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Automatic 3D CAD models reconstruction from 2D orthographic drawings

COMPUTERS & GRAPHICS-UK 2023年第1期114卷 179-189页

作者： Zhang, Chao Pinquie, Romain Polette, Arnaud Carasi, Gregorio De Charnace, Henri Pernot, Jean-Philippe HESAM Univ Arts & Metiers Inst Technol LISPEN EA 7515 Paris France Univ Grenoble Alpes CNRS Grenoble INP G SCOP F-38000 Grenoble France Cognit Design Syst Toulouse France

This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score.& COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Multiple-view 2D engineering drawings 3D shape reconstruction Wireframe and surface modeling Pattern matching clustering algorithm

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Topic Modeling-Based Framework for Extracting Marketing Information From E-Commerce Reviews

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IEEE ACCESS 2023年 11卷 135049-135060页

作者： An, Yusung Kim, Dongju Lee, Juyeon Oh, Hayoung Lee, Joo-Sik Jeong, Donghwa Sungkyunkwan Univ Seoul 03063 South Korea Sungkyunkwan Univ Coll Comp & Informat Seoul 03063 South Korea MechaSolut Daegu 41024 South Korea

Reviews left by consumers on e-commerce platforms provide crucial marketing information as they are a publicly available source of information providing insight into consumers' thoughts and opinions. However, it is physically impossible to read hundreds of reviews per product. Therefore, algorithmically extracting the necessary insights from a large volume of review data can provide more advanced information while reducing time and costs. This study aims to automate the process of extracting related products, pros and cons of products, and trend forecasting from review data using clustering algorithms. The review dataset for pros and cons of products and related products was constructed by selecting 17 products on the Naver Shopping platform and crawling them and the product keyword and search volume dataset for trend forecasting was constructed by crawling the data from Itemscout and Naver datalab platform. Various clustering-based algorithms such as Deep clustering Network, BERTopic, and a Transformer-based forecasting model were used to conduct the research. It is expected that this will allow for a more accurate understanding of consumer thoughts, which can be utilized in marketing for various products and services.

关键词： Topic modeling clustering algorithm trend forecasting BERT e-commerce marketing

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Temperature Prediction based on Fuzzy Time Series and MTPSO with Automatic clustering algorithm 2

Temperature Prediction based on Fuzzy Time Series and MTPSO ...

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2nd International Symposium on Computational and Business Intelligence (ISCBI)

作者： Sharma, Yashvardhan Sisodia, Sheetal BITS Pilani Dept Comp Sci & Informat Syst Pilani 333031 Rajasthan India

ISBN: (纸本)9781479975525

Weather prediction is an essential activity in today's world economy with its detrimental effects on various fields like Agriculture, Utility companies, Marine etc. Many methods have been presented based on fuzzy time series to make predictions in areas such as stock price, university enrolments, weather, etc. When using fuzzy time series for forecasting, the length of intervals in the universe of discourse is important due to the fact that it can affect the forecasting accuracy rate. This paper proposes a better approach to forecasting temperature by applying automatic clustering algorithm to partition the universe of discourse. Improvement in results is observed as compared to existing techniques that involve partitioning the universe of discourse in static intervals. The proposed method is tested on temperature prediction and improvements in results are compared to some of already existing techniques.

关键词： Temperature prediction modified turbulent particle swarm optimization (PSO) clustering algorithm fuzzy time series

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Singer Identification using clustering algorithm 3

Singer Identification using Clustering Algorithm

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3rd International Conference on Communications and Signal Processing (ICCSP)

作者： Dharini, D. Revathy, A. Saranathan Coll Engn Dept ECE Panjappur 620012 Thiruchirappall India

ISBN: (纸本)9781479933587

The main objective of this paper is to discuss the effectiveness of features and clustering algorithm to evaluate the performance of the singer identification system. The goal of singer identification is to identify the singer independent of training data. The training and testing phase are done for direct film song (vocal with background) for 10 singers. In training phase 15 film songs of a singer is taken as input. The input songs are made to undergo a set of pre-processing steps. The three stages of preprocessing are pre-emphasis, frame blocking and windowing. The Perceptual Linear Prediction (PLP) features are extracted from each frames of pre-processed signal. The singer model is developed by K-means clustering algorithm for each singer. In clustering method, the cluster centroids are obtained for cluster size of 256 and stored. One model is created for each singer by performing training and testing on the songs considered directly. Mean of minimum distances is computed for each model. Singer is classified based on selection of the model which produces minimum of average. The singer information is main factor in organizing and exploring music data. Singer identification also extends its application in music indexing and retrieval.

关键词： PLP clustering algorithm Singer identification

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V2V Communications Performance Enhancement

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期129卷 2387-2401页

作者： Hamarsheh, Qadri Daoud, Omar Al-Akaidi, Marwan Damati, Ahlam Philadelphia Univ Fac Engn & Technol Dept Commun & Elect Engn Amman Jordan Al Farahidi Univ Baghdad Iraq Philadelphia Univ Fac Engn & Technol Dept Renewable Engn Amman Jordan Amer Int Univ Elect & Comp Engn Al Jahraa Kuwait

During the last decade, vehicle communications gained a huge interest and considered as emerging technologies. It makes transportation easier and safer. However, it has a serious concern with respect to the issue of sharing the information. Some crucial data must be transferred between the vehicles, such as orientation, positions, and any urgent road data. Accordingly, the system reliability and feasibility must be *** this work, a robust vehicular communications benchmark is proposed to enhance the efficiency for such systems. It is based on enhancing the orthogonal frequency division multiplexing structure by imposing the fast frequency hopping structure. Hence, some of the channel impairments such as the effect of the Doppler spread is mitigated. The proposed benchmark is divided into two processing stages: adaptive re-clustering stage and the multiparalell one. These two stages lead to modifying the VANET standard;IEEE 802.11p physical layer. For enhancing the system's throughput, another modification will be made to have Multiple Input Multiple Output antennas as *** results show a noticeable stability compared to our previously published work. Since it improves the BER at 8 dB SNR from 3.10(- 2) to be 7.10(- 4) when using our proposition bases 2.2 MIMO. Another 30% improvement has been attained after increasing the number of antennas to be four. Another enhancement has been derived from the results comparing to the literature Lower-ID DCA technique;that they are exceed its performance from the communication's point of view of either for peed or for the range.

关键词： clustering algorithm Doppler Spread Fast Frequency Hopping MIMO OFDM V2V

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Video object segmentation through semantic visual words matching

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 19591-19605页

作者： Hao, Chuanyan Chen, Yadang Wu, Weimin Yang, Zhi-Xin Wu, Enhua Nanjing Univ Posts & Telecommun Sch Educ Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Macao Dept Electromech Engn State Key Lab Internet Things Smart City Taipa Macau Peoples R China Univ Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

Video object segmentation (VOS) has been widely used in the fields of computer vision. However, existing VOS algorithms have drawbacks, such as difficulty with object deformation, occlusion, and fast motion. We therefore propose an effective VOS algorithm based on semantic visual words matching. Specifically, given the support frame and its corresponding mask, the frame is firstly input to the encoder with an embedding layer, and then a clustering algorithm is followed to generate a group of semantic visual words according to its mask. For a query frame to be segmented, a matching operation is performed against words generated from the support frame. In this manner, each pixel on query frame can be classified into different object categories by the obtained similarity. What's more, a self-attention mechanism is applied to enhance the embedding features in order to capture the global dependencies before the words matching. For further handling the object changing and global mismatch problems, an online update and correction mechanism are also employed in our method. Experiments show that our proposed method achieved competitive results on the DAVIS 2016 and DAVIS 2017 datasets. J&F-mean, the mean value between regional similarity and contour accuracy, reached 83.2% and 72.3% on DAVIS 2016 and DAVIS 2017, respectively.

关键词： Video object segmentation clustering algorithm Visual words Self-attention Online update mechanism

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Methods for Cognitive Diagnosis of Students' Abilities Based on Keystroke Features

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APPLIED SCIENCES-BASEL 2025年第9期15卷 4783-4783页

作者： Chi, Xu Guo, Xinyu Sheng, Yu Cent South Univ Sch Comp Sci & Engeering Changsha 410000 Peoples R China Univ Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China

Keystroke data contain the behavioral information of students during the programming process. The clustering analysis of keystroke data can classify students based on specific characteristics in the programming process, thereby providing a basis for personalized teaching. Research combined with keystroke features is still in its initial stage. Due to the independence and discreteness of keystroke data, and the lack of a clear requirement for the selection of the number of clusters in traditional clustering algorithms, this selection is rather arbitrary, and outliers will affect the clustering effect. Aiming at the above problems, we improve the original method. Keystroke data were used to obtain students' programming behavior information and optimize the traditional clustering algorithm according to the characteristics of keystroke data. The K-means++ algorithm was adopted to determine the initial clustering centers, the elbow method was used to determine the number of clusters, and an outlier processing algorithm was introduced. We have independently constructed a keystroke dataset for computer-based programming examinations and used it to verify our method. Moreover, the improved algorithm has shown improvements in multiple evaluation indicators. Experiments have proven that the method proposed in this paper can more accurately classify students' proficiency levels in the evaluation of students' programming abilities in the educational field. This provides strong support for the formulation of teaching strategies and the allocation of resources, and the method possesses important application value and practical significance.

关键词： keystroke characteristics clustering algorithm cognitive diagnosis programming education

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Plausible Proxy Mining With Credibility for Unsupervised Person Re-Identification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第7期33卷 3308-3318页

作者： Zheng, Dingyuan Xiao, Jimin Sun, Mingjie Bai, Huihui Hou, Junhui Northwestern Polytech Univ Sch Microelect Xian 710072 Peoples R China Univ Liverpool Liverpool L69 3BX England Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou 215123 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3BX England Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

One effective way to address unsupervised person re-identification is to use a clustering-based contrastive learning approach. Existing state-of-the-art methods adopt clustering algorithms (e.g., DBSCAN) and camera ID information to divide all person images into several camera-aware proxies. Then, for each person image, the extracted feature representation is pulled closer to the centroids of its pseudo-positive proxies (the proxies that share the same pseudo-identity label with this image) and pushed away from the centroids of other pseudo-negative proxies (the proxies that share the different pseudo-identity label with this image). However, the quality of the proxy centroid is significantly affected by the proxy impurity issue and thus deteriorates the learned feature representations. On the premise that we cannot introduce superior supervision signals by thoroughly solving the proxy impurity issue, for a person image, identifying its plausible proxies: the pseudo-negative proxies which potentially include its wrongly-clustered instances (the instances with the same ground-truth identity with this image), and further fixing the resulted incorrect supervision signals become an urgent and challenging problem. This paper proposes a simple yet effective approach to address this problem. With a given image, our method can effectively locate its plausible proxies. Then we introduce credibility to measure how much we should treat the centroid of each mined plausible proxy as a positive supervision signal rather than entirely negative. Extensive experiments on three widely-used person re-ID datasets validate the effectiveness of our proposed approach. Codes will be available at: https://***/Dingyuan-Zheng/PPCL.

关键词： Person re-identification clustering algorithm proxy impurity issue contrastive learning plausible proxy super-vision signals

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Intelligent algorithms in privacy-preserving authentication schemes and traceability with accuracy in VANETs for smart transportation

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SOFT COMPUTING 2023年第23期28卷 1-10页

作者： Shikka, Rishi Kamalraj, R. Shah, Pradeep Kumar Sutariya, Kamal Anwar, Syed Rashid Kumar, Arvind Sanskriti Univ Dept Elect Mathura Uttar Pradesh India Jain Deemed Univ Sch Comp Sci & Informat Technol Bangalore India Teerthanker Mahaveer Univ Coll Comp Sci & IT Moradabad Uttar Pradesh India Parul Univ Parul Inst Engn & Technol Dept Comp Sci & Engn Vadodara Gujarat India Arka Jain Univ Dept Comp Sci & IT Jamshedpur Jharkhand India Galgotias Univ Sch Comp Sci & Engn Greater Noida Uttar Pradesh India

Vehicle ad hoc network is used on the road where vehicles serve as mobile nodes. VANET's primary uses include intelligent transportation and active security. The framework for intelligent transportation systems relies heavily on VANETs. Intelligent transportation networks are sometimes used to refer to VANETs. In VANETs, however, authentication and user privacy remain crucial issues. This study proposes a novel method for privacy-based authentication algorithms that improve the accuracy of data transmission for smart VANET transportation. Here, a lightweight hybrid authentication-based privacy-preserving algorithm is used to improve privacy. Then, an effective congestion-based clustering algorithm is used to send the data. Throughput, QoS, latency, computational cost, and data transmission rate are the focus of the experimental analysis. Throughput, QoS, latency, computational cost, and data transmission rate were all achieved using the proposed method. The proposed method makes use of lightweight, efficient pseudonyms that can be used for both message authentication and as a trapdoor to provide conditional anonymity. The proposed method's ability to withstand a variety of security and privacy threats is demonstrated by the various attack scenarios and the proposed technique attained throughput 93%, QoS 55%, latency 62%, computational cost 63%, and data transmission rate 69%.

关键词： Privacy-based authentication Data transmission accuracy Smart transportation VANET Efficient congestion clustering algorithm

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Scenario-Transformation-Based Optimal Sizing of Hybrid Hydrogen-Battery Storage for Multi-Timescale Islanded Microgrids

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2023年第3期14卷 1784-1795页

作者： Jiang, Sheng Wen, Shuli Zhu, Miao Huang, Yuqing Ye, Huili Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Marine Equipment Res Inst Dept Technol Manage Shanghai 200031 Peoples R China

The increasing penetration of volatile renewable energy poses a significant challenge for islanded microgrids in maintaining the seasonal power balance on a long-term timescale. To support renewable integration, seasonal energy storage techniques are expected to coordinate with short-term storage systems to compensate for power mismatches on multiple timescales. However, hybrid storage sizing is often hindered by the coupling of different timescales, which will lead to a large number of variables and greater computational complexity. Thus, in this article, a novel optimal sizing framework is proposed for a hybrid hydrogen-battery storage system, considering a year-round time horizon. To ensure reliable planning of hydrogen storage, a "seasonal-trend decomposition based on LOESS (STL)" technique is applied to preserve long-term power fluctuation characteristics during scenario clustering. Moreover, a least-squares-based scenario approximation method is developed to improve the accuracy of the clustering results. On this basis, a scenario-transformation solution method is proposed to avoid a large number of variables due to year-round hourly operation. The case studies verify the advantages and efficiency of the proposed method.

关键词： clustering algorithm hydrogen-battery energy storage multiple-timescale operation optimal sizing scenario transformation seasonal imbalance STL decomposition

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