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Добірка наукової літератури з теми "GWO"

Автор: Grafiati

Опубліковано: 16 січня 2024

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Статті в журналах з теми "GWO"

Liu, Guangwei, Zhiqing Guo, Wei Liu, Feng Jiang, and Ensan Fu. "A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm." PLOS ONE 19, no. 1 (January 2, 2024): e0295579. http://dx.doi.org/10.1371/journal.pone.0295579.

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This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.

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Liu, Yuanyuan, Jiahui Sun, Haiye Yu, Yueyong Wang, and Xiaokang Zhou. "An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm." Applied Sciences 10, no. 18 (September 11, 2020): 6343. http://dx.doi.org/10.3390/app10186343.

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Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

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Liu, Haiqiang, Gang Hua, Hongsheng Yin, and Yonggang Xu. "An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1723191.

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Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.

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Zhao, Jiankun, Wei Liu, Qingxian Zhang, Hexi Wu, Liangquan Ge, Tianbin Li, Yi Gu, Qi Zeng, and Yibao Liu. "Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm." Journal of Instrumentation 17, no. 08 (August 1, 2022): P08034. http://dx.doi.org/10.1088/1748-0221/17/08/p08034.

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Abstract Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into consideration, to demonstrate the effectiveness of the algorithm, which is appropriated in unknown areas of the radioactive waste repository. Since there are three factors with different contribution, such as position, quantity of the source and gamma ray energy, which considered to demonstrate its impact on success. So, a hybrid adaptive grey wolf algorithm (HAGWO) has been adopted and implemented to develop a novel rapid method of radioactive leak location. Three aspects, including the good point set initialization in population size, balanced convergence function, and self-adaptive greedy strategy for population update, were optimized and merged into the locating model. To investigate the effectiveness of the algorithm, results of HAGWO are compared with grey wolf algorithm (GWO), good point set initialization strategy GWO(GGWO) and adaptive head wolf strategy GWO (ALGWO) in convergence speed, accuracy, stability and positioning error of single and double leak points. It is observed that convergence speed is increased by 37.93 ± 2% at the highest; the convergence accuracy is increased by 92.42 ± 2% at the most; the stability is improved by 30% ∼ 50%. The positioning error of single leak point is within 1.08%, and the positioning error of double leak point is less than 8.90%. Besides, compared with GWO, GGWO and ALGWO, the single-point accuracy is improved by 1.36 percentage points (to GWO), and the double-point accuracy is improved by 40.35 percentage points (to ALGWO) at most. It is observed that HAGWO performs the best in locating leaks, with a faster convergence, stronger stability and more accuracy.

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Zheng, Yukun, Ruyue Sun, Yixiang Liu, Yanhong Wang, Rui Song, and Yibin Li. "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator." Actuators 12, no. 6 (May 25, 2023): 220. http://dx.doi.org/10.3390/act12060220.

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Анотація:

Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-heuristic algorithms such as particle swarm optimization (PSO), GWO, and DE. The results show that the proposed H-GWO algorithm can provide very competitive results. On this basis, the forgetting factor recursive least squares (FFRLS) method and the proposed H-GWO algorithm are combined to establish a parameter identification algorithm to identify parameters of the helical hydraulic rotary actuator (HHRA) with nonlinearity and uncertainty questions. In addition, the proposed method is verified by practical identification experiments. After comparison with the least squares (LS), recursive least squares (RLS), FFRLS, PSO, and GWO results, it can be concluded that the proposed method (H-GWO) has higher identification accuracy.

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Shi-fan, Qiao, Tan Jun-kun, Zhang Yong-gang, Wan Li-jun, Zhang Ming-fei, Tang Jun, and He Qing. "Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence." Advances in Civil Engineering 2021 (January 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/8896210.

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This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.

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Kohli, Mehak, and Sankalap Arora. "Chaotic grey wolf optimization algorithm for constrained optimization problems." Journal of Computational Design and Engineering 5, no. 4 (March 7, 2017): 458–72. http://dx.doi.org/10.1016/j.jcde.2017.02.005.

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Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems. Highlights Chaos has been introduced to the GWO to develop Chaotic GWO for global optimization. Ten chaotic maps have been investigated to tune the key parameter ‘a’, of GWO. Effectiveness of the algorithm is tested on many constrained benchmark functions. Results show CGWO's better performance over other nature-inspired optimization methods. The proposed CGWO is also used for some engineering design applications.

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Milenković, Branislav, Mladen Krstić, and Đorđe Jovanović. "Application of grey wolf algorithm for solving engineering optimization problems." Tehnika 76, no. 1 (2021): 50–57. http://dx.doi.org/10.5937/tehnika2101050m.

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Анотація:

This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other methods.

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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (June 11, 2020): 3335. http://dx.doi.org/10.3390/s20113335.

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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.

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Tumari, Mohd Zaidi Mohd, Mohd Muzaffar Zahar, and Mohd Ashraf Ahmad. "Optimal tuning of a wind plant energy production based on improved grey wolf optimizer." Bulletin of Electrical Engineering and Informatics 10, no. 1 (February 1, 2021): 23–30. http://dx.doi.org/10.11591/eei.v10i1.2509.

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The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.

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Більше джерел

Дисертації з теми "GWO"

Biscaro, Michele <1996&gt. "Analisi Tecnica: ottimizzazione di un Trading System tramite PSO e GWO." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/21544.

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Анотація:

L’avvento di Internet e lo sviluppo della tecnologia hanno cambiato radicalmente il concetto di trading negli ultimi vent’anni. La figura stessa del trader è stata stravolta, tant’è che ad oggi, tra le competenze richieste, la conoscenza dei mercati finanziari non è più fondamentale quanto invece lo sono le abilità informatiche e matematiche. In questo contesto, l’Analisi Tecnica ha vissuto un periodo di rinascita e successo, sia per il fascino e la semplicità che la caratterizzano, sia per i notevoli progressi che lo sviluppo della tecnologia ha permesso di raggiungere. Oggi, numerosi ricercatori valutano le regole di trading dettate da questa disciplina e le combinano con nozioni di informatica e ingegneria, con lo scopo di ottimizzarle e adeguarle alla dinamica dei mercati odierni. Nell’elaborato, inizialmente, verrà introdotta la disciplina dell’Analisi Tecnica partendo dalle sue fondamenta, passando per i concetti principali dell’analisi grafica e terminando con i suoi aspetti critici. In seguito, il focus si sposterà sui Trading Systems (TS): verranno introdotti i principali indicatori tecnici presenti in letteratura e il TS oggetto del lavoro. Dopo di che, verranno introdotti i due algoritmi bio-inspired che ottimizzeranno il TS, in particolare il Particle Swarm Optimizer (PSO) e il Grey Wolf Optimizer (GWO), di cui verranno trattate le strutture originali, i recenti sviluppi in letteratura e ne verrà effettuato un breve confronto. Infine, si procederà con l’ottimizzazione del TS attraverso i due algoritmi, con lo scopo di individuare i valori ottimi dei parametri degli indicatori tecnici che lo compongono.

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Chan, Yick Mei Keriner. "The functions and occurrence patterns of aspect markers jo, gwo, gan, jyuh in Cantonese narrative discourse." HKBU Institutional Repository, 1999. http://repository.hkbu.edu.hk/etd_ra/401.

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Alatram, Ala'a A. M. "A forensic framework for detecting denial-of-service attacks in IoT networks using the MQTT protocol." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2561.

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Анотація:

In the domain of the Internet of Things (IoT), The Message Queueing Telemetry Protocol (MQTT) is the most widely used protocol for applications across a wide range of realms, including industrial automation, healthcare, smart homes, and smart cities; MQTT is also used in many other critical real-world applicastions. An example is BMW’s Car Sharing application, that uses MQTT to provide reliable connectivity. However, due to a lack of security considerations during the design of the MQTT protocol, all the networks implementing it are prone to cyberattacks, such as denial-of-service (DoS) attacks. While the research community has a primary focus on MQTT vulnerabilities from the perspective of intrusion detection, digital forensic considerations of the protocol have yet to be addressed. This work attempts to address this issue, specifically by generating a novel dataset based on data captured from a testbed in an IoT setting, and the application of optimised Machine Learning (ML) algorithms to differentiate between cyberattacks and benign network traffic. The philosophical assumptions guiding the conduct of this research are Positivist Paradigm, Quantitative Methodology, Experimental Research Mode, and Quasi-Experimental as a Sub-category. As a result of the IoT testbed construction, a substantial quantity of IoT data was produced, including standard MQTT data and ten different DoS and DDoS attack scenarios. In addition, a network forensic analysis of the collected data shows specific information that can be extracted and the differences between attacks and normal data. Also, eight different ML algorithms were compared, resulting in the suggestions of Random Forest (RF), XGBoost, and Artificial Neural Network (ANN) for use in the proposed framework. Gray Wolf Optimiser (GWO) was selected to combine RF and ANN in a core component of the framework. It has been demonstrated that RF with GWO and ANN with GWO can optimise results. The output of this research can have a potential impact on the implementations of MQTT-powered networks globally, thereby improving the security of modern networks that use this protocol.

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Baldaccini, Chiara <1989&gt. "”Gao Jianli”, dramma storico “minore” di Guo Moruo: traduzione e commento." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4568.

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Анотація:

Attraverso la traduzione di "Gao Jianli", uno dei sei drammi storici di Guo Moruo, viene presentata quest'opera e proposta una ricerca sui motivi che hanno condotto l'autore ad impegnarsi nella stesura della stessa.

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Kirchhain, Simone. "Die Anwendung der Vertikal-GVO auf innerstaatliche Wettbewerbsbeschränkungen nach der 7. GWB-Novelle." Frankfurt,M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2006. http://d-nb.info/98596135X/04.

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Guo, Gao [Verfasser], K. D. [Gutachter] Wolff, M. [Gutachter] Farmand, and M. [Gutachter] Klein. "Retrospektive Untersuchung zu implantatverankerten Ohrepithesen / Gao Guo ; Gutachter: K. D. Wolff, M. Farmand, M. Klein." Berlin : Humboldt-Universität zu Berlin, 2005. http://d-nb.info/1207657913/34.

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Zheng, Ruiqin. "Chong gao yu tui fei : Wang Guowei, Lu Xun, Guo Moruo yu Yu Dafu = Sublime and decadence : Wang Guowei, Lu Xun, Guo Moruo and Yu Dafu /." click here to view the abstract and table of contents click here to view the fulltext, 2005. http://net3.hkbu.edu.hk/~libres/cgi-bin/thesisab.pl?pdf=b18843451a.pdf.

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Guo, Gao [Verfasser]. "Fibrillin-1 and elastin fragmentation in the pathogenesis of thoracic aortic aneurysm in Marfan syndrome / Gao Guo." Berlin : Freie Universität Berlin, 2011. http://d-nb.info/1026265851/34.

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Prudhomme, Jorian. "Phlébotomes et écosystèmes : impact des facteurs biotiques et abiotiques sur la structure génétique et phénotypique des populations." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS027/document.

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Les phlébotomes sont des insectes hématophages appartenant à la famille des Psychodidae et à la sous-famille des Phlebotominae. Cet insecte diptère, jaunâtre, relativement petit (2 à 3 mm) compte environ 800 espèces. 70 de ces espèces ont été identifiées comme vecteurs potentiels dont une quarantaine sont des vecteurs prouvés. Ils peuvent transmettre différents pathogènes dont les principaux sont les leishmanies et les phlébovirus. Ce travail de thèse est focalisé sur les phlébotomes vecteurs de la leishmaniose. Les leishmanioses sont des maladies parasitaires causées par un protozoaire du genre Leishmania. Elles touchent un large panel d’hôtes vertébrés, dont l’homme et le chien. Elles sont toujours un problème de santé publique majeur dans de nombreux pays et sont actuellement en expansion. Bien que cette maladie soit largement étudiée, nous avons encore beaucoup apprendre sur son vecteur : le phlébotome. Par exemple, l’organisation des populations dans les écosystèmes et les paramètres qui les structurent, sont à l’heure actuelle très peu étudiés. Il est pourtant primordial de bien connaitre la biologie des différents acteurs d’un cycle parasitaire pour mieux comprendre la transmission du pathogène, évaluer les risques et enfin être capable de lutter efficacement contre la maladie. Dans ce contexte, le but de cette thèse est d’étudier l’écologie et la structure des populations de phlébotomes dans un foyer connu de leishmaniose et l’impact des facteurs biotiques et abiotiques sur leur organisation. Pour atteindre cet objectif, nous avons réalisé une collecte de phlébotomes le long d’un transect de 14km localisé dans la région de Montpellier, présentant une diversité altitudinale, climatique et environnementale. Les populations de phlébotomes ont été caractérisées d’un point de vue taxonomique, spatio-temporel, génétique (microsatellites), et morphométrique (géométrie morphométrie). Les résultats génétiques, morphométriques et de distribution des espèces ont été ensuite confrontés à des paramètres climatiques (température, humidité) ou environnementaux (altitude, versant, station, microhabitat).Durant ce travail, 4 espèces ont été capturées : Phlebotomus ariasi (93,23%), P. perniciosus (0,48%), P. mascittii (0,11%) et S. minuta (6,18%). Elles ont une activité saisonnière de Mai à Octobre avec un pic d’abondance en Juillet-Août quand les températures moyennes sont optimales pour les phlébotomes (20-30°C). Bien que l'environnement ait été considérablement transformé dans notre zone d'étude en 30 ans, l'abondance des phlébotomes ne semble pas avoir changé de façon significative, soulignant leur capacité d'adaptation aux modifications de l'écosystème à court et long terme. La présence et l’abondance des deux espèces prédominantes (P. ariasi et S. minuta) sont significativement influencées par l’altitude, la température, l’humidité relative, le versant ainsi que l’orientation des murs. Les analyses génétiques montrent que la diversité est conservée à toutes les échelles d’études et qu’il existe une structuration des phlébotomes en micropopulations. Les données de géométrie morphométrie révèlent un dimorphisme sexuel bien connu chez les insectes mais également une structuration phénotypique en fonction des facteurs environnementaux ou temporels (mois, versant, altitude et station). Ces deux types d’approches permettent grâce à leur complémentarité d’apporter des informations sur l’écologie et l’organisation des populations de phlébotomes et de discuter des conséquences sur la transmission de la leishmaniose
Sandflies are hematophagous insects belonging to the family Psychodidae and the subfamily phlebotominae. This diptera, yellowish, relatively small (2-3 mm) has about 800 species. 70 of these species have been identified as potential vectors of which forty are proven ones. They can transmit different pathogens; the main ones are Leishmania and phlebovirus.This thesis focused on sandflies, vectors of leishmaniasis. Leishmaniases are parasitic diseases caused by protozoa of the genus Leishmania. They affect a wide range of vertebrate hosts, including humans and dogs. They are still a major problem of public health in many countries and are currently in expansion. Although this disease is widely studied, we still have a lot to learn about its vector: the sandfly. For example, the organization of populations in ecosystems and the parameters which structure them are very little studied up to now. It is therefore essential to know the biology of the different actors of a parasite cycle to better understand the transmission of pathogens, to assess risks of transmission, and finally to be able to effectively fight against the disease.In this context, the aim of this thesis is to study the ecology and the structuring of sandfly populations in a known endemic area of leishmaniasis and the impact of biotic and abiotic factors on their organization. To reach this objective, we performed captures of sandflies along a 14km transect located in the Montpellier region which presents an altitudinal, climate and environmental diversity. Sandfly populations have been characterized by taxonomic, spatio-temporal, genetic (microsatellites) and morphometric (geometry morphometry) approaches. The genetic, morphometric and species distribution results were then confronted with climatic (temperature, relative humidity) or environmental parameters (altitude, slope, station, microhabitat).During this work, four species were captured: Phlebotomus ariasi (93.23%), P. perniciosus (0.48%), P. mascittii (0.11%) and S. minuta (6.18%). They have a seasonal activity from May to October with an abundance peak in July-August when average temperatures are optimal for sandflies (20-30°C). Although the environment has been considerably transformed in our study area in 30 years, the abundance of sandflies does not seem to have changed significantly, highlighting their ability to adapt to ecosystem modifications in short and long-term. The presence and abundance of the two predominant species (P. ariasi and S. minuta) are significantly influenced by altitude, temperature, relative humidity, slope and wall orientation.The genetic analyses show that diversity is maintained at all scales of study and that sandflies are organized in micropopulations. The morphometric geometry data reveal a sexual dimorphism, well known in insects but also a phenotypic structuring correlated to environmental or temporal factors (month, slope, altitude and station).Both of these approaches, because of their complementarity, help provide information on the ecology and organization of sandfly populations and to discuss about the consequences in terms of leishmaniasis transmission

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Gabriele, Marzia. "La circolazione delle ceramiche del Neolitico nel medio e alto Tirreno e nell’area ligure-provenzale : Studi di provenienza." Thesis, Nice, 2014. http://www.theses.fr/2014NICE2052.

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Les régions de la moyenne et haute Tyrrhénienne, la Ligurie et la Provence ont connu pendant le VIème millénaire BCE d’intenses activités maritimes et d’échange, dans le contexte de la néolithisation de la Méditerranée occidentale et intimement liées à l'exploitation et à la circulation des matières premières telles que l'obsidienne, le silex et les roches vertes; dans ce contexte, la définition de la circulation céramique constitue évidement un point central pour la compréhension des rapports économiques et culturels entre les différents groupes néolithiques.Afin de contribuer à la recherche sur le complexe culturel du Néolithique ancien de la zone considérée, nous avons abordé les échanges et les interactions possibles entre les différents groupes grâce à la caractérisation de la matière première des productions céramiques, leur origine et leur diffusion. Notre méthodologie est fondée sur l'analyse pétrographique (microscope stéréoscopique et/ou microscope optique) des matériaux céramiques de certains des principaux sites du techno-complexe Impressa-Cardial afin de déterminer les potentielles zones d'origine des matières premières et les choix techniques de production, par la confrontation des données de géo-ressources et des céramiques archéologiques.Sur la base des résultats d’analyses obtenus, nous avons essayé de définir les productions céramiques et leur circulation entre la moyenne-haute Tyrrhénienne, la Ligurie et la Provence, pour les différentes étapes chrono-culturelles du Néolithique ancien
Intense seafaring and cultural/commercial exchanges took place in the region among the Central-Northern Tyrrhenian sea, Liguria and Provence during the VI millennium BCE; these contributed to the neolithisation of the western Mediterranean and were intimately linked to the exploitation and circulation of raw materials, such as obsidian, chert and greenstone. Within this framework, the assessment of ceramic circulation paths is obviously central to understand the economic and cultural relations between different Neolithic groups.Exchanges and potential interactions between different groups were assessed by characterising raw materials used in pottery production, as well as their provenance and diffusion, in order to contribute to research on the early Neolithic in the above mentioned areas.The methodology was aimed at sourcing raw materials and defining technical choices in pottery production by petrographic analyses (stereo- and / or optical microscope) of ceramic samples from some key sites of the Impressa-Cardiale Ware techno-complex, and on their comparison with geo-resources and archaeological pottery data.Pottery production and circulation among the Tyrrhenian, Liguria and Provence areas, in distinct chrono-cultural stages of Early Neolithic, was defined upon the results of these analyses

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Книги з теми "GWO"

Tardieu, Gérard-Marie. Gwo koze: Nòt pou Responsab Twoup nou yo. Port-au-Prince, Haiti: Kopivit L'Action Sociale, 2009.

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Troupe, Georges. Méthode d'apprentissage des sept rythmes de Gwo Ka: Graphie et musique. [Guadéloupe]: I.G.E.S., 1988.

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Blou, Léna. Techni'ka: [Recherches sur l'émergence d'une méthode d'enseignement à partir des danses gwo-ka]. Pointre-à-Pitre: Jasor, 2005.

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San guo lun gao. Zhengzhou: Zhongzhou gu ji chu ban she, 2018.

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Guo men bao gao. Beijing: Guang ming ri bao chu ban she, 2002.

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An gao xiao guo. Beijing: Gao deng jiao yu chu ban she, 2012.

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ling, Zhang, ed. Gao sheng di guo. Bei jing: Zhong xin chu ban she, 2010.

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Changyun, Shen, ed. Zhao guo shi gao. Beijing: Zhonghua shu ju, 2000.

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ming, Lian yu, and Wu jian zhong. 2007 zhong guo guo ce bao gao. Bei jing: Zhong guo shi dai jing ji chu ban she, 2007.

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Guo shi guan (China : Republic : 1949- ), ed. Guo shi ni zhuan: Zhonghua min guo guo shi gao. Taibei Xian Xindian Shi: Guo shi guan, 1988.

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Частини книг з теми "GWO"

Rezaei, Hossein, Omid Bozorg-Haddad, and Xuefeng Chu. "Grey Wolf Optimization (GWO) Algorithm." In Advanced Optimization by Nature-Inspired Algorithms, 81–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_9.

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Nayak, Gayatri, Mitrabinda Ray, Swadhin Kumar Barisal, and Bichitrananda Patra. "GWO Based Test Sequence Generation and Prioritization." In Smart Innovation, Systems and Technologies, 255–66. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_23.

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Shial, Gyanaranjan, Chitaranjan Tripathy, Sibarama Panigrahi, and Sabita Sahoo. "An Improved GWO Algorithm for Data Clustering." In Communications in Computer and Information Science, 79–90. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21750-0_7.

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Sahoo, Gopal Krishna, Niharika Patel, Debiprasad Panda, Shaswati Mishra, Sandeep Samantaray, and Deba Prakash Satapathy. "Streamflow Forecasting Using Novel ANFIS-GWO Approach." In Evolution in Computational Intelligence, 141–52. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7513-4_13.

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Ke, Jian, and Shiqian Yu. "Financial Crisis Prediction Based on GWO-SVM." In Atlantis Highlights in Intelligent Systems, 535–43. Dordrecht: Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-222-4_58.

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Tripathi, Sandeep, Ashish Shrivastava, and K. C. Jana. "GWO Based PID Controller Optimization for Robotic Manipulator." In Intelligent Computing Techniques for Smart Energy Systems, 943–51. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_100.

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Jiang, Wei, Weiguo Zhang, and Jingping Shi. "UAV Path Planning Based on Improved GWO Algorithm." In Lecture Notes in Electrical Engineering, 20–26. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_3.

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Fei, He, and Jiabei Shen. "Harmonic Elimination in Inverter Using Modified GWO Algorithm." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022), 165–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3408-9_15.

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Pan, Jeng-Shyang, Thi-Kien Dao, Shu-Chuan Chu, and Trong-The Nguyen. "A Novel Hybrid GWO-FPA Algorithm for Optimization Applications." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications, 274–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70730-3_33.

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Lu, Hongxia, Guidong Zhang, and Yongjun Shen. "Cyber Security Situation Prediction Model Based on GWO-SVM." In Innovative Mobile and Internet Services in Ubiquitous Computing, 162–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22263-5_16.

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Тези доповідей конференцій з теми "GWO"

Rocha, Lídia, and Kelen Vivaldini. "Comparison between Meta-Heuristic Algorithms for Path Planning." In VIII Workshop de Teses e Dissertações em Robótica/Concurso de Teses e Dissertações em Robótica. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wtdr_ctdr.2020.14950.

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Unmanned Aerial Vehicle (UAV) has been increasingly employed in several missions with a pre-defined path. Over the years, UAV has become necessary in complex environments, where it demands high computational cost and execution time for traditional algorithms. To solve this problem meta-heuristic algorithms are used. Meta-heuristics are generic algorithms to solve problems without having to describe each step until the result and search for the best possible answer in an acceptable computational time. The simulations are made in Python, with it, a statistical analyses was realized based on execution time and path length between algorithms Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Glowworm Swarm Optimization (GSO). Despite the GWO returns the paths in a shorter time, the PSO showed better performance with similar execution time and shorter path length. However, the reliability of the algorithms will depend on the size of the environment. PSO is less reliable in large environments, while the GWO maintains the same reliability.

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Cao, Enqi, and Fanliang Bu. "GWO-BP-AdaBoost: Enhancing Classification Performance of BP Neural Networks Through GWO-Based Optimization and AdaBoost Integration." In 2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2023. http://dx.doi.org/10.1109/icpics58376.2023.10235680.

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Mustaffa, Zuriani, Mohd Herwan Sulaiman, and Mohamad Nizam Mohmad Kahar. "Training LSSVM with GWO for price forecasting." In 2015 International Conference on Informatics, Electronics and Vision (ICIEV). IEEE, 2015. http://dx.doi.org/10.1109/iciev.2015.7334054.

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An, Qi, Liyue Fu, and Ancai Zhang. "Sensor Dynamic Modeling Based on GWO-LSSVM." In 2022 8th International Conference on Mechanical Engineering and Automation Science (ICMEAS). IEEE, 2022. http://dx.doi.org/10.1109/icmeas57305.2022.00052.

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Suresh, Koduru, G. Vinoth Kumar, and K. Narasimha Raju. "Cost Optimization For Intelligent Enterprises Using Modified GWO." In 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2020. http://dx.doi.org/10.1109/icssit48917.2020.9214166.

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Chauhan, Sandeep, and Manisha J. Nene. "Energy Efficent Protocols Leach — GWO vs PEGASIS Comparision." In 2022 International Conference on Futuristic Technologies (INCOFT). IEEE, 2022. http://dx.doi.org/10.1109/incoft55651.2022.10094452.

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Liu, Lechao, Yan Zhuang, and Xufei Gao. "Malicious traffic detection based on GWO-SVM model." In 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2022. http://dx.doi.org/10.1109/iccasit55263.2022.9986544.

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Soni, Dinesh, and Neetesh Kumar. "GWO-Based Workflow Scheduling in Cloud-Fog Environments." In 2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM). IEEE, 2023. http://dx.doi.org/10.1109/elexcom58812.2023.10370034.

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Jayapriya, J., and Michael Arock. "A parallel GWO technique for aligning multiple molecular sequences." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275611.

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Ahmed, Alisha, Rajeev Gupta, and Girish Parmar. "GWO/PID Approach for Optimal Control of DC Motor." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474105.

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Звіти організацій з теми "GWO"

Hong, Weiwei, and Jean Parsons. Guo Poem. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1665.

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White, Sala, and Nikki Kujawa. Geo Queen. Ames: Iowa State University, Digital Repository, November 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1682.

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Johnston, W. R. GEO-GEO Cross-Calibration Results for AE9 Development. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada605733.

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Author, Unknown. GAO-06-945 GAO Natural Gas Pipeline Risk-Based Standards. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2006. http://dx.doi.org/10.55274/r0012165.

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The Pipeline Safety Improvement Act of 2002 requires that operators (1) assess gas transmission pipeline segments in about 20,000 miles of highly populated or frequently used areas by 2012 for safety threats, such as incorrect operation and corrosion (called baseline assessments), (2) remedy defects, and (3) reassess these segments at least every 7 years. Under the Pipeline and Hazardous Materials Safety Administration's (PHMSA) regulations, operators must reassess their pipeline segments for corrosion at least every 7 years and for all safety threats at least every 10, 15, or 20 years, based on industry consensus standards - and more frequently if conditions warrant. Operators must also carry out other prevention and mitigation measures. To meet a requirement in the 2002 act, this study addresses how the results of baseline assessments and other information inform us on the need to reassess gas transmission pipelines every 7 years and whether inspection services and tools are likely to be available to do so, among other things. In conducting its work, GAO contacted 52 operators that have carried out about two-thirds of the baseline assessments conducted to date.

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Junor, William. MAGI - Multi-Aperture GEO Imaging. Office of Scientific and Technical Information (OSTI), April 2013. http://dx.doi.org/10.2172/1072244.

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Hirsch, Leonard. Final Technical Report -- GEO-VI - USGEO. Office of Scientific and Technical Information (OSTI), November 2009. http://dx.doi.org/10.2172/1133797.

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Doveton, John H., and W. Lynn Watney. Geo-Engineering through Internet Informatics (GEMINI). Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/808530.

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Hsu, Stephen M. Wind energy curriculum development at GWU. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1082756.

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Watney, W. Lynn, John H. Doveton, John R. Victorine, Goeffrey C. Bohling, Saibal Bhattacharya, Alan P. Byers, Timothy R. Carr, et al. Geo-Engineering through Internet Informatics (GEMINI). Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/795618.

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Dušek, Zdenek. Examples of Pseudo-Riemannian G.O. Manifolds. GIQ, 2012. http://dx.doi.org/10.7546/giq-8-2007-144-155.

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