Academic literature on the topic 'LCA algorthm'

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Journal articles on the topic "LCA algorthm"

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Salwa, Khalid Abdulateef, Naser Abdali Taj-Aldeen, Dawood Salman Alroomi Mohanad, and Aktham Ahmed Altaha Mohamed. "An optimise ELM by league championship algorithm based on food images." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 132–37. https://doi.org/10.11591/ijeecs.v20.i1.pp132-137.

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This paper presents an optimisation of extreme learning machine by league championship algorithm based on food images. Extreme Learning Machine (ELM) is an effective classifier because of the performance which is higher than other classifiers’ aspects. However, some important drawbacks still work as a hindrance like failure of optimal selection weights for the weights of the input-hidden layer and the output of the threshold. In spite of the wide number of problem-solving attempts, there was no solution to be considered effective. This paper presents the approach of hybrid learning and t
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Liu, Yuan, Haiquan Wang, and Xiguang Zhang. "Improved Synthetic Weighted Algorithm of Ontology-Based Semantic Similarity Computation." Journal of Robotics and Mechatronics 30, no. 6 (2018): 958–64. http://dx.doi.org/10.20965/jrm.2018.p0958.

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To solve the problems of incomplete consideration and low precision in existing domain ontology semantic similarity computation, an improved synthetic weighted algorithm of ontology-based semantic similarity computation is proposed, mixing path coincidence degree, the shortest distance, and concept property methods. First, the depths of lowest common ancestor (LCA) and an ontology tree are added to the formula of path coincidence degree for distinguishing the influence of LCA depth on similarity when multiple inheritances occur. Second, the analysis of similarity algorithm based on the shortes
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Kolovský, František, Jan Ježek та Ivana Kolingerová. "The ε-Approximation of the Time-Dependent Shortest Path Problem Solution for All Departure Times". ISPRS International Journal of Geo-Information 8, № 12 (2019): 538. http://dx.doi.org/10.3390/ijgi8120538.

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In this paper, the shortest paths search for all departure times (profile search) are discussed. This problem is called a time-dependent shortest path problem (TDSP) and is suitable for time-dependent travel-time analysis. Particularly, this paper deals with the ε -approximation of profile search computation. The proposed algorithms are based on a label correcting modification of Dijkstra’s algorithm (LCA). The main idea of the algorithm is to simplify the arrival function after every relaxation step so that the maximum relative error is maintained. When the maximum relative error is 0.001, th
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Rozell, Christopher J., Don H. Johnson, Richard G. Baraniuk, and Bruno A. Olshausen. "Sparse Coding via Thresholding and Local Competition in Neural Circuits." Neural Computation 20, no. 10 (2008): 2526–63. http://dx.doi.org/10.1162/neco.2008.03-07-486.

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While evidence indicates that neural systems may be employing sparse approximations to represent sensed stimuli, the mechanisms underlying this ability are not understood. We describe a locally competitive algorithm (LCA) that solves a collection of sparse coding principles minimizing a weighted combination of mean-squared error and a coefficient cost function. LCAs are designed to be implemented in a dynamical system composed of many neuron-like elements operating in parallel. These algorithms use thresholding functions to induce local (usually one-way) inhibitory competitions between nodes t
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Fu, Yu Juan, Yu Long Zhang, Huan Jie Cai, Jun Shi He, and Yin Hong Kang. "Application of Line-up Competition Algorithm with Variable Weight for Tree Pipe Network Layout Optimization." Applied Mechanics and Materials 143-144 (December 2011): 869–75. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.869.

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As a problem of network flows, pipe network layout optimization is a combinatorial problem with variable weight. This paper takes flow variation by different layout scheme into account to the pipeline optimization and obtains the irrigation pipe network layout optimization with variable weight. Line-up competition algorithm (LCA) is introduced to optimize the tree pipe network layout. The corresponding fitness function, encoded mode and mutation are designed on the basis of the characteristic of tree graph expressed by adjacency matrix. It is testified by case study that LCA can obtain the opt
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Moayedi, Bui, and Thi Ngo. "Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils." Applied Sciences 9, no. 23 (2019): 5264. http://dx.doi.org/10.3390/app9235264.

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This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is comp
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Yanto, Iwan Tri Riyadi, Ririn Setiyowati, Nursyiva Irsalinda, Rasyidah, and Tri Lestari. "Laying Chicken Algorithm (LCA) Based For Clustering." JOIV : International Journal on Informatics Visualization 4, no. 4 (2020): 208. http://dx.doi.org/10.30630/joiv.4.4.467.

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Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.
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Moayedi, Hossein, and Amir Mosavi. "Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings." Sustainability 13, no. 6 (2021): 3198. http://dx.doi.org/10.3390/su13063198.

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The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the AL
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Zhang, Xiaoqing, Yinsu Lou, Sunhao Hu, and Dan Zhu. "Backpropagation Neural Network Algorithm-Based Color Doppler Ultrasound Detection of Gestational Diabetes Mellitus and Perinatal Outcomes." Scientific Programming 2021 (July 19, 2021): 1–8. http://dx.doi.org/10.1155/2021/4258360.

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In this work, the related risk factors and perinatal outcomes of pregnant women with gestational diabetes mellitus (GDM) were analyzed based on color Doppler ultrasound (CDU) diagnosis. Backpropagation (BP) algorithm-based CDU imaging algorithm (BPC) was constructed in this study and applied in CDU images of 80 pregnant women with GDM. Besides, amplitude and phase estimation (APES) and low-complexity adaptive beam (LCA) algorithms were introduced for comparison with BPC in turn. It was found that Dice similarity coefficient (96.44%), sensitivity (95.45%), and specificity (91.56%) of BPC were g
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Park, Seohee, Seongeun Kim, and Ji Hoon Ryoo. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis." Mathematics 8, no. 11 (2020): 2076. http://dx.doi.org/10.3390/math8112076.

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Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approa
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Dissertations / Theses on the topic "LCA algorthm"

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Balavoine, Aurèle. "Implementation of the locally competitive algorithm on a field programmable analog array." Thesis, Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37255.

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Sparse approximation is an important class of optimization problem in signal and image processing applications. This thesis presents an analog solution to this problem, based on the Locally Competitive Algorithm (LCA). A Hopfield-Network-like analog system, operating on sub-threshold currents is proposed as a solution. The results of the circuit components' implementation on the RASP2.8a chip, a Field Programmable Analog Array, are presented.
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Perez, Gallardo Jorge Raúl. "Ecodesign of large-scale photovoltaic (PV) systems with multi-objective optimization and Life-Cycle Assessment (LCA)." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10505/1/perez_gallardo_partie_1_sur_2.pdf.

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Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environm
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Patel, Chandankumar Johakhim. "A Performance Analysis Framework for Coreference Resolution Algorithms." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1471954403.

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Gemayel, Pierre. "Optique adaptative par modulateur spatial de lumière en microscopie et holographie." Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH9294/document.

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Depuis les années 50, la récupération de la phase d’un faisceau optique diffracté par un objet quelconque, est un sujet important dans plusieurs domaines scientifiques, comme la microscopie, l’astronomie et bien d’autres. Généralement, les méthodes qui le permettent se divisent en deux grandes catégories : les méthodes interférométriques et les méthodes itératives basées sur la propagation du faisceau. L’intérêt de ces dernières, réside dans le fait qu’elles sont moins sensibles au bruit, et leur implémentation expérimentale est plus simple. Aussi, le développement des techniques informatiques
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Alsadhan, Majed. "An application of topic modeling algorithms to text analytics in business intelligence." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17580.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>William H. Hsu<br>In this work, we focus on the task of clustering businesses in the state of Kansas based on the content of their websites and their business listing information. Our goal is to cluster the businesses and overcome the challenges facing current approaches such as: data noise, low number of clustered businesses, and lack of evaluation approach. We propose an LSA-based approach to analyze the businesses’ data and cluster those businesses by using Bisecting K-Means algorithm. In this approach
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Lohier, Stéphane. "Approches inter-couches pour la qualité de servive dans les réseaux locaux sans fil." Paris 6, 2006. http://www.theses.fr/2006PA066121.

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Jaradat, Shatha. "OLLDA: Dynamic and Scalable Topic Modelling for Twitter : AN ONLINE SUPERVISED LATENT DIRICHLET ALLOCATION ALGORITHM." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177535.

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Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new docume
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Shapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.

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Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to
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Chouquet, Julie. "Development of a method for building life cycle analysis at an early design phase Implementation in a tool - Sensitivity and uncertainty of such a method in comparison to detailed LCA software = Calibration of new flavor tagging algorithms using Bs oscillations /." [S.l. : s.n.], 2007. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000009290.

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Wang, Xuechuan, and n/a. "Feature Extraction and Dimensionality Reduction in Pattern Recognition and Their Application in Speech Recognition." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030619.162803.

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Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Princi
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Books on the topic "LCA algorthm"

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures =: Evolution Programs. Springer Berlin Heidelberg, 1994.

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Van Landingham, Hugh F., 1935-, ed. Algorithms for computer-aided design of multivariable control systems. M. Dekker, 1993.

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Tomasz, Neumann, ed. Methods and algorithms in navigation: Marine navigation and safety of sea transportation. CRC Press, 2011.

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Michalewicz, Zbigniew. Algorytmy genetyczne + struktury danych = programy ewolucyjne. 3rd ed. Wydawnictwa Naukowo-Techniczne, 2003.

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A, Vasuki. Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2020.

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A, Vasuki. Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2020.

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A, Vasuki. Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2022.

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A, Vasuki. Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2020.

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Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2020.

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Bayesian phylogenetics: Methods, algorithms, and applications. CRC Press/Taylor & Francis, 2014.

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Book chapters on the topic "LCA algorthm"

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Hu, Ming, Chaoli Wang, Siavash Ghorbany, Siyuan Yao, and Ali Nouri. "Machine Learning Integration in LCA: Addressing Data Deficiencies in Embodied Carbon Assessment." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_78.

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AbstractLife Cycle Assessment (LCA) is an essential tool for quantifying the environmental burdens of products and processes, critical for advancing sustainability goals. Central to the effectiveness of LCA is the Life Cycle Inventory (LCI) phase, which requires reliable data to reflect the environmental footprint of products accurately. However, LCA practitioners often encounter data gaps that can compromise the assessment’s accuracy. To address this, we explore the integration of Machine Learning (ML) to enhance LCA data quality, particularly in the LCI stages B to D, which focus on product
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Rezaei, Hossein, Omid Bozorg-Haddad, and Xuefeng Chu. "League Championship Algorithm (LCA)." In Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_3.

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Amir, Amihood, Alberto Apostolico, Gad M. Landau, Avivit Levy, Moshe Lewenstein, and Ely Porat. "Range LCP." In Algorithms and Computation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25591-5_70.

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Feichtinger, Hans G., and Werner Kozek. "Quantization of TF lattice-invariant operators on elementary LCA groups." In Gabor Analysis and Algorithms. Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2016-9_8.

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Kärkkäinen, Juha, and Dominik Kempa. "LCP Array Construction in External Memory." In Experimental Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07959-2_35.

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Manzini, Giovanni. "Two Space Saving Tricks for Linear Time LCP Array Computation." In Algorithm Theory - SWAT 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27810-8_32.

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Grandoni, Fabrizio, Giuseppe F. Italian, Aleksander Łukasiewicz, Nikos Parotsidis, and Przemysław Uznański. "All-Pairs LCA in DAGs: Breaking through the O(n2.5) barrier." In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976465.18.

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Gog, Simon, and Enno Ohlebusch. "Fast and Lightweight LCP-Array Construction Algorithms." In 2011 Proceedings of the Thirteenth Workshop on Algorithm Engineering and Experiments (ALENEX). Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611972917.3.

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Sohn, Sang-Wook, Dae-Young Lee, Hun Choi, Jae-Won Suh, and Hyeon-Deok Bae. "Detection of Various Defects in TFT-LCD Polarizing Film." In Adaptive and Natural Computing Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71629-7_60.

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Egidi, Lavinia, and Giovanni Manzini. "Lightweight BWT and LCP Merging via the Gap Algorithm." In String Processing and Information Retrieval. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67428-5_15.

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Conference papers on the topic "LCA algorthm"

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Li, Shuangshuang. "LCA-YOLOv8: lightweight object detection with CCFF and ACMix for safety helmets." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049620.

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Zhang, Xintong, He Wang, and Huajun Shi. "Automatic Radar Modulation Recognition Using CRLNet with Rotary Encoding and LCA Mechanism." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019967.

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Aboagye, Emmanuel A., Austin L. Lehr, Ethan Shumaker, et al. "Machine Learning Methods for the Forecasting of Environmental Impacts in Early-stage Process Design." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.141240.

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Initial design stages are inherently complex and often lack comprehensive information, posing challenges in evaluating sustainability metrics. Machine Learning (ML) emerges as a valuable solution to address these challenges. ML algorithms, particularly effective in predicting environmental impacts of new chemicals with limited data, enable more informed decisions in sustainable design. This study focuses on employing ML for predicting the environmental impacts related to human health, ecosystem quality, climate change, and resource utilization to aid in early-stage environmental impact assessm
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Wen, Bo, Zhongwen Shen, and Jianjiang Zhou. "Optimized LCD local dimming algorithm with multi partitions." In Optical Design and Testing XIV, edited by Rengmao Wu, Yongtian Wang, and Tina E. Kidger. SPIE, 2024. http://dx.doi.org/10.1117/12.3042569.

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Zhang, Yu. "Research on switching hybrid wireless charging system for electric vehicles based on S-S and LCC-S compensation topology." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039363.

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Intravaia, Matteo, Lorenzo Becchi, Marco Bindi, et al. "LCC-S Compensated Wireless Power Transfer: System Optimization Using Genetic Algorithms." In 2024 International Conference on Electrical Machines (ICEM). IEEE, 2024. http://dx.doi.org/10.1109/icem60801.2024.10700581.

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Qi, Donghao, Zhiqian Wang, Yue Zou, Tao Zhang, Bohao Tang, and Shuai Yang. "Target Detection Algorithm in The Range Measurement Based on YOLO-Improved." In 2024 Light Conference (LCW). IEEE, 2024. https://doi.org/10.1109/lcw63366.2024.10776594.

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Zhao, Mengchen, Bo An, Wei Gao, and Teng Zhang. "Efficient Label Contamination Attacks Against Black-Box Learning Models." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/551.

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Label contamination attack (LCA) is an important type of data poisoning attack where an attacker manipulates the labels of training data to make the learned model beneficial to him. Existing work on LCA assumes that the attacker has full knowledge of the victim learning model, whereas the victim model is usually a black-box to the attacker. In this paper, we develop a Projected Gradient Ascent (PGA) algorithm to compute LCAs on a family of empirical risk minimizations and show that an attack on one victim model can also be effective on other victim models. This makes it possible that the attac
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Ruzhnikov, Alexey, Valerian Guillot, and Mauricio Corona. "Real-Time Evaluation of the Lost Circulation Zone Based on a Changepoint Analysis of the Mechanical Specific Energy and Standpipe Pressure." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24471-ms.

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Abstract There are multiple mechanisms that may drive a lost circulation event while drilling a well. Efficiently dealing with those events requires that the operations team quickly get an understanding of subsurface conditions that caused the event. Globally, some lost circulation events are cured by basic lost circulation materials (LCM) / bridging, while others fail to be cured even after days of attempts with lost circulation cement plugs (LCP) and novel materials. The aperture of the lost circulation zone (LCZ) remains unknow in most of the cases unless open hole logs ran to identify it.
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Ma, Jungmok, and Harrison M. Kim. "Predictive Usage Mining for Sustainability of Complex Systems Design." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34755.

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A new perspective of dynamic LCA (life cycle assessment) is proposed with the predictive usage mining for sustainability (PUMS) algorithm. By defining usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon. Large-scale sensor data of product operation is analyzed in order to mine usage patterns and build a usage model for LCA. The PUMS algorithm consists of handling missing and abnormal values, seasonal period analysis, segmentation analysis, time series analysis, and predictive LCA. A newly developed segm
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Reports on the topic "LCA algorthm"

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Peñaloza, Rafael, and Anni-Yasmin Turhan. Completion-based computation of least common subsumers with limited role-depth for EL and Prob-EL⁰¹. Technische Universität Dresden, 2010. http://dx.doi.org/10.25368/2022.175.

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The least common subsumer (lcs) w.r.t general EL-TBoxes does not need to exists in general due to cyclic axioms. In this report we present an algorithm for computing role-depth bounded EL-lcs based on the completion algorithm for EL. We extend this computation algorithm to a recently introduced probabilistic variant of EL: Prob-EL⁰¹.
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SALINGER, ANDREW G., NAWAF M. BOU-RABEE, ELIZABETH A. BURROUGHS, et al. LOCA 1.0 Library of Continuation Algorithms: Theory and Implementation Manual. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/800778.

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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for nu
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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for nu
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Ariza Nieto, C., B. Mojica, O. L. Mayorga, et al. Characterization of forage resources of Colombian highlands grazing systems using LOCAL algorithm with near-infrared spectroscopy. Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, 2016. http://dx.doi.org/10.21930/agrosavia.poster.2016.54.

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Las fracciones de fibra y el contenido de proteína son características químicas importantes de los recursos de forraje debido a su estrecha relación con los factores que afectan el rendimiento de las vacas lecheras. Avances en metodologías para evaluar esas características y el uso de métodos indirectos como el la espectroscopia de infrarrojo cercano (NIRS) promueve la caracterización química del forraje como un elemento fundamental, y una herramienta para evaluar la eficiencia de la producción de leche bajo condiciones específicas en la producción.
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Rieu, Pierre, Samira Amraoui, and Marco Restano. Standalone Multi-mission Altimetry Processor (SMAP). European Space Agency, 2021. http://dx.doi.org/10.5270/esa-cnes.sentinel-3.smap.

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SMAP is a standalone altimeter data processor written in Python 3 (3.7.3). It implements in particular the fully-focused SAR (FF-SAR) processing (both time-domain and frequency-domain algorithms). SMAP is currently able to process Sentinel-3 L1a Ground Segment products. This processor has been developed though studies and projects funded by ESA and CNES.
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Tenenbaum, P. Beam-Based Alignment of the NLC Main Linac, Part One: Single-Bunch Comparative Study of Three Algorithms(LCC-0013). Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/826898.

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Brandt, Sebastian, Anni-Yasmin Turhan, and Ralf Küsters. Foundations of non-standard inferences for DLs with transitive roles. Technische Universität Dresden, 2003. http://dx.doi.org/10.25368/2022.127.

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Description Logics (DLs) are a family of knowledge representation formalisms used for terminological reasoning. They have a wide range of applications such as medical knowledge-bases, or the semantic web. Research on DLs has been focused on the development of sound and complete inference algorithms to decide satisfiability and subsumption for increasingly expressive DLs. Non-standard inferences are a group of relatively new inference services which provide reasoning support for the building, maintaining, and deployment of DL knowledge-bases. So far, non-standard inferences are not available fo
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Hong, G., R. A. Fernandes, L. Sun, and N. Djamai. Monthly Vegetation Essential Climate Variable Maps for the Hudson Bay Lowland using the LEAF-Toolbox Implementation of the SL2P Algorithm. Natural Resources Canada/CMSS/Information Management, 2025. https://doi.org/10.4095/pbvvgcknc5.

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L'indice de surface foliaire (LAI), la fraction du couvert végétal (fCOVER) et la fraction du rayonnement photosynthétiquement actif absorbé (FAPAR) sont des variables climatiques essentielles et largement utilisées dans les activités de surveillance, de compréhension et de modélisation liées aux surfaces terrestres. Landscape Evolution and Forecasting (LEAF), qui comprend l'algorithme Simplified Level 2 Product Prototype Processor (SL2P), offre un moyen efficace de produire des paramètres biophysiques au niveau régional à l'aide de Sentinel 2 ou de Landsat 8. Cette étude applique LEAF pour pr
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Küsters, Ralf, and Alex Borgida. What's in an Attribute? Consequences for the Least Common Subsumer. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.102.

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Functional relationships between objects, called 'attributes', are of considerable importance in knowledge representation languages, including Description Logics (DLs). A study of the literature indicates that papers have made, often implicity, different assumptions about the nature of attributes: whether they are always required to have a value, or whether they can be partial functions. The work presented here is the first explicit study of this difference for (sub-)classes of the CLASSIC DL, involving the same-as concept constructor. It is shown that although determining subsumption between
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