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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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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations9;*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave ... 详细信息

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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object Detection from the Images of Hotel Scenario Based on Faster-R-CNN-FFS Model

Object Detection from the Images of Hotel Scenario Based on ...

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Jing Zhang Anqi Zhou Chuanxiu Zhao Degen Huang Jian Xu Dongbei University of Finance and Economics Dalian China Dalian University of Technology Dalian China

this paper aims to solve the problem of object detection in images generated by hotel consumers, which have complex backgrounds and various sizes of objects. It proposes an improved Faster R-CNN-FFS model, which improves the Faster R-CNN model from three aspects: first, it uses feature fusion technology based on Resnet-50 to build feature maps, capturing the whole picture semantic and enhancing the detection effect of small objects; second, it adopts Focal loss function instead of cross entropy loss function, increasing the weight ratio of data samples with less quantity, alleviating the problem of imbalanced positive and negative samples of all categories of detecting objects; third, it introduces Soft-NMS mechanism to filter candidate boxes, improving the robustness of object detection model under occlusion scenario. Experimental results show that the recognition performance of the proposed Faster R-CNNFFS model is significantly different from the state-of-the-art models Retinanet, YOLO v3 and Faster R-CNN, and outperforms Retinanet model significantly.

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Adaptive reppoint: an efficient approach for orientated object detection in remote sensing imagery

Adaptive reppoint: an efficient approach for orientated obje...

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Yuhao Zhan Jiandan Zhong Yongjun Du Qiaomu Wei Chao Chen Lin Fu Luying Li Chengdu University of Information Technology Chengdu China Chengdu Shishi High School (Wenmiao Campus) Chengdu China

With the improvement of the remote sensing technology, more and more remote sensing imagery data can be obtained easily. therefore, the object detection in remote sensing imagery becomes an important task in computer vision area. However, the object of remote sensing imagery are presented with multi-scales, arbitrary orientation and complex background, which make the task becomes challenging. Currently, the mainstream object detection methods of multi-scales and oriented objects, such as RepPoint, which focus on learning a set of adaptive points and generating the point set to the bounding box (with angle information) of the objects. However, the diversity of categories and complex background always make the learned points (in adaptive points set) deviating outside the ground-truth bounding box, which will yield inaccurate location of the objects and decrees the detection result. To deal with this problem, this paper proposes a spatial constraint method that can enforce penalties on outlier points within the point set. Firstly, a reasonable evaluation metric is proposed for quantifying the distance between the outlier points and the ground-truth; Secondly, an spatial constraint parameter is defined for the objects in different scales. Extensive testing was performed on the challenging DOTA and HRSC 2016 datasets. According to the results, the proposed method demonstrated a performance improvement of 1.84% and 0.4% for the respective datasets.

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CMCLNet Cross-Modality Attention Fusion and Cross-Level Feature Interaction for RGBD salient object detection

CMCLNet Cross-Modality Attention Fusion and Cross-Level Feat...

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ieee international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Lifang Xiao Huimei Chen Qingzhen Xu Qiang Chen School of Artificial Intelligence South China Normal University Guangzhou China School of Education Guangzhou University Guangzhou China School of Computer Science Guangdong University of Education Guangzhou China

So as to address the issue of edge defects in RGB-D based salient object detection, we propose a novel structure called Cross Modality Attention Fusion and Cross-Level Feature Interaction (CMCL), with the aim of improving edge quality and effectively extracting salient object from complex backgrounds. Our network is an encoder-decoder structure, specifically, (1) a cross-modality attention fusion (CMAF) module is proposed, which enhance and fuse relevant information of RGB images and depth map, then output enhanced features; (2) the cross-level feature interaction (CLFI) module is proposed to improve edge quality in the encoder stage, combining low-level edge features with high-level semantic information; (3) In the decoder stage, we put forward a gate fusion unit (GU) to reduce background noise, enhancing the significant features of the images. We carry on experiments quantitatively and qualitatively on six publicly available RGB-D datasets and compared them using four evaluation indicators. the results show that our network outperforms mainstream salient detection methods to a certain extent, which is of great significance for solving the key problem of salient object detection in computer vision.

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A Study of Batik Style Transfer using Neural Network 9

A Study of Batik Style Transfer using Neural Network

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9th international conference on Information and Communication Technology, ICoICT 2021

作者： Firman Ihsan, Aditya Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781665404471

In this study, two remarkable applications of convolutional neural network, i.e. texture synthesis and style transfer are applied to batik texture. Individual layers from four pre-trained networks such as VGG-19, Inception V3, ResNet-50, and DenseNet-121 are compared and analyzed. Different batik motifs with some specific criteria are also compared to see the capability of original style transfer algorithm to regenerate concrete texture of Batik. Lastly, we propose a way to reconstruct batik images with some patterns following an object's shape contained in content image. © 2021 ieee.

关键词： Textures

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Intelligent Dictionary Elements Extraction using Optical Character Recognition and Deep Learning based object Detection

Intelligent Dictionary Elements Extraction using Optical Cha...

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international conference for Convergence of Technology (I2CT)

作者： Nigod Dayal Dhurke Sanjay Rabidas Sanket Vijay Narkhede Human Centred Design Computing Group Centre for Development of Advanced Computing (C-DAC) Pune India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

the Sanskrit Encyclopaedic Dictionary conceived and compiled by Deccan College Post Graduate Institute, Pune, India is available in paper format. For greater reach, there is a need for digitization of the dictionaries and conversion of these volumes into a comprehensive structured database. these volumes have Articles with a typical complex structure. In this paper, a novel approach for Automatic Lexical Element Extraction from Sanskrit Encyclopaedic Dictionary Pages (ALEE-SED) that consists of Sanskrit Dictionary Page Segmentation into articles and their meaning blocks using Optical Character Recognition (OCR) and Detectron2 Model further followed by an intelligent article wise dictionary (Lexical) element extraction is described.

关键词： Deep learning Dictionaries Databases Optical character recognition object detection Convergence

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Enhanced Interactive Understanding for Human-object Interaction Detection

Enhanced Interactive Understanding for Human-Object Interact...

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

作者： Jilin Chen Shilong An Zhipeng Zhao Huafeng Xue Ruichun Tang Faculty of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Identifying and classifying the interaction between people and objects in images plays an important auxiliary role in the task of visual understanding. However, it is still a challenging task due to the diversity of human movements and object types. In this paper, a single-stage three-branch HOI detection model is proposed, which divides and fuses the difference in the relationship between the subtasks of the model, uses the three-branch to deal with the difference of subtasks, and enhances the interaction understanding by constructing the association module between the subtasks. In addition, by introducing a powerful visual language model, we designed a visual language knowledge transfer strategy, and used its visual language prior knowledge to assist HOI detection, which enhanced the understanding ability of the model. Finally, the proposed model was evaluated on two published standard datasets, HICO-Det and V-COCO. the experimental results show that the proposed network has better performance than other state-of-the-art methods.

关键词： Visualization Cloud computing Fuses Computational modeling Big Data object recognition Task analysis

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From Mushroom-Peaches to Disease Prediction: Deep Learning Approaches 9

From Mushroom-Peaches to Disease Prediction: Deep Learning A...

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9th international conference on Future Internet of things and Cloud, FiCloud 2022

作者： Loukil, Zainab Mirza, Qublai Khan Ali University of Gloucestershire Cyber and Technical Computing Gloucestershire United Kingdom

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

Recent years have seen a noticeable shift towards the use of automated mechanisms by medical professionals for disease identification with an aim to also eventually predict life threatening diseases. the use of Artificial Intelligence is at the centre of such automated approaches, generally powered by the magnitude of rich datasets of medical imaging. Current approaches involving Machine Learning and other associated technologies have shown promising results in this domain with somewhat accurate detection of certain diseases. However, there are limitations with respect to resource consumption and accurate detection and a significant lack of accurate prediction models. this paper presents a thorough analysis to understand the origins of disease detection through object localisation. Discussion in this paper is based on the identification of certain fruits and vegetables that have complicated visual attributes from machine's perspective. this analogy is then used to enhance the understanding of disease detection that can lead to accurate prediction. © 2022 ieee.

关键词： Forecasting

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Prayer Hall Vacancy Detection

Prayer Hall Vacancy Detection

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international conference on computing, Engineering, and Design (ICCED)

作者： Muhammad Naqib Syahmi Bin Ab Razak Muhammad Imran Bin Mohamad Dini Oktarina Dwi Handayani Zainab S. Attarbashi Sandra Hakiem Afrizal Zeldi Suryady Kuliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia Public Health Master Programs Faculty of Health Sciences and Technology Universitas Binawan Indonesia Telekom Malaysia Research and Development Sdn. Bhd (TM RnD) Malaysia

the issue of overcrowding in prayer rooms located in public areas, such as shopping centers, educational institutions, and other public spaces during prayer times, is a matter of concern nowadays. this condition has the potential to induce discomfort and adversely impact public health issues due to the increased risk of disease transmission, particularly respiratory illnesses. To address this issue, this paper presents a proof-of-concept system that uses computer vision techniques to detect, track, and count the number of people entering and exiting a prayer hall and subsequently count the total number of vacancies. the proposed system is based on the YOLOv5 object detection algorithm the Centroid Tracker object tracking algorithm and an existing object counting method. A custom dataset of 1,993 images of people entering and exiting a prayer hall was collected, cleaned, and annotated for use in training the system. the results of a sample video inference showed an average people detection mAP score of 98.9% and a people counting accuracy of 40% running on a Macbook Pro with an Ml chip. the counting results were displayed on a web app platform hosted on the Heroku cloud application platform.

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