Relevant bibliographies by topics / Golden Section Search

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Golden Section Search'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Golden Section Search"

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Luo, Yiping, Jinhao Meng, Defa Wang, and Guobin Xue. "New One-Dimensional Search Iteration Algorithm and Engineering Application." Shock and Vibration 2021 (November 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/7643555.

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In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one-dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one-dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one-dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T-section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one-dimensional search optimization problem.
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Chakraborty, Suvra Kanti, and Geetanjali Panda. "Golden section search over hyper-rectangle: a direct search method." International Journal of Mathematics in Operational Research 8, no. 3 (2016): 279. http://dx.doi.org/10.1504/ijmor.2016.075517.

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Pronzato, L. "A generalized golden-section algorithm for line search." IMA Journal of Mathematical Control and Information 15, no. 2 (June 1, 1998): 185–214. http://dx.doi.org/10.1093/imamci/15.2.185.

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Jones, David, and Robert D. Grisso. "Golden section search as an optimization tool for spreadsheets." Computers and Electronics in Agriculture 7, no. 4 (December 1992): 323–35. http://dx.doi.org/10.1016/s0168-1699(05)80013-4.

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Kurdhi, Nughthoh Arfawi, Winita Sulandari, Titin Sri Martini, Hartatik, and Yudho Yudhanto. "GOLDEN SECTION SEARCH OPTIMIZATION TECHNIQUE FOR STOCHASTIC INVENTORY PROBLEM." Far East Journal of Mathematical Sciences (FJMS) 99, no. 2 (December 31, 2015): 205–20. http://dx.doi.org/10.17654/ms099020205.

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Couriol, Catherine, Catherine Porte, and Henri Fauduet. "Optimization of glycine crystallization by the golden section search." Process Control and Quality 11, no. 1 (January 1, 1998): 13–21. http://dx.doi.org/10.1163/156856698750246958.

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Vieira, Douglas A. G., Ricardo H. C. Takahashi, and Rodney R. Saldanha. "Multicriteria optimization with a multiobjective golden section line search." Mathematical Programming 131, no. 1-2 (April 17, 2010): 131–61. http://dx.doi.org/10.1007/s10107-010-0347-9.

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Loxton, Ryan, and Qun Lin. "Optimal fleet composition via dynamic programming and golden section search." Journal of Industrial & Management Optimization 7, no. 4 (2011): 875–90. http://dx.doi.org/10.3934/jimo.2011.7.875.

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Yazıcı, İrfan, Ersagun Kürşat Yaylacı, and Faruk Yalçın. "Modified golden section search based MPPT algorithm for the WECS." Engineering Science and Technology, an International Journal 24, no. 5 (October 2021): 1123–33. http://dx.doi.org/10.1016/j.jestch.2021.02.006.

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Panda, Srikumar, and Ranjan Das. "A golden section search method for the identification of skin subsurface abnormalities." Inverse Problems in Science and Engineering 26, no. 2 (April 9, 2017): 183–202. http://dx.doi.org/10.1080/17415977.2017.1310857.

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Dissertations / Theses on the topic "Golden Section Search"

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Sinkus, Skirmantas. "Kinect įrenginiui skirtų gestų atpažinimo algoritmų tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140806_143213-09689.

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Microsoft Kinect įrenginys išleistas tik 2010 metais. Jis buvo skirtas Microsoft Xbox 360 vaizdo žaidimų konsolei, vėliau 2012 metais buvo pristatytas Kinect ir Windows personaliniams kompiuteriams. Taigi tai palyginus naujas įrenginys ir aktualus šiai dienai. Daugiausiai yra sukurta kompiuterinių žaidimų, kurie naudoja Microsoft Kinect įrenginį, bet šį įrenginį galima panaudoti daug plačiau ne tik žaidimuose, viena iš sričių tai sportas, konkrečiau treniruotės, kurias būtų galima atlikti namuose. Šiuo metu pasaulyje yra programinės įrangos, žaidimų, sportavimo programų, kuri leidžia kontroliuoti treniruočių eigą sekdama ar žmogus teisingai atlieka treniruotėms numatytus judesius. Kadangi Lietuvoje panašios programinės įrangos nėra, taigi reikia sukurti įrangą, kuri leistų Lietuvos treneriams kurti treniruotes orientuotas į šio įrenginio panaudojimą. Šio darbo pagrindinis tikslas yra atlikti Kinect įrenginiui skirtų gestų atpažinimo algoritmų tyrimą, kaip tiksliai jie gali atpažinti gestus ar gestą. Pagrindinis dėmesys skiriamas šiai problemai, taip pat keliami, bet netyrinėjami kriterijai kaip atpažinimo laikas, bei realizacijos sunkumas. Šiame darbe sukurta programa, judesius bei gestus atpažįsta naudojant Golden Section Search algoritmą. Algoritmas palygina du modelius ar šablonus, ir jei neranda atitikmens, tai pirmasis šablonas šiek tiek pasukamas ir lyginimo procesas paleidžiamas vėl, taipogi tam tikro kintamojo dėka galime keisti algoritmo tikslumą. Taipogi... [toliau žr. visą tekstą]
Microsoft Kinect device was released in 2010. It was designed for Microsoft Xbox 360 gaming console, later on in 2012 was presented Kinect device for Windows personal computer. So this device is new and current. Many games has been created for Microsoft Kinect device, but this device could be used not only in games, one of the areas where we can use it its sport, specific training, which can be performed at home. At this moment in world are huge variety of games, software, training programs which allows user to control training course by following a person properly perform training provided movements. Since in Lithuania similar software is not available, so it is necessary to create software that would allow Lithuania coaches create training focused on the use of this device. The main goal of this work is to perform research of the Kinect device gesture recognition algorithms to study exactly how they can recognize gestures or gesture. It will focus on this issue mainly, but does not address the criteria for recognition as the time and difficulty of realization. In this paper, a program that recognizes movements and gestures are using the Golden section search algorithm. Algorhithm compares the two models or templates, and if it can not find a match, this is the first template slightly rotated and comparison process is started again, also a certain variable helping, we can modify the algorithm accuracy. Also for comparison we can use Hidden Markov models algorhithm received... [to full text]
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Puranam, Muthukumar B. "Towards Full-Body Gesture Analysis and Recognition." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/227.

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With computers being embedded in every walk of our life, there is an increasing demand forintuitive devices for human-computer interaction. As human beings use gestures as importantmeans of communication, devices based on gesture recognition systems will be effective for humaninteraction with computers. However, it is very important to keep such a system as non-intrusive aspossible, to reduce the limitations of interactions. Designing such non-intrusive, intuitive, camerabasedreal-time gesture recognition system has been an active area of research research in the fieldof computer vision.Gesture recognition invariably involves tracking body parts. We find many research works intracking body parts like eyes, lips, face etc. However, there is relatively little work being done onfull body tracking. Full-body tracking is difficult because it is expensive to model the full-body aseither 2D or 3D model and to track its movements.In this work, we propose a monocular gesture recognition system that focuses on recognizing a setof arm movements commonly used to direct traffic, guiding aircraft landing and for communicationover long distances. This is an attempt towards implementing gesture recognition systems thatrequire full body tracking, for e.g. an automated recognition semaphore flag signaling system.We have implemented a robust full-body tracking system, which forms the backbone of ourgesture analyzer. The tracker makes use of two dimensional link-joint (LJ) model, which representsthe human body, for tracking. Currently, we track the movements of the arms in a video sequence,however we have future plans to make the system real-time. We use distance transform techniquesto track the movements by fitting the parameters of LJ model in every frames of the video captured.The tracker's output is fed a to state-machine which identifies the gestures made. We haveimplemented this system using four sub-systems. Namely1. Background subtraction sub-system, using Gaussian models and median filters.2. Full-body Tracker, using L-J Model APIs3. Quantizer, that converts tracker's output into defined alphabets4. Gesture analyzer, that reads the alphabets into action performed.Currently, our gesture vocabulary contains gestures involving arms moving up and down which canbe used for detecting semaphore, flag signaling system. Also we can detect gestures like clappingand waving of arms.
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Castelo, Branco César Augusto Santana. "Algoritmos adaptativos LMS normalizados proporcionais: proposta de novos algoritmos para identificação de plantas esparsas." Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/1688.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-23T20:42:44Z No. of bitstreams: 1 CesarCasteloBranco.pdf: 11257769 bytes, checksum: 911c33f2f0ba5c1c0948888e713724f6 (MD5)
Made available in DSpace on 2017-06-23T20:42:44Z (GMT). No. of bitstreams: 1 CesarCasteloBranco.pdf: 11257769 bytes, checksum: 911c33f2f0ba5c1c0948888e713724f6 (MD5) Previous issue date: 2016-12-12
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ)
This work proposes new methodologies to optimize the choice of the parameters of the proportionate normalized least-mean-square (PNLMS) adaptive algorithms. The proposed approaches use procedures based on two optimization methods, namely, the golden section and tabu search methods. Such procedures are applied to determine the optimal parameters in each iteration of the adaptation process of the PNLMS and improved PNLMS (IPNLMS) algorithms. The objective function for the proposed procedures is based on the a posteriori estimation error. Performance studies carried out to evaluate the impact of the PNLMS and IPNLMS parameters in the behavior of these algorithms shows that, with the aid of optimization techniques to choose properly such parameters, the performance of these algorithms may be improved in terms of convergence speed for the identification of plants with high sparseness degree. The main goal of the proposed methodologies is to improve the distribution of the adaptation energy between the coefficients of the PNLMS and IPNLMS algorithms, using parameter values that lead to the minimal estimation error of each iteration of the adaptation process. Numerical tests performed (considering various scenarios in which the plant impulse response is sparse) show that the proposed methodologies achieve convergence speeds faster than the PNLMS and IPNLMS algorithms, and other algorithms of the PNLMS class, such as the sparseness controlled IPNLMS (SC-IPNLMS) algorithm.
Neste trabalho, novas metodologias para otimizar a escolha dos parâmetros dos algoritmos adaptativos LMS normalizados proporcionais (PNLMS) são propostas. As abordagens propostas usam procedimentos baseados em dois métodos de otimização, a saber, os métodos da razão áurea e da busca tabu. Tais procedimentos são empregados para determinar os parâmetros ótimos em cada iteração do processo de adaptação dos algoritmos PNLMS e PNLMS melhorado (IPNLMS). A função objetivo adotada pelos procedimentos propostos é baseada no erro de estimação a posteriori. O estudo de desempenho realizado para avaliar o impacto dos parâmetros dos algoritmos PNLMS e IPNLMS no comportamento dos mesmos mostram que, com o auxílio de técnicas de otimização para escolher adequadamente tais parâmetros, o desempenho destes algoritmos pode ser melhorado, em termos de velocidade de convergência, para a identificação de plantas com elevado grau de esparsidade. O principal objetivo das metodologias propostas é melhorar a distribuição da energia de ativação entre os coeficientes dos algoritmos PNLMS e IPNLMS, usando valores de parâmetros que levam ao erro de estimação mínimo em cada iteração do processo de adaptação. Testes numéricos realizados (considerando diversos cenários nos quais a resposta impulsiva da planta é esparsa) mostram que as metodologias propostas alcançam velocidades de convergência superiores às dos algoritmos PNLMS e IPNLMS, além de outros algoritmos da classe PNLMS, tais como o algoritmo IPNLMS com controle de esparsidade (SCIPNLMS).
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NUGRAHA, DIMAS AJI, and DIMAS AJI NUGRAHA. "A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4hr2yd.

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碩士
國立臺灣科技大學
電機工程系
107
Partial shading is a common and difficult problem to be solved in a photovoltaic (PV) system. Numerous efforts have been introduced to mitigate this problem. Some commonly used approaches are deploying some meta-heuristic (MH) algorithm to track the multiple peak P - V curve of partially shaded PV system. Cuckoo Search (CS) is a new optimization algorithm based on MH approach. It has been used to solve optimization problems in many applications including Maximum Power Point Tracking (MPPT) problem. CS algorithm performs well in tracking the Global Maximum Power Point (GMPP). However, just like any other MH algorithms, there is still a dilemmatic trading between their accuracy and the tracking time needed to find Global Maximum Power Point (GMPP). This thesis proposes a new MPPT algorithm by combining CS algorithm with Golden Section Search (GSS) to take beneficial features from both algorithms. To validate the proposed algorithm, it is evaluated with various cases of partial shading. The simulation and experimental result show a noticeable performance improvement compared to original CS algorithm and other MH algorithms.
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Sakla, Wesam Adel. "Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7526.

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A fundamental challenge in target detection in hyperspectral imagery is spectral variability. In target detection applications, we are provided with a pure target signature; we do not have a collection of samples that characterize the spectral variability of the target. Another problem is that the performance of stochastic detection algorithms such as the spectral matched filter can be detrimentally affected by the assumptions of multivariate normality of the data, which are often violated in practical situations. We address the challenge of lack of training samples by creating two models to characterize the target class spectral variability --the first model makes no assumptions regarding inter-band correlation, while the second model uses a first-order Markovbased scheme to exploit correlation between bands. Using these models, we present two techniques for meeting these challenges-the kernel-based support vector data description (SVDD) and spectral fringe-adjusted joint transform correlation (SFJTC). We have developed an algorithm that uses the kernel-based SVDD for use in full-pixel target detection scenarios. We have addressed optimization of the SVDD kernel-width parameter using the golden-section search algorithm for unconstrained optimization. We investigated a proper number of signatures N to generate for the SVDD target class and found that only a small number of training samples is required relative to the dimensionality (number of bands). We have extended decision-level fusion techniques using the majority vote rule for the purpose of alleviating the problem of selecting a proper value of s 2 for either of our target variability models. We have shown that heavy spectral variability may cause SFJTC-based detection to suffer and have addressed this by developing an algorithm that selects an optimal combination of the discrete wavelet transform (DWT) coefficients of the signatures for use as features for detection. For most scenarios, our results show that our SVDD-based detection scheme provides low false positive rates while maintaining higher true positive rates than popular stochastic detection algorithms. Our results also show that our SFJTC-based detection scheme using the DWT coefficients can yield significant detection improvement compared to use of SFJTC using the original signatures and traditional stochastic and deterministic algorithms.
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Book chapters on the topic "Golden Section Search"

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Pejic, Dragana, and Milos Arsic. "Minimization and Maximization of Functions: Golden-Section Search in One Dimension." In Computer Communications and Networks, 55–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13803-5_3.

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Liu, W. P., Y. F. Shang, X. Yang, R. Deklerck, and J. Cornelis. "Shape Deformation Using Golden Section Search in PCA-Based Statistical Shape Model." In IFMBE Proceedings, 659–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23508-5_171.

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Glasmachers, Tobias, and Sahar Qaadan. "Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search." In Machine Learning, Optimization, and Data Science, 329–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0_28.

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Chen, Y., S. Hsieh, and P. Shan-Kung. "Assessment of liquefaction potential by principal component analysis and golden section search." In Cyclic Behaviour of Soils and Liquefaction Phenomena, 473–78. Taylor & Francis, 2004. http://dx.doi.org/10.1201/9781439833452.ch57.

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Conference papers on the topic "Golden Section Search"

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Li Xin and Yuan-yuan Jiang. "Golden-section peak search in fractional Fourier domain." In 2011 International Conference on Electric Information and Control Engineering (ICEICE). IEEE, 2011. http://dx.doi.org/10.1109/iceice.2011.5778100.

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Chang, Yen-Ching. "N-Dimension Golden Section Search: Its Variants and Limitations." In 2009 2nd International Conference on Biomedical Engineering and Informatics. IEEE, 2009. http://dx.doi.org/10.1109/bmei.2009.5304779.

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Arai, Hirotaka, Takuya Arafune, Shohei Shibuya, Yutaro Kobayashi, Koji Asami, and Haruo Kobayashi. "Fibonacci sequence weighted SAR ADC as golden section search." In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2017. http://dx.doi.org/10.1109/ispacs.2017.8266559.

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Melnyk, M. R., A. B. Kernytskyy, M. V. Lobur, P. Zajac, M. Szermer, C. Maj, W. Zabierowski, and A. Napieralski. "Applying the golden section search in optimization of micro actuator design." In 2015 13th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM). IEEE, 2015. http://dx.doi.org/10.1109/cadsm.2015.7230794.

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Chen, Wenli, Zhiwen Mo, and Wen Guo. "Detection of QRS Complexes Using Wavelet Transforms and Golden Section Search." In International Conference on Intelligent Systems and Knowledge Engineering 2007. Paris, France: Atlantis Press, 2007. http://dx.doi.org/10.2991/iske.2007.32.

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Scherrer, Tomas, Soo-Yong Kim, and Chaehag Yi. "Low complexity, real-time adjusted power management policy using Golden Section Search." In 2013 International Soc Design Conference (ISOCC). IEEE, 2013. http://dx.doi.org/10.1109/isocc.2013.6864014.

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Oh, Sehoon, and Yoichi Hori. "Development of Golden Section Search Driven Particle Swarm Optimization and its Application." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.314857.

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Tagawa, Kiyoharu, Hirokazu Takeuchi, and Atsushi Kodama. "Memetic differential evolutions using adaptive golden section search and their concurrent implementation techniques." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257200.

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Agrawal, Jaya, and Mohan Aware. "Golden section search (GSS) algorithm for Maximum Power Point Tracking in photovoltaic system." In 2012 IEEE 5th India International Conference on Power Electronics (IICPE). IEEE, 2012. http://dx.doi.org/10.1109/iicpe.2012.6450384.

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Oh, Sehoon, and Yoichi Hori. "Parameter Optimization for NC Machine Tool Based on Golden Section Search Driven PSO." In 2007 IEEE International Symposium on Industrial Electronics. IEEE, 2007. http://dx.doi.org/10.1109/isie.2007.4375113.

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