque Yaacob, Mashkuri Azmin, Nor Fadhillah M. Ismail, Nanang Yusof Ismail, Md Kartiwi, Mira Rahman, Azhar A. Int. Islamic University Malaysia ECE Department Kuala Lumpur53100 Malaysia UIN Sunan Gunung Djati Electrical Eng. Department Bandung40614 Indonesia Int. Islamic University Malaysia MME Department Kuala Lumpur53100 Malaysia Int. Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Plant Manager Meditech Gloves Sdn Bhd Nilai Malaysia

ISBN: (纸本)9781665453899

LED Seven Segment displays have become embedded in our daily lives, beginning with small devices like wristwatches and progressing to massive mechanical equipment used in industry. Every day, the application base for LED seven-segment displays expands in commercial and industrial applications. To facilitate industrial automation, the previous process's displayed information must be recognized as the input to the subsequent process, even more so when direct communication between processes is not visible. the purpose of this paper is to develop a recognition system for seven segment displays on the Raspberry Pi. the design of hardware, software, and algorithms will be discussed. SSD-MobileNet will be used for subsequent experiments, out of the two architectures evaluated, Tiny YOLO and SSD-MobileNet. Experiments on the Raspberry Pi 4 demonstrate that our proposed system is capable of accurately and quickly detecting seven-segment displays. © 2022 ieee.

关键词： object recognition

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Real-Time Sperm Detection Using Lightweight YOLOv5 8

Real-Time Sperm Detection Using Lightweight YOLOv5

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8th ieee international conference on Computer and Communications, ICCC 2022

作者： Zhang, Zebin Qi, Bolin Ou, Shimin Shi, Chenjian Shenyang Institute of Computing Technology Chinese Academy of Sciences Shengyang110106 China University of Chinese Academy of Sciences Beijing100049 China Xiamen Information Center Department of Pubulic Credit Information Technology Xiamen361001 China Linköping University Department of Computer and Information Science Sweden

ISBN: (纸本)9781665450515

Malformed sperm is an important cause of male infertility, and sperm morphology analysis (SMA) is an effective means to diagnose sperm morphology. Deep learning assists to enhance performance on precise SMA;however, existing deep learning based SMA methods mostly focus on single cell scale, which presents a challenge for obtaining single-sperm-image datasets (one sperm cell per image). It is also challenging to integrate current object detection models on low-performance devices. this paper presents a lightweight model for sperm detection. By removing 50% of convolutional kernels cutting the large-object-detecting head from YOLOv5s, our model got a similar precision to the original YOLOv3 (mAP.5 of 0.957 and 0.947, respectively), but with a model size of only 2.8 MB (123.6 MB of YOLOv3). there is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.604 with 14.4MB model size);however, our model still shows a significant advantage in reducing the number of parameters. Experimental results also indicated that MS COCO pre-training is helpful in sperm detection tasks, and the mosaic augmentation strongly enhances the precision for all YOLO models. © 2022 ieee.

关键词： object detection

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Research on the Evolution of Network Public Opinion of enterprise Environmental Emergencies Based on System Dynamics

Research on the Evolution of Network Public Opinion of Enter...

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ieee international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Yuxin Tong De Xia Shiying Deng College of Management Wuhan University of Technology Wuhan China

In order to actively and effectively manage the public opinion needs triggered by enterprise environmental emergencies, exploring the related factors that affect the heat of enterprise environmental emergencies can help enterprises to scientifically deal with public opinion crisis and control the negative effects of network public opinion on enterprises. Using the system dynamics modeling method, this paper studied the dynamic behavior of the evolution process of network public opinion of enterprise environmental emergencies from the four dimensions of netizens, media, government and enterprises. this paper simulates and analyzes the evolution model of network public opinion with Vensim PLE. the research results show that the evolution of network public opinion on enterprise environmental emergencies generally presents a cyclical process of four stages: gestation, shock, adjustment and adaptation, and end. Variables such as public sentiment, response speed, information transparency, and enterprise disclosure channel preference gap all have a greater impact on the dissemination of network public opinion on enterprise environmental emergencies.

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VATE: Edge-Cloud System for object Detection in Real-Time Video Streams

VATE: Edge-Cloud System for Object Detection in Real-Time Vi...

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ieee international conference on Fog and Edge computing (ICFEC)

作者： Maximilian Maresch Stefan Nastic Distributed Systems Group TU Wien

ISBN: (数字)9798350361353

ISBN: (纸本)9798350361360

In the realm of edge intelligence, emerging video analytics applications are often based on resource constrained edge devices. these applications need systems which are able to provide both low-latency and high-accuracy video stream processing, such as for object detection in real-time video streams. State-of-the-art systems tackle this challenge by leveraging edge computing and cloud computing. Such edge-cloud approaches typically combine low-latency results from the edge and high accuracy results from the cloud when processing a frame of the video stream. However, the accuracy achieved so far leaves much room for improvement. Furthermore, using more accurate object detection often requires having more capable hardware. this limits the edge devices which can be used. Applications related to autonomous drones, with the drone being the edge device, give one example. A wide variety of objects needs to be detected reliably for drones to operate safely. Drones with more computing capabilities are often more expensive and suffer from short battery life, as they consume more energy. In this paper, we introduce VATE, a novel edge-cloud system for object detection in real-time video streams. An enhanced approach for edge-cloud fusion is presented, leading to improved object detection accuracy. A novel multi-object tracker is introduced, allowing VATE to run on less capable edge devices. the architecture of VATE enables it to be used when edge devices are capable of running on-device object detection frequently and when edge devices need to minimise on-device object detection to preserve battery life. Its performance is evaluated on a challenging, drone-based video dataset. the experimental results show that VATE improves accuracy by up to 27.5% compared to the state-of-the-art system, while running on less capable and cheaper hardware.

关键词： Cloud computing Accuracy Image edge detection object detection Streaming media Real-time systems Hardware Low latency communication Drones Edge computing

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Syzballer: Kernel Fuzzing Based on Basic Block Weight and Multi-armed Bandit 8

Syzballer: Kernel Fuzzing Based on Basic Block Weight and Mu...

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8th ieee international conference on Computer and Communications, ICCC 2022

作者： Huang, Zhengyang Song, Xuyan Luo, Yifan Yang, Jun Cui, Baojiang School of Computing Beijing University of Posts and Telecommunications Beijing China School of Cyber Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665450515

the Linux operating system is now extensively used on personal computers, cloud platforms, and enterprise servers. the security of the Linux kernel has also increased in importance. Several techniques, such as symbolic execution, data flow analysis, and reinforcement learning, have been adapted for vulnerability discovery in recent years. Among these techniques, fuzzing is the most widely used one. However, the ease of accessing each kernel code basic block has not been considered in previous research. this means many high-risk vulnerabilities cannot be detected. To solve this problem, we present Syzballer, a hybrid fuzzer that combines multi-armed bandits with basic block weight, which is calculated by traversing the control flow graph generated by the kernel source code. First, we compile the kernel source code into LLVM bitcode and use the static analysis tool SVF to compute the weight of each basic block. then we launched the fuzzer and loaded the weight file. Finally, a multi-armed bandit machine model is used to dynamically alter the task and seed selection. To verify the effectiveness of Syzballer, we compared it with the two most popular kernel fuzzers, Syzkaller and Syzvegas. Experiments have demonstrated that our Syzballer has improved in terms of code coverage and vulnerability detection. © 2022 ieee.

关键词： Static analysis

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Synthetic License Plate Generation: A Novel Approach For Effective License Plate Recognition In Malaysia 8

Synthetic License Plate Generation: A Novel Approach For Eff...

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8th ieee international conference on Engineering Technologies and Applied Sciences, ICETAS 2023

作者： Asaad, Ahmed Faizabadi, Ahmed Rimaz Mohd Zaki, Hasan Firdaus International Islamic University Malaysia Department of Mechatronics Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350327090

As deep learning algorithms devour large amounts of data, the quality and quantity of the dataset utilized to solve the problem of Automatic License Plate Recognition (ALPR) are critical to the success of any proposed solution. However, the variability in license plate designs, lighting conditions, vehicle position, and environmental factors leads to a real challenge due to the limitations of gathering a real dataset that can cover all these variations. this paper presents a novel approach for effective license plate recognition in Malaysia by generating a synthetic dataset for Malaysian license plates. the proposed approach leverages synthetic data generation techniques to create a diverse and comprehensive dataset of license plate images that capture the variability in Malaysian license plate designs and fonts. the synthetic dataset in machine learning is the process of generating artificial, unreal data that simulates real-world data. A fully automatic annotation method is also introduced to label the whole dataset without the need for the tedious manual labeling approach. By exploiting a novel text generation technique and systematically generating the character sequence, we generate realistic synthetic license plate images that closely resemble the characteristics of real license plates in Malaysia. the synthetic license plate images are automatically and accurately annotated using proposed novel algorithms. the two models are trained on a real dataset only and a combined dataset (real and synthetic). Experimental results demonstrate that the synthetic license plate dataset generation approach improves recognition accuracy compared to using only real license plate images with a whopping 12.1% performance increment. © 2023 ieee.

关键词： object detection

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3Pod: Federated Learning-based 3 Dimensional Pothole Detection for Smart Transportation 8

3Pod: Federated Learning-based 3 Dimensional Pothole Detecti...

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8th ieee international Smart Cities conference, ISC2 2022

作者： Alshammari, Sami Song, Sejun Northern Border University Faculty of Computing and Information Technology Rafha Saudi Arabia School of Computing and Engineering University of Missouri-Kansas City Kansas CityMO United States

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

the roadway is the backbone of the country. Bad road conditions can cause vehicles damages and create hazardous driving conditions. Auto damages caused by potholes can add up to thousands of dollars per vehicle. the problem is supposed to be more significant to the auto-driving cars. therefore, evaluating and maintaining roads of road defects plays an essential role in the economy. Most cities rely on residents to report those conditions. However, aside from being a burden on residents, it may not be an effective method. Automating road defect detection using computer vision techniques would significantly ensure roads remain safe and efficient. Smart cities leverage physical and virtual technologies that rely on sensors and cloud-based communication to improve urban environments. this paper proposes a federated deep learning-based 3 Dimensional (3D) pothole detection (3Pod), which is an intelligent real-time evaluation and reporting platform of road conditions and MRI (Maintenance Responsiveness Indicator) using IoT and Artificial Intelligence technologies. It detects road defects in 3D with size estimation to discern other road objects, including patched potholes, fake road bumps, etc. Furthermore, it provides an avoidability score for each defect to show its risk to commuters on the road and their vehicles. We also propose a crowd-voting technique to calculate MRI (Maintenance Responsiveness Indicator), which helps evaluate maintenance performance. © 2022 ieee.

关键词： object detection

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Performance Improvement of Proxy Server Cache Management Using Web Usage Mining 8

Performance Improvement of Proxy Server Cache Management Usi...

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8th international conference on Information Technology Research, ICITR 2023

作者： Leenas, thasan Caldera, H.A. Eastern University Information Technology Center Trincomalee Campus Sri Lanka University of Colombo School of Computing Colombo Sri Lanka

ISBN: (纸本)9798350359503

In these modern industries, all sectors are transitioning from manual to web-oriented applications. thus, the number of Internet users are increasing drastically. therefore, there is a substantial traffic, which increases the demand on the server and server response latency to obtain web objects. the proxy server caching mechanism is one of the approaches to enhance the performance of accessing web objects via the Internet. Since the cache is typically limited in size, a replacement strategy is required to decide which cached web object should be eliminated to allow spaces for fresh web items. Proxy servers make use of various cache replacement strategies such as Least Recency Used (LRU), Least Frequently Used (LFU), and SIZE. the web objects in the proxy cache are influenced by variables like recency, frequency, fetching time, and size. the traditional caching policies decide only one factor at a time, unpopular web objects are wasted in the cache memory (cache pollution) and the performance of the proxy cache decreases. To increase its performance, we propose using the proxy server log file to identify individual users and their sessions as well as categorize the web objects into three groups: high priority, average priority, and low priority web objects. the prepared log file is used to train the classifiers. Future requests are classified as high, average, or low objects using the classifiers, and it is then chosen whether to store them in the proxy cache or not. the objective of this research is to enhance the proxy caching mechanism by implementing the techniques mentioned above. We compared the performance of the suggested approach with traditional caching policies using a trace-driven simulation method. Two performance metrics, Hit Ratio (HR), and Byte Bit Ratio (BHR), were used for our investigation. Our experimental findings demonstrate that the suggested approach outperforms traditional caching policies. © 2023 ieee.

关键词： Proxy caches

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Design and Implementation of Mechanical Processing Quality Control System in Networked Manufacturing Environment

Design and Implementation of Mechanical Processing Quality C...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Xue Li Faculty of Automotive and Mechatronic Engineering Harbin Cambridge University Harbin Heilongjiang China

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

With the rapid development and popularization of the Internet today, mechanical processing enterprises should not only adapt to new situations, but also reorganize various business processes. this article uses the Web to efficiently integrate internal and external resources of the enterprise, and monitors the network operation of the mechanical processing process. this article is based on object-oriented, distributed computing and other technologies to achieve fast and remote intelligent control of mechanical machining processes. Based on this, an object-oriented CNC machining quality control system is constructed. In the average error statistics of different batches of products, the highest error is 0.8mm, and the overall error is relatively small.

关键词： Error analysis object oriented modeling Process control Quality control Machining Inspection Manufacturing

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