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2020 24th ISPRS Congress - Technical Commission V (TC-V) on Education and Outreach - Youth Forum

作者： Khoshelham, K. Tran, H. Acharya, D. Díaz Vilariño, L. Kang, Z. Dalyot, S. Dept. of Infrastructure Engineering University of Melbourne Parkville3010 Australia Applied Geotechnologies Group School of Industrial Engineering University of Vigo Spain Dept. of Remote Sensing and Geo-Information Engineering School of Land Science and Technology China University of Geosciences Beijing100083 China Environmental Crowdsourcing Laboratory Faculty of Civil and Environmental Engineering Technion-Israel Institute of Technology Haifa3200003 Israel

Automated 3D reconstruction of indoor environments from point clouds has been a topic of intensive research in recent years. Different methods developed for the generation of 3D indoor models have achieved promising results on different case studies. However, a comprehensive evaluation and comparison of the performance of these methods has not been available. This paper presents the preliminary results of the ISPRS benchmark on indoor modelling, an initiative of Working Group IV/5 to benchmark the performance of indoor modelling methods using a public dataset and a comprehensive evaluation framework. The performances of the different methods are compared through geometric quality evaluation of the reconstructed models in terms of completeness, correctness, and accuracy of wall elements. The results show that the reconstruction methods generally achieve high completeness but lower correctness for the reconstructed models while accuracies range from 0.5 cm to 6.7 cm. © 2020 Authors.

关键词： Benchmarking

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A Skin Type Classification Method Using Mobile Device-Based Deep Learning Model

A Skin Type Classification Method Using Mobile Device-Based ...

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International Conference on Applied System Innovation (ICASI)

作者： Hung-Tse Chan Yan-Wei Liao Sin-Ye Jhong Shih-Che Chien Kai-Lung Hua Yung-Yao Chen Dept. of Electronic and Computer Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC Department of Engineering Science National Cheng Kung University No.1 University Road Tainan City Taiwan ROC National Chung-Shan Institute of Science and Technology No.481 6th Neighborhood Sec. Jia’an Zhongzheng Rd. Longtan Dist. Taoyuan City Taiwan ROC Dept. of Computer Science and Information Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC

Skin care products should be tailored to suit different skin types. However, skin testing can be expensive and time-consuming, particularly for students or office workers who may need access to specialized equipment. In the present study, we developed a skin type detection system by using computer-vision and deep-learning techniques that can be easily accessed through a mobile phone application. Our system integrates with the TensorFlow Lite framework on the Android platform and therefore supports various hardware accelerations and easy model validation. TensorFlow Lite, an open-source library developed by Google, is a lightweight, cross-platform, machine-learning framework for mobile and Internet of Things devices. It also supports various hardware accelerations. Our experimental results reveal that the proposed method has an accuracy of 96% and is easy to use on mobile devices. This system provides a convenient and cost-effective means of identifying the skin type and selecting appropriate skin care products.

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Estimating the Effect of Sand-Roughened Bed on Hydraulic Jump Characteristics Using Heuristic Models

SSRN

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SSRN 2024年

作者： Daneshfaraz, Rasoul Sammen, Saad Sh. Norouzi, Reza Abba, Sani Issa Salem, Ali Mirzaee, Reza Siha, Parveen Hamed, Mohammed Magdy Elbeltagi, Ahmed Civil Engineering Department Faculty of Engineering University of Maragheh East Azerbaijan Maragheh Iran Civil Engineering Department College of Engineering Diyala University Diyala Iraq Interdisciplinary Research Centre for Membrane and Water Security King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia CivilEngineeringDepartment FacultyofEngineering MiniaUniversity Minia61111 Egypt Structural Diagnostics and Analysis Research Group Faculty of Engineering and Information Technology University of Pécs Hungary Civil Engineering Department Faculty of Engineering Semnan University Semnan Iran Department of Civil Engineering Chandigarh University Punjab 140301 India B 2401 Smart Village Giza12577 Egypt Agricultural Engineering Dept. Faculty of Agriculture Mansoura University Mansoura35516 Egypt

The hydraulic jump occurs in open channels and downstream of hydraulic structures because these features change the flow regime from super-critical to sub-critical. To prevent downstream channel erosion and guarantee the construction of a hydraulic jump, a stilling basin is utilized. The effect of sand-roughened beds on hydraulic jump characteristics was investigated using an experimental study. After that, new machine learning models including Emotional neural network (ENN), Hammerstein Weiner model (HWM), Elman neural networks ENN, and Extreme learning neural networks (ELNN) were adopted to estimate the ratio of hydraulic jump length to initial water dept.. Different evaluation criteria such as R2, MSE, R, and RMSE, scatter plots, time series comparison, and Spiral plots have been used to evaluate the models' performance. The results revealed that the Elman NN model outperformed the other models in estimating the (Lj/y1), while the HW model has a weaker performance than other models in estimating Lj/y1. © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

arXiv

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arXiv 2022年

作者： Wang, Xiang Yang, Yimin Guo, Zhichang Zhou, Zhili Liu, Yu Pang, Qixiang Du, Shan The Dept. of Computer Science Lakehead University Thunder BayON Canada The Dept. of electrical and computer engineering Western University LondonON Canada The School of Mathematics Harbin Institute of Technology Hei Long Jiang China The School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China The School of Microelectronics Tianjin University Tianjin China The Okanagan College KelownaBC Canada The Dept. of Comp. Sci. Math Physics & Statistics The University of British Columbia OkanaganBC Canada

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods. © 2022, CC BY.

关键词： Convolutional neural networks

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Client-server Mobile System for Diagnosis of Asynchronous Motors

Client-server Mobile System for Diagnosis of Asynchronous Mo...

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Advanced technology (KhPIWeek), IEEE KhPI Week on

作者： Olha Chorna Petro Kurliak Victor Busher Oleksii Chornyi Valerii Tytiuk Iurii Zachepa Institute of Education and Science in Electrical Engineering and Information Technologies Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Electrical power engineering electrical engineering and electromechanics Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Dept. of Electrical Engineering and Electronics National University “Odessa Maritime Academy” Odessa Ukraine Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

This paper presents client-server mobile system for diagnosis of asynchronous motors. The system is created as a mobile application for smartphones and tablets. The system provides processing of the measured values of the currents and voltages of the stator phases of the asynchronous motor. According to the processing results, the results of diagnostics and forecasting of the current state of asynchronous motors are calculated. The software runs on the Android OS. The main purpose is to predict the current state of AD, which has a parametric electromagnetic asymmetry of the stator phases.

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Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts

arXiv

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arXiv 2024年

作者： Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Bernard and Irene Schwartz Center for Biomedical Imaging Dept. of Radiology NYU School of Medicine NY United States Dept. of Radiology NYU School of Medicine NY United States CUBRIC School of Engineering Cardiff University Cardiff United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Neuherberg Germany School of Computation Information and Technology Technical University of Munich Munich Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Neurobiology Research Unit Copenhagen University Hospital Copenhagen Denmark

Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics’ robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. © 2024, CC BY.

关键词： Image quality

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On the Role of the Genetic Features Selection for Intelligent Classification of Covid-19 Patients

SSRN

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SSRN 2023年

作者： Aliani, Cosimo Rossi, Eva Solinski, Mateusz Francia, Piergiorgio Bocchi, Leonardo Lanatà, Antonio Buchner, Teodor Dept. of Information Engineering University of Florence Via di Santa Marta 3 Florence Italy School of Biomedical Engineering Imaging Sciences Faculty of Life Sciences Medicine King’s College London Strand LondonWC2R 2LS United Kingdom Faculty of Physics Warsaw University of Technology Koszykowa 75 Warsaw00-662 Poland

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection can cause feared consequences such as those affecting microcirculation. These abnormalities are highly considered because they have been associated with prognosis in the acute phase. The use of genetic algorithms can be helpful in better understanding the characteristics of microcirculation that are mainly affected by *** study aimed to verify the presence of microcirculation alterations in Patients with COVID-19 performing heart rate variability (HRV) analysis by using peak-to-peak intervals extracted from photoplethysmographic (PPG) signals. The dataset comprises 97 participants divided into two groups: healthy (50 subjects) and patients affected by mild COVID-19 (47 subjects). The parameters evaluated with the HRV analysis were investigated using the genetic algorithms with three different subject selection strategies (two random subjects, five subjects tournament, and roulette wheel selection), and four different classifiers (Discriminant Analysis Classification (DISCR), Binary Decision Tree (DT), K-Nearest Neighbor (KNN) and Naive Bayes (NB)) to assess which was the most representative for each subject class. All the classifiers consider three features (meanRR, sd2/sd1, and alpha1) particularly important. These features are present respectively in the 94.2%, 78%, and 80.2% of the considered subjects. The Fitness End value remains about the same among all three selection methods with the same classifier but changes instead with different classifiers. For each selection method used, the DT classifier achieves the best results regarding maximum fitness value within the population: 91.8% for the two random subjects and five subjects tournament selection method and 92.2% with the roulette wheel selection method. Subsequently, machine learning classifications were performed by training classifiers with only the three features, and the best result was achieved with the DT classifier obtai

关键词： COVID-19

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COLD: A Benchmark for Chinese Offensive Language Detection

arXiv

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arXiv 2022年

作者： Deng, Jiawen Zhou, Jingyan Sun, Hao Zheng, Chujie Mi, Fei Meng, Helen Huang, Minlie The CoAI group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Huawei Noah’s Ark Lab Hong Kong

Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To this end, we propose a benchmark – COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset – COLDATASET and a baseline detector – COLDETECTOR which is trained on the dataset. We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources. We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models. We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues. Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational linguistics

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Smart home-healthcare for skin lesions classification with IoT based data collection device.

TechRxiv

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TechRxiv 2021年

作者： Islam, M.D. Khairul Kaushal, Chetna Amin, M.D. Al Dept. of Information Communication Technology Islamic University Kushtia Bangladesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prime University Dhaka1216 Bangladesh

Skin lesions or malignancies have been a source of worry for many individuals in recent years. The underline reason behind it mainly the diet and environmental pollution. Yet many individuals are unaware of the issue and, more importantly, many people do not want to visit a hospital for diagnostic or therapeutic purposes. So, we have come up with a pipeline to diagnose skin lesions at home. Firstly, we proposed a IoT base data collection device which is accessible by patient to capture skin lesions image. This IoT device will encrypt and send the collected image towards a cloud storage;then it will decrypt the image send to the computer assisted diagnosis system. In CAD, we have implemented ensemble classifier. Ensemble classifier created depending on four deep learning classifiers namely VGG16, DenseNet201, Inception V3 and Efficient B7;whereas encryption and decryption performed in order to secure a patient data from unauthorized access. For skin lesions classification, we have used "HAM10000" dataset where 7 kind of skin lesions data included;Although DenseNet201 performed well, the ensemble model provides the highest accuracy with 87.22% as well as its test loss/error is lower than others with 0.4131. © 2021, CC BY.

关键词： Digital storage

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High Gain Patch Antenna Array with Defected Ground Structure for 5G Applications 14

High Gain Patch Antenna Array with Defected Ground Structure...

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14th International Conference on Open Source Systems and Technologies, ICOSST 2020

作者： Farhat, Sidra Arshad, Farzana Ahmad, Ashfaq Khan, Ameer Taimour Actsena Research Group University of Engineering and Technology Telecommunication Engineering Department Taxila Punjab47050 Pakistan Chosun University Dept. of Information and Communications Engineering Korea Republic of School of Computational Sciences University of Faisalabad Faisalabad Pakistan

ISBN: (纸本)9781728190501

A novel, single layer patch antenna array based on 1 × 4 elements having a compact size, high gain and with low design complexity is presented. A single patch is designed and to enhance the performance, two distinct arrangements of arrays are proposed and analyzed. To reduce the coupling between the antenna array elements, an I-shaped slot is inserted in the ground. Parasitic metallic strips are employed to increase the peak gain. The proposed antenna array has a compact size of 3.6 × 3.6 cm2, Rogers RT5880 is used as a substrate with a thickness of 0.508 mm. The design achieves an operational bandwidth of 2.06 GHz with a peak gain of 15.07 dBi. The maximum radiation efficiency of 91.9% is obtained at 28.5 GHz. The proposed array-2 is then fabricated and measured. Various parameters such as reflection coefficient, gain, radiation efficiency, Voltage Standing Wave Ratio (VSWR), surface currents and radiation patterns for E and H-fields are investigated. © 2020 IEEE.

关键词： Radiation efficiency

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