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Internet of Things, Robotics and Distributed Computing (ICIRDC), International Conference on

作者： Heng Wang Yanwei Zhou Zihao Zhang Tianjin Academy of Fine Arts School of Design and Art Tianjin China School of Architecture and Art Design Hebei University of Technology Tianjin China Center for Urban and Rural Renewal and Architectural Heritage Conservation Hebei University of Technology Tianjin China Hebei Key Laboratory of Healthy Living Environment Tianjin China Samueli Electrical and Computer Engineering University of California Los Angeles USA

For a long time, the art of painting has been mysterious to most ordinary people. Since ancient times, countless artists have devoted their lives to painting works, and many masters have formed their own unique painting style. With the rapid development of computer technology, we can extract and imitate the painting styles of various famous artists, and even ordinary people can pry into the mysteries of artistic style, so as to make works with master artistic style. This style rendering technology is called style style transfer. With the continuous development of stereoscopic display technology, people’s demand for more diversified stereoscopic display effects is also increasing. Chinese ink art, as one of the typical representatives of Oriental painting art, has positive significance and far-reaching influence by combining it with three-dimensional display technology. Through the study of Chinese traditional landscape painting and a large number of research on the simulation results of ink style at home and abroad, this paper focuses on the freehand ink style rendering of three-dimensional terrain, and designs a ink style rendering method based on program gray coloring, which can easily observe the effect of ink change when the terrain changes.

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Detection method of nickel foam foreign matter based on double-branch lightweight network

Detection method of nickel foam foreign matter based on doub...

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Chinese Automation Congress (CAC)

作者： Jianqi Li Yincong Liang Huiting Liu Binfang Cao Wei Yan Rui Du Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft Hunan University of Arts and Sciences Changde China College of Information Science and Engineering Jishou University Changde China College of Computer and Electrical Engineering Hunan University of Arts and Sciences Changde China College of of Mathematics and Science Hunan University of Arts and Sciences Changde China College of Automation and Electronic Information Xiangtan University Xiangtan China

Real-time, accurate and rapid detection of defects in nickel foam is a challenging task and plays a crucial role in the quality control of nickel foam production. Currently, research on deep learning-based industrial defect detection usually uses large neural network models to pursue high accuracy, ignoring model complexity and hardware requirements. In order to reduce the application cost, the Detection method of nickel foam foreign matter based on double-branch lightweight network is proposed. First, a lightweight backbone convolutional network is designed to extract features, which reduces computational complexity while effectively extracting features. Then, atrous convolution is used to further optimize the model accuracy, increasing the receptive field of the convolution without losing information, and ensuring the quality of the extracted features. Finally, we use a dual-branch structure for segmentation and classification to dynamically query the detection results of defects. The experimental results on the nickel foam defect dataset show that our model is able to achieve high detection accuracy and single-sheet detection rate, and has strong competitive accuracy and lower detection rate compared with state-of-the-art detectors amount of calculation.

关键词： Convolution Computational modeling Production Quality control Object detection Feature extraction Nickel

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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IRFD: A Feature engineering based Ensemble Classification for Detecting Electricity Fraud in Traditional Meters

IRFD: A Feature Engineering based Ensemble Classification fo...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Zesun Ahmed Mia Md. Moinul Islam Monjurul Haque Saiful Islam S.M.A Mohaiminur Rahman Electrical and Electronic Engineering Bangladesh University of Engineering and Technology University of Liberal Arts Bangladesh Dhaka Bangladesh Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Electronics and Telecommunication Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Mechanical Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9781665494366

Nations have suffered significant economic losses as a result of non-technical electric losses resulting from power fraud. It is a criminal act of stealing electricity by applying various mechanisms that incorporate unauthorized tapping to the power line, bypassing the smart meter, etc. Electricity theft is a significant concern for not only developing countries but also developed countries as well. However, for most developing countries, the implications are catastrophic, given that their usage is always less than their demands. Electricity theft must be detected precisely and quickly in order to be mitigated. In our study, we have proposed a method of predictive ensemble machine learning techniques (IRFD) with a novel combination of feature distinction methods to detect electricity theft. In our proposed model, we have combined feature selection technique, Recursive Feature Elimination with Stratified 10-Fold cross-validation (RFECV) and Isolation Forest (IF), to identify and remove outliers along with several machine learning classifiers to forecast the theft of electricity. This study additionally enhances the management of highly imbalanced fraudulent data with Borderline-SMOTE with SVM (SVMSMOTE) and feature scaling with StandardScaler. Following the study, the Random Forest classifier observed a higher degree of accuracy (97.06%) with higher precision, recall, and F1-Score. To evaluate the efficacy of our proposed model, comparative analysis of the classification metrics is also assessed with several machine learning classifiers like Logistic Regression, Gradient Boosting, XGBoost, AdaBoost, KNN, ANN, along with Random Forest before and after fitting our proposed feature engineering techniques.

关键词： Meters Support vector machines Forestry Developing countries Predictive models Feature extraction Loss measurement

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SDA-Net: A boundary shape and distance aware joint learning framework for segmenting and classifying OCTA images

arXiv

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arXiv 2021年

作者： Lin, Li Wang, Zhonghua Wu, Jiewei Huang, Yijin Lyu, Junyan Cheng, Pujin Wu, Jiong Tang, Xiaoying Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Seit Sun Yat-Sen University Guangzhou China School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan China

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irregularity of FAZ are important biomarkers of various ocular pathologies. Therefore, precise segmentation of FAZ has great clinical interest. Also, there is no existing research reporting that FAZ features can improve the performance of deep diagnostic classification networks. In this paper, we propose a novel multi-level boundary shape and distance aware joint learning framework, named BSDA-Net, for FAZ segmentation and diagnostic classification from OCTA images. Two auxiliary branches, namely boundary heatmap regression and signed distance map reconstruction branches, are constructed in addition to the segmentation branch to improve the segmentation performance, resulting in more accurate FAZ contours and fewer outliers. Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier. Through extensive experiments, the proposed BSDA-Net is found to yield state-of-the-art segmentation and classification results on the OCTA-500, OCTAGON, and FAZID datasets. © 2021, CC BY.

关键词： Classification (of information)

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« Sound people speak to Star people – A sound experts’ perspective on astronomy sonification projects»

arXiv

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arXiv 2022年

作者： Misdariis, Nicolas Özcan, E. Grassi, M. Pauletto, S. Barrass, S. Bresin, R. Susini, P. STMS Ircam-Cnrs-SU Paris France Faculty of Industrial Design Engineering Delft Univ. of Technology Delft Netherlands Department of General Psychology University of Padova Italy Dept of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden *** Canberra Australia 1 Place Igor Stravinsky Paris75004 France

The Audible Universe project aims at making dialogue between two scientific domains investigating two distinct research objects, briefly said, Stars and Sound. It has been instantiated within a collaborative workshop that started to mutually acculturate both communities, by sharing and transmitting respective knowledge, skills and practices. One main outcome of this exchange was a global view on the astronomical data sonification paradigm that allowed to observe either the diversity of tools, uses and users (including visually-impaired people), but also the current limitations and potential ways of improvement. From this perspective, the current paper presents basic elements gathered and contextualised by sound experts in their respective fields (sound perception / cognition, sound design, psychoacoustics, experimental psychology), in order to anchor sonification for astronomy in a more well-informed, methodological and creative process. © 2022, CC BY-NC-ND.

关键词： Stars

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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

arXiv

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arXiv 2020年

作者： Choi, Hongjun Som, Anirudh Turaga, Pavan Geometric Media Lab School of Arts Media and Engineering Arizona State University School of Electrical Computer and Energy Engineering Arizona State University

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://***/hchoi71/AMC-Loss. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Tracking the Exploration and Exploitation in Stochastic Population-Based Nature-Inspired Algorithms Using Recurrence Plots 1

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7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019

作者： Angus, Daniel Fister, Iztok Digital Media Research Centre Queensland University of Technology BrisbaneQLD4059 Australia Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor Slovenia

ISBN: (数字)9783030378387

ISBN: (纸本)9783030378370

The success of every stochastic population-based nature-inspired algorithms is characterized through the dichotomy of exploration and exploitation. In general, exploration refers to the evaluation of points in previously untested areas of a search space, while exploitation refers to evaluation of points in close vicinity to previously visited points. How to balance both components properly during the evolutionary process is still considered as a topical problem in the evolutionary computation community. In this paper, we propose a recurrence plot visualization method for evaluating this process. Our analysis shows that recurrence plots are highly appropriate for revealing how particular algorithms balance exploration and exploitation. © Springer Nature Switzerland AG 2020.

关键词： Evolutionary algorithms

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The Food Preservation using a Solar Dryer Monitoring System

The Food Preservation using a Solar Dryer Monitoring System

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Smart Technologies in Computing, electrical and Electronics (ICSTCEE), International Conference on

作者： S. Sugumaran Sivasankaran V S. Vijayakumar A. Nithya M. G. Chitra Department of ECE Sreenivasa Institute of Technology and Management Studies Chittoor Andhra Pradesh India School of Electrical and Electronics Engineering VIT Bhopal University Sehore M.P India Department of ECE Sreeenivasa Institute of Technology and Management Studies Chittoor Andhra Pradesh Department of ECE MRK Institute of Technology Kattumannarkoil Tamil Nadu Department of Computer Science MMES Women’s Arts and Science College Melvisaram Tamilnadu India

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384635

High-moisture foods like mango, chili, meat etc., are not able to use long days because they are easily decomposed due to changing atmospheres. So the primitive peoples followed different methods to preserve the food and used it for a long time without poisoning. The sun dryer is the usual method to dry the food, but it will take more than one day. The food drying process is done with fossil fuels, but the increasing population is increasing the cost of the fuels. So, the food drying process needs an alternative method to dry the food. In this paper, the food is dried and preserved by solar energy. The overall processes are continuously monitored and preservation of the food by Arduino microcontroller with different sensors such as moisture sensor, LDR, Temperature sensor and cooling fans. The result of this work is better than other existing work and dried food at a low cost.

关键词： Temperature sensors Temperature measurement Fans Costs Moisture Solar energy Sensors

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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