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13th International Conference on Multimedia and Ubiquitous Engineering (MUE) / 14th International Conference on Future Information Technology (Future Tech)

作者： Kim, Jeong Ah Kim, SunTae Choi, JaeYoung Lee, Ji Young Cho, Young Hwa Catholic Kwandong Univ Dept Comp Educ 24 Beomil Ro 579 Beon Gil Gangneung Si Gangwon Do South Korea Chonbuk Natl Univ CAIIT Dept Software Engn 567 Baekje Daero Jeonju Si Jeollabuk Do South Korea Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon Gyeonggi Do South Korea

ISBN: (纸本)9789813292444;9789813292437

It is not easy to define the well-defined process for software research project but it is important to keep the tracking the progress of the projects. Without the process, it is not easy to measure the progress since we cannot define the unit of measure. In this paper we suggested RD(research descriptor) concept to define the unit of measure. Also we suggested RD relation to trace the progress the research workflow. With suggested mechanism, we can monitor the research project process and visualization the progress.

关键词： Software research R&D project Traceability deep learning algorithm

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Output Only Damage Detection of a Steel Truss Bridge Based on a Semisupervised BiLSTM Modeling Scheme

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Structural Control and Health Monitoring 2025年第1期2025卷

作者： Zahid, Tazwar Bakhtiyar Rana, Shohel Haque, Md. Niamul Dhaka1000 Bangladesh

The application of machine learning techniques in bridge health monitoring is gaining widespread popularity as it overcomes the problems faced by conventional methods. However, the scarcity of labeled data for damaged bridges in training the model acts as a hindrance. The present study proposes a data science–based novel approach for overcoming this hindrance using a semisupervised, output-only method for multiple-level damage identification of a steel truss bridge. The method employs sequence-to-sequence modeling of vehicle-induced vibration response only from a single sensor position. The authors have used a bidirectional long short-term memory (BiLSTM) network for damage feature extraction. A statistical distance metric tool, Kullback–Leibler divergence, has then been utilized for feature discrimination. The method’s efficiency is numerically investigated through a 3-D finite element model of a steel truss bridge based on real bridge specifications. A dynamic analysis using a moving vehicle is performed to obtain vehicle-induced accelerations. A total of 36 different damage scenarios have then been incorporated into the bridge. The effect of sensor position and performance because of variation in vehicle operation has also been investigated. The results show that the proposed approach successfully detects all the damage scenarios. The methodology’s performance has also been validated in detecting damages for the Old ADA Bridge benchmark data. The methodology successfully detected multiple damage states using a single sensor response. Copyright © 2025 Tazwar Bakhtiyar Zahid et al. Structural Control and Health Monitoring published by John Wiley & Sons Ltd.

关键词： damage detection deep learning algorithm sequence-to-sequence modeling statistical metric steel truss bridge vehicle induced response

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Measurement of spatial heterogeneity in street restorative perceptions and street refinement design

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Scientific reports 2025年第1期15卷 19388页

作者： Yalun Lei Qingqing Li Jingwen Tian Jia Hu Jixiang Jiang College of Fashion and Design Donghua University Shanghai 200051 China. College of Design and Innovation Tongji University Shanghai 200092 China. College of Art and Design Nanjing Forestry University Nanjing 210037 China. College of Art and Creativity Anhui Vocational and Technical College Hefei 230011 China. jiah9177@***.

Restorative perception of streets is an essential metric for evaluating urban environments and serves as a key indicator of pedestrians' perspectives on street refinement design. However, restorative perception varies significantly across different streets, necessitating an analysis of these disparities. This study integrates street view data, deep learning algorithms, the MGWR model, and space syntax to analyze spatial heterogeneity in restorative perceptions and optimize street design strategies. Using the random forest (RF) algorithm and restorative component scales (RCS), we assessed residents' perceptions of street restoration in Shanghai's Huangpu District. Our analysis identified eight key visual elements influencing perceptions, such as sidewalks, buildings, and walls. Results revealed significant geographic variations, with high-perception areas concentrated around open parks, waterfronts, and well-designed buildings featuring thoughtful amenities. Visual elements like trees and plants were found to significantly enhance restorative perceptions. Moran's Ι statistics and multiple regression models further revealed spatial heterogeneity and clustering in perceptions, highlighting the importance of location-based planning. Among the regression models, the MGWR model achieved the highest R value (0.615), indicating that variables like trees, roads, sidewalks, and intercepts are particularly sensitive to spatial heterogeneity. Additionally, space syntax analysis underscores the positive impact of complex street networks on accessibility, convenience, and environmental satisfaction. The main contribution of this study is identifying the most effective model through a comparison of multiple regression models, demonstrating the spatial heterogeneity of different visual elements. Based on restorative perception and accessibility coupling assessment, streets in urgent need of rehabilitation were identified. We believe our findings can assist professionals in developi

关键词： deep learning algorithm MGWR model Refinement design Restorative environment perception Spatial heterogeneity Street view images

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Research on image tagging algorithm on internet

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 13619-13625页

作者： Zhang, Junyang Guo, Yang Hu, Xiao Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

At the age of big data, the information changes quickly. How to extract the key information timely seems to be quite important. Therefore, improving the execution speed of BFS algorithm means a lot to the processing of big data. This paper firstly introduces the implementation flow, features and performance evaluation criteria of the breadth-first search algorithm, and secondly introduce the research status of BFS algorithm based on current CPU platform both at home and abroad. Thirdly, this paper optimizes the algorithm by using the local principle of program, load balancing method and so on. Finally, the comparison of the algorithm performance is shown in this paper: the program optimized in this paper gets good performance and could be popularized further in practice.

关键词： BFS algorithm Graph search deep learning algorithm

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Bridge Inspection and condition assessment using Unmanned Aerial Vehicles (UAVs): Major challenges and solutions from a practical perspective

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SMART STRUCTURES AND SYSTEMS 2019年第5期24卷 669-681页

作者： Jung, Hyung-Jo Lee, Jin-Hwan Yoon, Sungsik Kim, In-Ho Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 34141 South Korea

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amou

关键词： bridge inspection unmanned aerial vehicle (UAV) imaging device condition assessment deep learning algorithm

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Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 48-54页

作者： Dou, Yangliu Fang, Yihao Hu, Chuan Zheng, Rong Yan, Fengjun McMaster Univ Dept Mech Engn 1280 Main St West Hamilton ON L8S 4K1 Canada McMaster Univ Dept Comp & Software 1280 Main St West Hamilton ON L8S 4K1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. TensorFlow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.

关键词： driver information systems traffic engineering computing optimisation principal component analysis neural nets behavioural sciences computing learning (artificial intelligence) gated branch neural network GBNN algorithm mandatory lane changing suggestion on-ramps-of-highway mandatory lane changing behaviour modelling MLC behaviour modelling MLC suggestion system advanced driver assistance systems ADAS nonmerge event merge event correlation analysis scaled exponential linear units activation function adaptive moment estimation optimiser real-world dataset U S Highway 101 U S Interstate 80 Federal Highway Administration Next Generation Simulation NGSIM principal component analysis TensorFlow framework Python Pearson correlation method receiver operating characteristic score merge accuracy nonmerge accuracy deep learning algorithm

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deep learning approach for automatic out-of-plane needle localisation for semi-automatic ultrasound probe calibration

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HEALTHCARE TECHNOLOGY LETTERS 2019年第6期6卷 204-209页

作者： Groves, Leah A. Vanberlo, Blake Peters, Terry M. Chen, Elvis C. S. Univ Western Ontario Sch Biomed Engn London ON Canada Univ Western Ontario Robarts Res Inst London ON Canada Univ Western Ontario Schulich Sch Med London ON Canada Univ Western Ontario Med Biophys London ON Canada

The authors present a deep learning algorithm for the automatic centroid localisation of out-of-plane US needle reflections to produce a semi-automatic ultrasound (US) probe calibration algorithm. A convolutional neural network was trained on a dataset of 3825 images at a 6 cm imaging depth to predict the position of the centroid of a needle reflection. Applying the automatic centroid localisation algorithm to a test set of 614 annotated images produced a root mean squared error of 0.62 and 0.74 mm (6.08 and 7.62 pixels) in the axial and lateral directions, respectively. The mean absolute errors associated with the test set were 0.50 +/- 0.40 mm and 0.51 +/- 0.54 mm (4.9 +/- 3.96 pixels and 5.24 +/- 5.52 pixels) for the axial and lateral directions, respectively. The trained model was able to produce visually validated US probe calibrations at imaging depths on the range of 4-8 cm, despite being solely trained at 6 cm. This work has automated the pixel localisation required for the guided-US calibration algorithm producing a semi-automatic implementation available open-source through 3D Slicer. The automatic needle centroid localisation improves the usability of the algorithm and has the potential to decrease the fiducial localisation and target registration errors associated with the guided-US calibration method.

关键词： medical image processing calibration learning (artificial intelligence) needles image registration mean square error methods biomedical ultrasonics convolutional neural nets out-of-plane needle localisation semiautomatic ultrasound probe calibration deep learning algorithm needle reflection probe calibration algorithm convolutional neural network automatic centroid localisation algorithm probe calibrations pixel localisation semiautomatic implementation automatic needle centroid localisation target registration errors calibration method size 6 0 cm size 4 0 cm to 8 0 cm

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deep Boltzmann machine algorithm for accurate medical image analysis for classification of cancerous region

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COGNITIVE COMPUTATION AND SYSTEMS 2019年第3期1卷 85-90页

作者： Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel Mepco Schlenk Engn Coll Autonomous Dept Elect & Elect Engn Sivakasi 626005 Tamil Nadu India

In this research work, a deep learning algorithm is applied to the medical domain to deliver a better healthcare system. For this, a deep learning framework for classification the region of interest pattern of complex hyperspectral medical images is proposed. The performance of computer-aided diagnosis by verifying the region in hyperspectral image by pre and post-cancerous region classification is enhanced. For this a deep Boltzmann machine (DBM) architecture of the bipartite structure as an unsupervised generative model was developed. The performance of DBM is compared with deep convolutional neural network architecture. For implementation, a three-layer unsupervised network with a backpropagation structure is used. From the presented dataset, image patches are collected and classified into two classes, namely non-informative and discriminative classes as labelled classes. The spatial information is used for classification and spectral-spatial representation of class labels is formed. In the labelled classes, the accuracy, false-positive predictions, sensitivity are obtained for the proposed fully-connected network. By the proposed cognitive computation technique an accuracy of 95.5% with 93.5% sensitivity was obtained. From the obtained classification, accuracy and success rate DBM provide a better classification of complex images compared to traditional convolution network.

关键词： learning (artificial intelligence) pattern classification unsupervised learning image classification Boltzmann machines feature extraction medical image processing cancer convolutional neural nets deep Boltzmann machine algorithm accurate medical image analysis deep learning algorithm medical domain healthcare system deep learning framework interest pattern complex hyperspectral medical images computer-aided diagnosis hyperspectral image post-cancerous region classification deep Boltzmann machine architecture bipartite structure unsupervised generative model deep convolutional neural network architecture three-layer unsupervised network backpropagation structure image patches discriminative classes labelled classes spatial information spectral-spatial representation cognitive computation technique success rate DBM complex images traditional convolution network

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Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

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IEEE ACCESS 2020年 8卷 130431-130451页

作者： Aldhyani, Theyazn H. H. Alrasheedi, Melfi Alqarni, Ahmed Abdullah Alzahrani, Mohammed Y. Bamhdi, Alwi M. King Faisal Univ Community Coll Abqaiq Al Hasa 31982 Saudi Arabia King Faisal Univ Dept Quantitat Methods Sch Business Al Hasa 31982 Saudi Arabia Albaha Univ Dept Comp Sci & Informat Technol Al Baha 65431 Saudi Arabia Umm Al Qura Univ Coll Comp Mecca 24231 Saudi Arabia

Network traffic analysis and predictions have become vital for monitoring networks. Network prediction is the process of capturing network traffic and examining it deeply to decide what is the occurrence in the network. The accuracy of analysis and estimation of network traffic are increasingly becoming significant in achieving guaranteed Quality of Service (QoS) in the network. The main aim of the presented research is to propose a new methodology to improve network traffic prediction by using sequence mining. The significance of this important topic lies in the urge to contribute to solving the research problem in network traffic prediction intelligently. We propose an integrated model that combines clustering with existing series models to enhance prediction the network traffic. Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series. The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Furthermore, we have suggested using the weighted exponential smoothing model as preprocessing stages for increasing the reliability of the proposed model. In this research paper, machine intelligence proposed to predict network traffic. The machine intelligence is working as pre-processing for enhancing the existing time series models. The machine intelligence combines non-crisp Fuzzy-C-Means (FCM) clustering and the weight exponential method for improving deep learning Long Short-Time Memory(LSTM)and Adaptive Neuro-Fuzzy Inference System (ANFIS)time series models. The ANFIS and LSMT time series models are applied to predict network traffic. Two real network traffic traces were conducted to test the proposed time series models. The empirical results of proposed to enhanced LSTM 97.95% and enhanced ANFIS model is R = 96.78% for cellular traffic data, with respect to the correlation indicator. It is obse

关键词： Telecommunication traffic Predictive models Autoregressive processes Time series analysis Computational modeling Prediction algorithms Machine learning Machine intelligence soft computing machine learning network traffic deep learning algorithm

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Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology

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Journal of Pathology Informatics 2018年第1期9卷 32页

作者： Olsen, Thomas Jackson, B. Feeser, Theresa Kent, Michael Moad, John Krishnamurthy, Smita Lunsford, Denise Soans, Rajath Department of Dermatology Boonshoft School of Medicine Wright State University School of Medicine United States Dermatopathology Laboratory of Central States Dayton OH United States Proscia Inc. Philadelphia PA United States

Background: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. Aims: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. Methods: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. Results: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. Conclusions: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories. © 2018 Journal of Pathology Informatics | Published by Wolters Kluwer - Medknow.

关键词： Artificial intelligence computational pathology computer-aided diagnosis deep learning algorithm dermatopathology digital pathology whole slide images

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