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Journal articles on the topic 'Collaborative filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Xia, Wei, Meiqiu Sun, and Zhuoyang Zhou. "Diffusion Collaborative Feedback Particle Filter." IEEE Signal Processing Letters 27 (2020): 1185–89. http://dx.doi.org/10.1109/lsp.2020.3003795.

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2

Rwedhi, Riyam, and Salam Al-augby. "Improving Collaborative Filter Using BERT." Journal of Kufa for Mathematics and Computer 10, no. 2 (2023): 23–29. http://dx.doi.org/10.31642/jokmc/2018/100204.

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With the increasing number of books published and the difficulty of obtaining appropriate research attention, the recommendation systems can increase the affordability and availability of these books. In this work, we expand our work to enhance the accuracy of book collaborative filtering by applying semantic similarity to book summaries, in addition to that addressing major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of three stages: preprocessing, building the system, and evaluation. The technol

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Yan, Bingyong, Haixu Cui, Haitao Fu, Jiale Zhou, and Huifeng Wang. "A New Method for Feature Extraction and Classification of Single-Stranded DNA Based on Collaborative Filter." Mathematical Problems in Engineering 2020 (July 21, 2020): 1–10. http://dx.doi.org/10.1155/2020/3876367.

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The traditional support vector machine algorithm is not enough to classify single-stranded DNA molecules, so this paper proposes an improved threshold extraction algorithm based on collaborative filter for the classification of single-stranded DNA. Firstly, according to the different characteristic curves of the blocking current signals formed by the four bases (A, T, C, and T) that make up DNA molecules crossing the nanopore, the collaborative filter feature extraction algorithm with improved threshold is proposed. Then, the feature information is reconstructed and sent to the SVM classifier

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4

Mao, Xing‐Peng, Yun‐Long Yang, Hong Hong, and Wei‐Bo Deng. "Multi‐domain collaborative filter for interference suppressing." IET Signal Processing 10, no. 9 (2016): 1157–68. http://dx.doi.org/10.1049/iet-spr.2016.0140.

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5

Gou, Jianping, Yue Hu, Liyuan Sun, Zhi Wang, and Hongxing Ma. "Collaborative knowledge distillation via filter knowledge transfer." Expert Systems with Applications 238 (March 2024): 121884. http://dx.doi.org/10.1016/j.eswa.2023.121884.

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6

Fu, Tian Ran, and Guo Hui Song. "Research on Network Literature Recommendation Technology Based on Literature Tag." Applied Mechanics and Materials 687-691 (November 2014): 2059–62. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2059.

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With the development of network literature website, the number of network literature increase dramatically. Readers will hardly know which literature to select from the great mass of network literature that steadily accumulates. Some network literature website use collaborative filter method to recommend potential interested literature to user. However, the collaborative filter algorithm has many defects, such as cold start and sparsity. To solve these problems, we propose a new method which based on literature tag. This method is helpful to overcome collaborative filter algorithm’s shortcomin

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Рубель, Андрей Сергеевич, та Владимир Васильевич Лукин. "ЛОКАЛЬНО-АДАПТИВНА ФІЛЬТРАЦІЯ ЗОБРАЖЕНЬ З ВИКОРИСТАННЯМ ТЕТРОЛЕТ ПЕРЕТВОРЕННЯ". Aerospace Technic and Technology, № 5 (7 грудня 2017): 92–99. http://dx.doi.org/10.32620/aktt.2017.5.13.

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Image filtering is one of the main tasks in image processing. Images are inevitably subject to noise during image formation and subsequent transmission. Thus, it is desirable to remove noise. Image denoising (filtering) improves visual appearance and facilitates subsequent automatic processing (segmentation, classification, detection of edges). A large number of filters has been developed so far. Among them, filters based on orthogonal transforms as well as non-local filters are the most effective. One of the representatives of filters based on orthogonal transforms is the standard sliding win

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8

Wang, Guoqing, Ning Li, and Yonggang Zhang. "Hybrid consensus sigma point approximation nonlinear filter using statistical linearization." Transactions of the Institute of Measurement and Control 40, no. 8 (2018): 2517–25. http://dx.doi.org/10.1177/0142331217691758.

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In this paper, a hybrid consensus sigma point approximation nonlinear filter is proposed for state estimation in collaborative sensor network, where hybrid consensus of both measurement and information is utilised. Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate

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Tan, Jingwen, Youxin Hu, and Jianqiu Luo. "Social recommendation algorithm based on collaborative filter algorithms." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 120–23. http://dx.doi.org/10.54097/fcis.v3i1.6346.

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For traditional collaborative filter recommendation algorithm technology, this paper combines the collaborative filtering recommendation algorithm with the community division technology of social networks, use the Louvain community to divide algorithms, divide the recommendation users to a community of similar users, and use the collaborative filter algorithm based on the user similarity formula within the community to recommend. In order to verify the effectiveness and accuracy of the algorithm in this paper, based on the introduction of the Douban dataset and the evaluation criteria used, a

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10

Minetto, Alex, Alessandro Gurrieri, and Fabio Dovis. "A Cognitive Particle Filter for Collaborative DGNSS Positioning." IEEE Access 8 (2020): 194765–79. http://dx.doi.org/10.1109/access.2020.3033626.

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11

Wu, T., and P. O’Grady. "An Extended Kalman filter for collaborative supply chains." International Journal of Production Research 42, no. 12 (2004): 2457–75. http://dx.doi.org/10.1080/00207540410001666279.

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12

Haymaker, John, Paul Keel, Edith Ackermann, and William Porter. "Filter mediated design: generating coherence in collaborative design." Design Studies 21, no. 2 (2000): 205–20. http://dx.doi.org/10.1016/s0142-694x(99)00042-3.

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13

Sun, Yongqiang. "Design and Application of Collaborative Experiment Management Platform for Innovation and Entrepreneurship Education Based on an Intelligent Sensor Network." Journal of Sensors 2022 (August 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/9542280.

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Innovation and entrepreneurship is the soul of entrepreneurship, and realizing the integration of innovation and entrepreneurship education and collaborative experiment platform in colleges and universities is an important way to promote the development of entrepreneurship and facilitate higher quality employment. In this paper, the authors propose the 3D-IVF intelligent sensor overlay algorithm wisdom laboratory system structure and application mode through in-depth investigation of collaborative laboratory, build the macroenvironment and construction demand of collaborative experiment manage

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14

Li, Jichang, Guanbin Li, Hui Cheng, Zicheng Liao, and Yizhou Yu. "FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3118–26. http://dx.doi.org/10.1609/aaai.v38i4.28095.

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Federated Learning with Noisy Labels (F-LNL) aims at seeking an optimal server model via collaborative distributed learning by aggregating multiple client models trained with local noisy or clean samples. On the basis of a federated learning framework, recent advances primarily adopt label noise filtering to separate clean samples from noisy ones on each client, thereby mitigating the negative impact of label noise. However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damagin

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15

Yan, Zhenya, Baoyu Zheng, Li Xu, and Shitang Li. "Collaborative tracking via particle filter in wireless sensor networks." Journal of Electronics (China) 25, no. 3 (2008): 311–18. http://dx.doi.org/10.1007/s11767-006-0277-0.

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16

Nugroho, RA, AM Polina, and YD Mahendra. "Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach." International Journal of Applied Sciences and Smart Technologies 2, no. 2 (2020): 119–26. http://dx.doi.org/10.24071/ijasst.v2i2.2987.

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Many people like traveling. But, often they are difficult to find a tourism site that they like much. Too many information about tourism is the problem. To overcome this problem, we need to filter the information. Recommender System could filter the information. By considering the advantages, the system used item-based collaborative filtering approach to give recommendation. Some tourism site around Yogyakarta province were used in this research. The system is able to give recommendation to users. The accuracy of the rating prediction is 0,6293 and the average time consumption is 1693,33 milli

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17

Pascacio, Pavel, Sven Casteleyn, Joaquín Torres-Sospedra, Elena Simona Lohan, and Jari Nurmi. "Collaborative Indoor Positioning Systems: A Systematic Review." Sensors 21, no. 3 (2021): 1002. http://dx.doi.org/10.3390/s21031002.

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84

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18

Zhang, Liu Mei, Jian Feng Ma, Di Lu, and Yi Chuan Wang. "Attribute Clustering Based Collaborative Filtering." Applied Mechanics and Materials 513-517 (February 2014): 965–68. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.965.

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The paper proposed an attribute clustering based collaborative filtering algorithm for recommendation. It utilizes similarity to filter out redundant attributes by feature selection. Then by incorporating K-Means clustering, it is able to effectively solve the rating scale problems existing in the traditional collaborative filtering recommendation algorithm. The algorithm is verified by real data sets. Experiments use location information for clustering the restaurant data. By integration of users rating on restaurant service and external impression the experiment study combined the collaborat

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19

Zhang, Jianming, Yang Liu, Hehua Liu, and Jin Wang. "Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection." Sensors 21, no. 4 (2021): 1129. http://dx.doi.org/10.3390/s21041129.

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Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targe

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20

Li, Yu, Kun Qian, Weiyan Shi, and Zhou Yu. "End-to-End Trainable Non-Collaborative Dialog System." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8293–302. http://dx.doi.org/10.1609/aaai.v34i05.6345.

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End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative d

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21

Ni, Peng, Bo Zhang, Yafei Song, and Mingliang Zhang. "Multisensor Distributed Dynamic Programming Method for Collaborative Warning and Tracking." Mathematical Problems in Engineering 2020 (May 15, 2020): 1–19. http://dx.doi.org/10.1155/2020/2818416.

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Multisensor distributed dynamic programming for collaborative warning and tracking during antimissile combat serves to meet the tracking accuracy requirements of all ballistic targets in the battlefield under the circumstance of a limited total amount of sensor resources. This paper proposes a method of multisensor distributed dynamic programming for collaborative warning and tracking based on game theory. First, starting from the target tracking algorithm, according to the characteristics of antimissile multisensor combat planning, the box particle filter (BPF) theory capable of distributed f

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22

Garcia-Fernandez, N., H. Alkhatib, and S. Schön. "COLLABORATIVE NAVIGATION SIMULATION TOOL USING KALMAN FILTER WITH IMPLICIT CONSTRAINTS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 559–66. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-559-2019.

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<p><strong>Abstract.</strong> Collaborative Positioning (CP) is a networked positioning technique in which different multi-sensor systems (nodes) enhance the accuracy and precision of these navigation solutions by performing measurements or by sharing information (links) between each other. The wide spectrum of available sensors that are used in these complex scenarios bring the necessity to analyze the sensibility of the system to different configurations in order to find optimal solutions. In this paper, we discuss the implementation and evaluation of a simulation tool that

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23

He, Ran, Guo Hui Song, and Shu Tao Sun. "Network Literature Recommendation Technology Based on User-Based Collaborative Filter." Advanced Materials Research 926-930 (May 2014): 2362–65. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2362.

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With the development of web2.0, network literature website has become an import carrier of publishing literature and getting novel. At present, the increasing of the number of network literature brings great confusion for audience. Therefore, network literature website need to recommend potential interested literature according to readers’ different requirements to take full advantage of resource and provide personalized services. The thesis we use the user-based collaborative algorithm to recommend readers for interested literature. We analysis the influence of k-value on recommend result and

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24

Barman, K., and O. Dabeer. "Analysis of a Collaborative Filter Based on Popularity Amongst Neighbors." IEEE Transactions on Information Theory 58, no. 12 (2012): 7110–34. http://dx.doi.org/10.1109/tit.2012.2216980.

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25

López Hernández, Fernando, Elena Verdú Pérez, J. Javier Rainer Granados, and Rubén González Crespo. "A Nondisturbing Service to Automatically Customize Notification Sending Using Implicit-Feedback." Scientific Programming 2019 (March 3, 2019): 1–17. http://dx.doi.org/10.1155/2019/1293194.

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This paper addresses the problem of automatically customizing the sending of notifications in a nondisturbing way, that is, by using only implicit-feedback. Then, we build a hybrid filter that combines text mining content filtering and collaborative filtering to predict the notifications that are most interesting for each user. The content-based filter clusters notifications to find content with topics for which the user has shown interest. The collaborative filter increases diversity by discovering new topics of interest for the user, because these are of interest to other users with similar

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26

Lai, Jinxiang, Wenlong Wu, Bin-Bin Gao, et al. "MatchDet: A Collaborative Framework for Image Matching and Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2858–65. http://dx.doi.org/10.1609/aaai.v38i3.28066.

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Image matching and object detection are two fundamental and challenging tasks, while many related applications consider them two individual tasks (i.e. task-individual). In this paper, a collaborative framework called MatchDet (i.e. task-collaborative) is proposed for image matching and object detection to obtain mutual improvements. To achieve the collaborative learning of the two tasks, we propose three novel modules, including a Weighted Spatial Attention Module (WSAM) for Detector, and Weighted Attention Module (WAM) and Box Filter for Matcher. Specifically, the WSAM highlights the foregro

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Wang, Gongtang, Fuyu Bo, Xue Chen, Wenfeng Lu, Shaohai Hu, and Jing Fang. "A Collaborative Despeckling Method for SAR Images Based on Texture Classification." Remote Sensing 14, no. 6 (2022): 1465. http://dx.doi.org/10.3390/rs14061465.

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Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle

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Hu, Bing, and Ning Hui He. "WSN Collaborative Target Tracking Based on Dynamic Clustering." Advanced Materials Research 712-715 (June 2013): 1868–71. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1868.

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The paper puts forward an algorithm base on the strategy of sensor nodes dynamic clustering which also combined the method of particle filter to track the target. The simulation shows that the algorithm efficiently reduces the amount of communication between the redundant nodes and sensor nodes, decreases the energy consumption of the entire network, extends the network life span and improves the tracking accuracy.

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Tang, Kuo-Hao, Chia-Feng Ho, Jan Mehlich, and Shih-Ting Chen. "Assessment of Handover Prediction Models in Estimation of Cycle Times for Manual Assembly Tasks in a Human–Robot Collaborative Environment." Applied Sciences 10, no. 2 (2020): 556. http://dx.doi.org/10.3390/app10020556.

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The accuracy and fluency of a handover task affects the work efficiency of human–robot collaboration. A precise and proactive estimation of handover time points by robots when handing over assembly parts to humans can minimize waiting times and maximize efficiency. This study investigated and compared the cycle time, waiting time, and operators’ subjective preference of a human–robot collaborative assembly task when three handover prediction models were applied: traditional method-time measurement (MTM), Kalman filter, and trigger sensor approaches. The scenarios of a general repetitive assemb

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Bo, Du, Lin Jian, Ou Li, Shi Yu, and MA Jin-yi. "Full Wave Simulation and Design of Film Bulk Acoustic Wave Filters." MATEC Web of Conferences 160 (2018): 01005. http://dx.doi.org/10.1051/matecconf/201816001005.

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In order to simulate the frequency response of thin film bulk acoustic wave filter accurately, the MBVD model of film bulk acoustic wave filter chip and three dimension electro-magnetic model of package were respectively established. And then, the collaborative simulation of acoustic and electro-magnetic components was realized in ADS software. Finally, thin film bulk acoustic wave filter with working frequency of 2.825GHz and 5.6 GHz were designed and fabricated by this method. Insertion loss of 2.825GHz FBAR filter is less than 1.5dB, and rejection is more than 40dB. Insertion loss of 5.6GHz

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31

Entis, Phyllis, J. Allen, A. Bhatnagar, et al. "Hydrophobic Grid Membrane Filter Method for Aerobic Plate Count in Foods: Collaborative Study." Journal of AOAC INTERNATIONAL 69, no. 4 (1986): 671–76. http://dx.doi.org/10.1093/jaoac/69.4.671.

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Abstract Twenty-one laboratories participated in a collaborative study to validate a hydrophobic grid membrane filter (HGMF) method for aerobic plate count by comparing its performance against the AOAC/APHA pour plate method. Raw milk, raw poultry, whole egg powder, flours, and spices were included in the study. Counts obtained by the HGMF and pour plate methods did not differ significantly, except in the case of whole egg powder, for which the HGMF method produced significantly higher counts. The hydrophobic grid membrane filter method for aerobic plate count in foods has been adopted officia

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32

Wang, Peng, Jing Yang, and Jianpei Zhang. "A Strategy toward Collaborative Filter Recommended Location Service for Privacy Protection." Sensors 18, no. 5 (2018): 1522. http://dx.doi.org/10.3390/s18051522.

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33

Zhang Chaoyi, 张超溢, 彭力 Peng Li, 贾天豪 Jia Tianhao, and 闻继伟 Wen Jiwei. "Multi-Filter Collaborative Tracking Algorithm Based on High-Confidence Updating Strategy." Laser & Optoelectronics Progress 56, no. 19 (2019): 191502. http://dx.doi.org/10.3788/lop56.191502.

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34

Ma, Wenming, Junfeng Shi, and Ruidong Zhao. "Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6562371.

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Item-based collaborative filter algorithms play an important role in modern commercial recommendation systems (RSs). To improve the recommendation performance, normalization is always used as a basic component for the predictor models. Among a lot of normalizing methods, subtracting the baseline predictor (BLP) is the most popular one. However, the BLP uses a statistical constant without considering the context. We found that slightly scaling the different components of the BLP separately could dramatically improve the performance. This paper proposed some normalization methods based on the sc

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35

Matsubara, Takashi, Masao Kubo, and Yusuke Murachi. "Particle Filter for Collaborative Multi-Robot Localization Tolerant to Recognition Error." Advanced Robotics 24, no. 15 (2010): 2043–58. http://dx.doi.org/10.1163/016918610x534259.

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36

Qiu, Jun-Wei, and Yu-Chee Tseng. "M2M Encountering: Collaborative Localization via Instant Inter-Particle Filter Data Fusion." IEEE Sensors Journal 16, no. 14 (2016): 5715–24. http://dx.doi.org/10.1109/jsen.2016.2566679.

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37

Rolando, Ludovica. "Unfolding the Concept of Flexibility in Collaborative Housing Design." ZARCH, no. 21 (December 31, 2023): 158–69. http://dx.doi.org/10.26754/ojs_zarch/zarch.2023218935.

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The article discusses the concept of flexibility in architecture and its application in collaborative housing projects. It explores three key connotations of flexibility - redundancy, technical means, and political strategy - as put forth by Adrian Forty. The design strategies for flexibility in collaborative housing include filter elements to create an intimacy gradient, redundant circulation spaces, programmatic genericness of spaces, and a wide variety of typologies and technical devices. The article also discusses the role of collective decision-making in collaborative housing design and t

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Singh, Raushan Kumar, Pradeep Kumar Singh, Juginder Pal Singh, Akhilesh Kumar Singh, and Seshathiri Dhanasekaran. "Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System." Applied Sciences 12, no. 22 (2022): 11686. http://dx.doi.org/10.3390/app122211686.

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The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm fo

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Okoniewski, Piotr, Rafał Osypiuk, and Jacek Piskorowski. "Short-Transient Discrete Time-Variant Filter Dedicated for Correction of the Dynamic Response of Force/Torque Sensors." Electronics 9, no. 8 (2020): 1291. http://dx.doi.org/10.3390/electronics9081291.

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The perception of touch opens new perspectives for so-called ’intelligent robotics’. Force/torque sensors are currently a key component of autonomous assembly processes or of the dynamically developing sector of collaborative robots. Response time is a critical parameter of force control, which has a direct effect on impact forces when the robot initiates contact with the environment. This paper indicates parameters of one of commercial force/torque sensors by JR3, in particular, its pre-defined low-pass filters. Their stationary nature introduces i.e. significant delay in the time domain, res

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40

Nie, Qiong. "Operation Fault Detection of Electrical and Mechanical Equipment Based on Kalman Filter." Journal of Physics: Conference Series 2717, no. 1 (2024): 012036. http://dx.doi.org/10.1088/1742-6596/2717/1/012036.

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Abstract In order to realize the remote collaborative diagnosis of electromechanical equipment fault in integrated energy system, a design method of remote collaborative diagnosis system of electromechanical equipment fault in integrated energy system based on Kalman filter is proposed. The hardware of the diagnosis system designed in this paper includes main control module, fault information parameter identification module, dynamic sensor acquisition module, fault signal filtering and detection module, adaptive control module, output interface module and so on. The collected fault information

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41

Entis, Phyllis. "Rapid Hydrophobic Grid Membrane Filter Method for Salmonella Detection in Selected Foods: Collaborative Study." Journal of AOAC INTERNATIONAL 68, no. 3 (1985): 555–64. http://dx.doi.org/10.1093/jaoac/68.3.555.

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Abstract A collaborative study was carried out in 36 laboratories to validate a hydrophobic grid membrane filter method for rapid (2-3 days) Salmonella detection by comparing its performance against the AOAC/ BAM reference method. Six products were included in the study: semisweet chocolate, raw ground poultry meat, ground black pepper, cheese powder, egg powder, and nonfat dry milk. With the exception of the naturally contaminated poultry, all products were inoculated in advance with low concentrations of a variety of Salmonella serotypes. The hydrophobic grid membrane filter method detected

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42

Lee, Jong Ki, Dorota A. Grejner-Brzezinska, and Charles Toth. "Network-based Collaborative Navigation in GPS-Denied Environment." Journal of Navigation 65, no. 3 (2012): 445–57. http://dx.doi.org/10.1017/s0373463312000069.

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Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning informati

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Lv, Taizhi, Juan Zhang, and Yong Chen. "A SLAM Algorithm Based on Edge-Cloud Collaborative Computing." Journal of Sensors 2022 (November 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/7213044.

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Simultaneous localization and mapping (SLAM) is a typical computing-intensive task. Based on its own computing power, a mobile robot has difficult meeting the real-time performance and accuracy requirements for the SLAM process at the same time. Benefiting from the rapid growth of the network data transmission rate, cloud computing technology begins to be applied in the robotics. There is the reliability problem caused by solely relying on cloud computing. To compensate for the insufficient airborne capacity, ensure the real-time performance and reliability, and improve the accuracy, a SLAM al

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Cai, Zhi, Jiahang Liu, Weijian Chi, and Bo Zhang. "A Low-Cost and Robust Multi-Sensor Data Fusion Scheme for Heterogeneous Multi-Robot Cooperative Positioning in Indoor Environments." Remote Sensing 15, no. 23 (2023): 5584. http://dx.doi.org/10.3390/rs15235584.

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The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision positioning is a major challenge. However, due to the comprehensive impact of factors such as poor performance, variety, complex calculations, and accumulation of environmental errors used by commercial robots, the difficulty of high-precision collaborative positioning is further exacer

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Yang, Feng, Yongqi Wang, Hao Chen, Pengyan Zhang, and Yan Liang. "Adaptive Collaborative Gaussian Mixture Probability Hypothesis Density Filter for Multi-Target Tracking." Sensors 16, no. 10 (2016): 1666. http://dx.doi.org/10.3390/s16101666.

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Liu, Zhi, Shuai Wang, and Mengmeng Zhang. "Improved Sparse 3D Transform-Domain Collaborative Filter for Screen Content Image Denoising." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 03 (2017): 1854006. http://dx.doi.org/10.1142/s021800141854006x.

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Screen content videos or images are widely used in applications such as screen sharing. Compressed screen content videos or images may have distortions or noises because of the quantization process. This paper proposes an improved sparse 3D transform-domain collaborative filter to enhance screen content image quality by block classification and block segmentation. Experimental results show that the proposed algorithm achieves a peak signal-to-noise ratio increase and subjective visual quality improvements for reconstructed screen content images.

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Latré, Steven, Jeroen Famaey, John Strassner, and Filip De Turck. "Automated context dissemination for autonomic collaborative networks through semantic subscription filter generation." Journal of Network and Computer Applications 36, no. 6 (2013): 1405–17. http://dx.doi.org/10.1016/j.jnca.2013.01.011.

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Meng, Xin-Jia, Shi-Kai Jing, Li-Xiang Zhang, Ji-Hong Liu, and Hai-Cheng Yang. "Linear approximation filter strategy for collaborative optimization with combination of linear approximations." Structural and Multidisciplinary Optimization 53, no. 1 (2015): 49–66. http://dx.doi.org/10.1007/s00158-015-1303-3.

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Wibowo, Suryo Adhi, Hansoo Lee, Eun Kyeong Kim, and Sungshin Kim. "Collaborative Learning based on Convolutional Features and Correlation Filter for Visual Tracking." International Journal of Control, Automation and Systems 16, no. 1 (2018): 335–49. http://dx.doi.org/10.1007/s12555-017-0062-x.

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Zhao, Shujie, Lijun Zhang, and Wenxia Lu. "A generalized collaborative functional link adaptive filter for nonlinear active noise control." Applied Acoustics 175 (April 2021): 107799. http://dx.doi.org/10.1016/j.apacoust.2020.107799.

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