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Animation Video Production System Based on three Dimensional Vision sensor Network 4

Animation Video Production System Based on Three Dimensional...

引用

4th international Academic Exchange conference on Science and Technology Innovation, IAECST 2022

作者： Liu, Yongchun Fuyang Normal University Fuyang China

ISBN: (纸本)9798350320008

How to produce deformable human characters with a strong sense of realism more efficiently has been one of the hot spots in the research of computer 3D animation technology. the confusion of staffing in the process of animation production, the weak association between production staff and tasks and the difficulty for system managers to keep track of the progress of the project and other information restrict the further development of 3D animation. this paper therefore investigates animation video production systems based on 3D visual sensing networks. the paper begins with a brief description of the evaluation of visual comfort in 3D animation stereoscopic effects and explains the user requirements of the system. the results show that the number of surface and solid voxels tends to increase as the voxel size decreases, but does not affect the operation of the system, so the animation production system designed in this paper is able to meet the demand for the operational efficiency of deformable human characters. © 2022 ieee.

关键词： Animation

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Automatic Bangla License Plate Recognition System for Low-Resolution Images 4

Automatic Bangla License Plate Recognition System for Low-Re...

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4th international conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Haque, Naimul Islam, Samira Tithy, Rabeiya Afrose Uddin, Mohammad Shorif Jahangirnagar University Department of Cse Bangladesh Manarat International University Department of Cse Bangladesh

ISBN: (纸本)9781665490450

Automatic License Plate Recognition (ALPR) System is a crucial task that is used in numerous critical situations. Although there are several ways to accomplish LP recognition, our method is intended to be effective not just on LP with high resolution, but even when the LP is of very low resolution (LR). Previous works on ALPR systems emphasized achieving recognition benchmarks on LP images that are close to the sensor. However, if the camera is far from the vehicle, the generated detected frame yields an LP image of LR, which is hard for the recognition model to classify. Our method employed Enhanced Super Resolution Generative Adversarial Networks (ESRGANs) to upscale the [32 × 24] LR image to the high resolution of dimension [256 × 192] with a scale factor ratio of 8. For training purposes, we also collected and trained the models on a dataset of 2211 images. the Peak signal-to-noise ratio (PSNR) for the reconstruction on 200 testing samples is 12.587. the optical character recognition accuracy for the reconstructed and ground truth images are 78% and 91% respectively. © 2022 ieee.

关键词： Optical character recognition

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Enhancing sensor-based Capabilities in Low-cost Drone for Surveying and Inspection Applications 7

Enhancing Sensor-based Capabilities in Low-cost Drone for Su...

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7th international conference on Automation, Control and Robots, ICACR 2023

作者： Almalsi, Qussai Anwar Ali, Syed Saad Azhar Muqeeth, Mohammed Abdul Abdallah, Ayman M. King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Irc for Avaiation and Space Exploration Aerospace Engineering Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350302882

Drones used in modern applications require sophisticated command, control, and communication. Depending on the application, the computing power and communication requirements vary from one application to another, in some applications it requires a processing power that surpasses the capabilities of the onboard systems. As a result, several technology businesses have been developing a variety of drone control systems and offering various functionalities for their drones with a high price tag, making them not feasible option for people with limited resources. the drone system described in this paper proposes an alternative way to control and integrate one of the applications that require high processing power like object detection using a purpose-built drone and a budget companion computer. the paper provides a comprehensive guide to the hardware and assembly of a drone system, the software needed for a basic drone setup, as well as additional software required for controlling the drone and applying significant processing power technology to validate our results. these components are implemented using an additional Raspberry Pi 4 Model B, flight controller, and Raspberry Pi High Quality Camera Module resulting in a cost-effective solution. this study also explores updates regarding the universal asynchronous receiver-transmitter (UART) serial connections between the flight controller and the companion computer. © 2023 ieee.

关键词： Cost effectiveness

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An Improved RBF Neural Network based on Clustering Algorithm for Estimating Wind Speed from X-band Marine Radar Images 4

An Improved RBF Neural Network based on Clustering Algorithm...

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4th ieee international conference on Industrial Cyber-Physical systems (ICPS)

作者： Wang, Hui Fan, Dongyang Qiu, Haiyang Zhu, Zhiyu Zhi, Pengfei Zhu, Wanlu Jiangsu Univ Sci & Technol Dept Elect Informat Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728162072

An improved RBF neural network based on clustering algorithm for estimating wind speed from X-band marine radar images is proposed. To remove the outliner caused by rainfall or co-frequency noise interference, K-means clustering algorithm is applied to the data related to the wind speed estimation. the filtered outliner removing data is feed into the RBF neural network, hidden layer and extended constants are determined by subtractive clustering, the recursive least squares is used to make the network connection weights. Radar and sensor data collected from the East China Sea are used for validation. Compared to the BP neural network based methods, the accuracy of wind estimation is further improved with a reduction of about 2.82m/s in root square deviation, the proposed method can effectively estimate the wind speed from X-band marine radar image.

关键词： marine radar images wind speed RBF neural network clustering algorithm sensor data

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An Efficient distributed Energy and Consumption Method for Ensuring Wireless sensor Network (WSN) Coverage Using the Firefly Algorithm 4th

An Efficient Distributed Energy and Consumption Method for E...

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4th international conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Uma, K. Ramesh Kumar, C. Shanmugam, thirumurugan School of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India School of Computing Science and Engineering Galgotias University UP Greater Noida India Department of Information Technology University of Technology and Applied Sciences Suhar Muscat Oman

ISBN: (纸本)9789819916641

In this article, we are talking about the wireless sensor network sub-field of a body area sensor network. the establishment of the wireless body area sensor network has taken place after the refinement of the wireless sensor network was underway. We have introduced an innovative energy-efficient routing system that incorporates a fuzzy logic also bio-inspired firefly algorithm, which considers these limitations. Together, our proposal of correlation region selection then bio-stimulated firefly algorithm centered NBSs use fuzzy logic-based correlations to minimize energy transmission, while our joint firefly NBS proposal makes a valuable contribution by cutting energy consumption. To identify and select enriched regions that contain high energy and detect information, our suggested fuzzy logic-based correlation region selection mechanism is configured to favor those regions. We, however, suggested a bio-stimulated firefly algorithm fitness function for the collection of NBSs. To prevent exhaustion of valuable transmission, the fitness function accounts for the diffusion antiquity and residual energy for NBSs. To confirm the in-depth influence of our suggested scheme, we deportment numerous simulations exhausting the Nano-SIM tool to measure the various aspects involved in conserving energy resources, sinking endwise deferment, and increasing packet delivery ratio. We found that by using an optimized selection for correlated regions besides NBSs in refining network lifespan and packet delivery ratio although dipping the typical endwise delay, our proposed system accomplishes the significant task of determining which regions and NBSs are more important to the network and allows for the lower end-to-end delay. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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A Deep Residual Convolutional Neural Network based Bearing Fault Diagnosis with Multi-sensor Data 4

A Deep Residual Convolutional Neural Network based Bearing F...

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4th ieee international conference on Industrial Cyber-Physical systems (ICPS)

作者： Niu, Guangxing Liu, Enhui Bin Zhang Golda, Michael Mastro, Stephen Univ South Carolina Dept Elect Engn Columbia SC 29208 USA Stephen Mastro Are Naval Surface Warfare Ctr Philadelphia Div NSWCPD Philadelphia PA USA

ISBN: (纸本)9781728162072

Accurate bearing fault diagnosis is essential and significant for the safety and reliability of industrial rotating systems. To monitor the health condition of bearings in full scales, multi-sensors are always assembled in different locations of bearings. the data from different types or different locations of sensors have different sensitivity for fault diagnosis and localization, which makes feature extraction from multi-source data is critical for diagnosis. Deep learning based approaches have been widely used in bearing fault diagnosis, and have achieved significant successes. Deep residual convolutional neural network, as an improved structure of convolutional neural networks, has powerful learning ability when dealing with a deeper network. To improve the training efficiency and accuracy of diagnosis and make better use of bearing vibration data, this paper proposes a deep residual convolutional neural network based bearing fault diagnosis in which multi-sensor data are converted and combined into grayscale images for feature extraction and diagnosis. the proposed approach is verified using the data of tapered roller bearings, which are tested under different rotating speeds and loads. Experimental results and comparisons show that the proposed approach can achieve promising diagnosis accuracy with high efficiency.

关键词： Bearing Convolutional Neural Network Multi-sensor Deep Residual Convolutional Neural Network

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BIG TRAJECTORY DATA: A distributed computing PERSPECTIVE 7

BIG TRAJECTORY DATA: A DISTRIBUTED COMPUTING PERSPECTIVE

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7th international conference on Smart City Applications, SCA 2022

作者： Anbaroglu, B. Alter, Y. Dept. of Geomatics Engineering Hacettepe University Ankara Turkey Graduate School of Science and Engineering Hacettepe University Ankara Turkey Aselsan Inc. Ankara Turkey

Trajectory data constitute location of objects at specified time intervals. the continuous availability of GNSS signals, or discrete availability of sensor systems such as license plate recognition cameras are used to generate trajectory data. Consequently, in a smart city context, big trajectory data are being generated on a daily basis. the analysis of big trajectory data entails the use of a distributed environment to conduct analysis, and at least two data sources. the literature review conducted in this paper shows that the two Vs of big data, Volume and Variety, may not be satisfied since researchers usually rely on a centralised computing environment, and analyse data coming from a single data source. Out of the 17 papers published from 2020 in Scopus, only five of them relied on a distributed computing environment, and two of them utilised more than one data source. © 2022 international Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Trajectories

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Complex-Event Mining Over Centralized and distributed Data Streams

Complex-Event Mining Over Centralized and Distributed Data S...

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4th international conference on Process Mining (ICPM)

作者： Garofalakis, Minos Tech Univ Crete Sch Elect & Comp Engn Univ Campus Kounoupidiana Khania 73100 Hellas Greece

ISBN: (数字)9798350397147

ISBN: (纸本)9798350397147

Massive, continuous data streams arise naturally in emerging large-scale event monitoring scenarios (such as enabling observability for complex distributed systems, or network-operations monitoring in large ISPs), where usage information from numerous devices needs to be continuously collected and analyzed for interesting trends and real-time reaction to different conditions (e.g., anomalies, hotspots, or DDoS attacks). Such applications raise important memory-, time-, and communication-efficiency issues, making it critical to carefully optimize the use of available computation and communication resources. In this talk, I will provide an overview of centralized and distributed data streaming models and some of the key algorithmic tools in the space of streaming complex-event mining, along with relevant applications and directions for future research.

关键词： distributed databases Patents Monitoring Data mining Web services Tutorials Technological innovation

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Intelligent Fall Detection System based on sensor and Image data for Elderly Monitoring 4

Intelligent Fall Detection System based on Sensor and Image ...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Shilpa Aarthi, M. Juliet, Sujitha Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In recent years Internet of things has gained much attention. Various techniques are being developed to acquire the data from human environment for various smart services and applications. Monitoring elderly people remotely using smart homes is one of the difficult and challenging tasks in lOT due to many accidents that occur during daily activities like falls etc. In elderly people, falling is considered as a main reason for cause of injuries of death. To prevent the fall of elderly people and to identify the fall in smart homes technology need to be increased so that the fall can be prevented and rate of survival can be increased. Currently many loT, wearable devices, mobile phones etc have been developed with advanced technologies which can be used to develop a fall detection technique using loT and Deep Learning. In this paper an loT and Deep learning based elderly fall detection technique is proposed. Deep convolutional Network is used to extract the features obtained using loT devices and to detect the fall using intelligent Deep fall Detection algorithm. Feature extraction is performed using Squeeze Net model by extracting required features. SVM classifier is used for classification of fall and non-fall occurrences. If a fall event is detected an alert message is sent to the smartphone of hospital management. UR fall detection dataset is utilized to perform the experiments. Based on the obtained results it is inferred that proposed method achieved an accuracy of 99.81 % on fall detection dataset. © 2022 ieee.

关键词： Fall detection

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Large Scale Machine Translation Language Model Training System Based on Spark

Large Scale Machine Translation Language Model Training Syst...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： Yan Hu Department of English Education Shandong Vocational College of Light Industry Zibo Shandong China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

When faced with large language datasets, machine translation language models are difficult to process, resulting in low accuracy and efficiency of translation. therefore, this study constructs a framework based on Firstly, by installing Apache Spark and configuring its integration with Hadoop distributed File System (HDFS), a distributed computing environment capable of parallel data processing is established to support rapid processing of large-scale data. then, the distributed storage system HDFS is adopted to optimize data access efficiency and reduce IO (Input/Output) bottlenecks. Finally, a new iterative training strategy is implemented to gradually improve the translation accuracy of the model through incremental learning. Under the optimal configuration, the combination of a learning rate of 0.1 and batch processing of 2048 achieves a BLEU score of up to 30.8%, with training lasting only 4 hours. this study demonstrates the effectiveness of a large-scale machine translation language model training system based on Spark in improving translation efficiency and accuracy.

关键词： Training Translation Accuracy Computational modeling distributed databases Cluster computing Parallel processing Data models Machine translation Sparks

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