Academic literature on the topic 'Robust Hybrid Method (RHM)'
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Journal articles on the topic "Robust Hybrid Method (RHM)":
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Yaich, A., G. Kharmanda, A. El Hami, L. Walha, and M. Haddar. "Reliability Based Design Optimization for Multiaxial Fatigue Damage Analysis Using Robust Hybrid Method." Journal of Mechanics 34, no. 5 (July 6, 2017): 551–66. http://dx.doi.org/10.1017/jmech.2017.44.
Abstract:
AbstractThe purpose of the Reliability-Based Design Optimization (RBDO) is to find the best compromise between safety and cost. Therefore, several methods, such as the Hybrid Method (HM) and the Optimum Safety Factor (OSF) method, are developed to achieve this purpose. However, these methods have been applied only on static cases and some special dynamic ones. But, in real mechanical applications, structures are subject to random vibrations and these vibrations can cause a fatigue damage. So, in this paper, we propose an extension of these methods in the case of structures under random vibrations and then demonstrate their efficiency. Also, a Robust Hybrid Method (RHM) is then developed to overcome the difficulties of the classical one. A numerical application is then used to present the advantages of the modified hybrid method for treating problem of structures subject to random vibration considering fatigue damage.
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Ha, La Phan Phuong, Tran Hong Huy, Pham Huu Huan, Nguyen Thi Minh Thu, Cao Minh Thi, and Pham Van Viet. "Peroxymonosulfate Activation on a Hybrid Material of Conjugated PVC and TiO2 Nanotubes for Enhancing Degradation of Rhodamine B under Visible Light." Advances in Polymer Technology 2020 (November 25, 2020): 1–9. http://dx.doi.org/10.1155/2020/8888767.
Abstract:
Visible-light-driven photocatalysis is a robust technology for amending the negative effect of pollutants on the environment with a minimum energy use. Herein, we describe a simple approach to producing such a photocatalyst by coupling conjugated polyvinyl chloride (cPVC) with the TiO2 nanotube (TNT) thermolysis method. By activating peroxymonosulfate (PMS) to make a cPVC/TNT/PMS system using visible light as the source, we obtain a significant enhancement in the photocatalytic performance. We show that PMS use at a concentration of 3 mM can fully degrade rhodamine B (RhB) solution at a remarkably high concentration (200 mg L-1) just in 120 min under visible light. The cPVC/TNT/PMS system also shows excellent stability in recycling tests for at least five times. Further, by confining the active species in photocatalytic reactions, we report a thorough understanding of the extent of involvement from those radicals. Our work presents a robust approach to make a high-performance, visible-light-driven photocatalyst, which can be potentially used in practice.
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Aziguli, Wulamu, Yuanyu Zhang, Yonghong Xie, Dezheng Zhang, Xiong Luo, Chunmiao Li, and Yao Zhang. "A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data." Scientific Programming 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3610378.
Abstract:
Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.
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Del Razo Gonzalez, Abraham, and Vsevolod Yutsis. "Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach." Applied Sciences 13, no. 20 (October 14, 2023): 11292. http://dx.doi.org/10.3390/app132011292.
Abstract:
One of the fundamental challenges in geophysics is the calculation of distribution models for physical properties in the subsurface that accurately reproduce the measurements obtained in the survey and are geologically plausible in the context of the study area. This is known as inverse modeling. Performing a 3D joint inversion of multimodal geophysical data is a computationally intensive task. Additionally, since it involves a modeling process, finding a solution that matches the desired characteristics requires iterative calculations, which can take days or even weeks to obtain final results. In this paper, we propose a robust numerical solution for 3D joint inversion of gravimetric and magnetic data with Gramian-based structural similarity and structural direction constraints using parallelization as a high-performance computing technique, which allows us to significantly reduce the total processing time based on the available Random-Access Memory (RAM) and Video Random-Access Memory (VRAM)and improve the efficiency of interpretation. The solution is implemented in the high-level programming languages Fortran and Compute Unified Device Architecture (CUDA) Fortran, capable of optimal resource management while being straightforward to implement. Through the analysis of performance and computational costs of serial, parallel, and hybrid implementations, we conclude that as the inversion domain expands, the processing speed could increase from 4× up to 100× times faster, rendering it particularly advantageous for applications in larger domains. We tested our algorithm with two synthetic data sets and field data, showing better results than standard separate inversion. The proposed method will be useful for joint geological and geophysical interpretation of gravimetric and magnetic data used in exploration geophysics for example minerals, ore, and petroleum search and prospecting. Its application will significantly increase the reliability of physical-geological models and accelerate the process of data processing.
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Zhao, Siqi, Deliang Chen, Yawu Gao, Tao Li, Shasha Yi, Haipeng Ji, Xiaochao Zuo, et al. "One-Pot Synthesis of Fe–N–C Species-Modified Carbon Nanotubes for ORR Electrocatalyst with Overall Enhanced Performance Superior to Pt/C." Nano 16, no. 03 (February 16, 2021): 2150028. http://dx.doi.org/10.1142/s1793292021500284.
Abstract:
Novel synthesis of efficient noble-metal-free electrocatalysts for both oxygen reduction/evolution reaction (ORR/OER) in energy conversion devices (e.g., fuel cells, metal–air batteries) is of essential significance for further sustainable development. This paper reports a facile synthesis of Fe–N–C species-modified carbon nanotubes (F/N-CNTs) for ORR application by directly pyrolyzing a fluffy hybrid precursor at a moderate temperature ([Formula: see text]C) in Ar. The fluffy hybrid precursors consisting of nitro-hydrochloric-acid-treated CNTs, melamine and Fe[Formula: see text] species are prepared via a freeze-drying method. On account of the synergistic effect of various active sites, including pyridine–N, Fe–Nx and Fe3C, and the high conductivity of the CNTs matrix, the as-obtained F/N-CNT electrocatalysts exhibit excellent ORR activities, comparable to commercial Pt/C. The addition of N heteroatoms, the dosage of Fe and the pyrolysis temperature highly influence the ORR properties of the F/N-CNT samples. The typical F/N-CNT sample obtained at the optimized parameters shows an onset potential of 1.06[Formula: see text]V and a half-wave potential of 0.91[Formula: see text]V versus reversible hydrogen electrode (RHE) in an alkaline condition, more positive than those (1.01[Formula: see text]V and 0.88[Formula: see text]V versus RHE) of Pt/C. The F/N-CNT exhibits outstanding bifunctional ORR/OER activity and excellent methanol tolerance, and the F/N-CNT-based Zn–air battery (ZAB) with an open-circuit voltage (OCV) of 1.405[Formula: see text]V presents a current density of 125[Formula: see text]mA[Formula: see text]cm[Formula: see text] and a power density of 76.5[Formula: see text]mW[Formula: see text]cm[Formula: see text]; these electrocatalytic properties are highly superior to Pt/C. The direct pyrolysis of fluffy hybrid precursors provides a concise but robust technical platform to achieve high-performance noble-metal-free electrocatalysts with ORR/OER activities superior to Pt/C.
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Nassereddine, Yassine, Manal Benyoussef, Nitul S. Rajput, Sébastien Saitzek, Mimoun El Marssi, and Mustapha Jouiad. "Strong Intermixing Effects of LFO1−x/STOx toward the Development of Efficient Photoanodes for Photoelectrocatalytic Applications." Nanomaterials 13, no. 21 (October 29, 2023): 2863. http://dx.doi.org/10.3390/nano13212863.
Abstract:
Aiming to improve the photocatalytic properties of transition metal perovskites to be used as robust photoanodes, [LaFeO3]1−x/[SrTiO3]x nanocomposites (LFO1−x/STOx) are considered. This hybrid structure combines good semiconducting properties and an interesting intrinsic remanent polarization. All the studied samples were fabricated using a solid-state method followed by high-energy ball milling, and they were subsequently deposited by spray coating. The synthesized compounds were demonstrated to possess orthorhombic (Pnma) and cubic (Pm3¯m) structures for LFO and STO, respectively, with an average grain size of 55–70 nm. The LFO1−x/STOx nanocomposites appeared to exhibit high visible light absorption, corresponding to band gaps of 2.17–3.21 eV. Our findings show that LFO0.5/STO0.5 is the optimized heterostructure; it achieved a high photocurrent density of 11 μA/cm2 at 1.23 V bias vs. RHE and an applied bias photo-to-current efficiency of 4.1 × 10−3% at 0.76 V vs. RHE, as demonstrated by the photoelectrochemical measurements. These results underline the role of the two phases intermixing LFO and STO at the appropriate content to yield a high-performing photoanode ascribed to efficient charge separation and transfer. This suggests that LFO0.5/STO0.5 could be a potential candidate for the development of efficient photoanodes for hydrogen generation via photoelectrocatalytic water splitting.
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Vanjari, Prof Ms S. P., Vaishnavi Bhangale, Deepti Singh, Nitin Andhale, and Amruta Dalavi. "A Survey: Cryptography Techniques for Communication System." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 1951–57. http://dx.doi.org/10.22214/ijraset.2023.56920.
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Abstract: Sensitive data is being used more and more in online communication these days. Therefore, internet consumers' top concern is data security. The best course of action is to utilize a cryptography technique that encrypts data, translates it over the internet, and then decrypts it back to the original data. The process of securely transmitting data is the focus of the field of cryptography. The intention is to prevent eavesdroppers from comprehending a message while enabling the intended recipients to receive it correctly. A collection of methods known as cryptography are used to jumble or hide data so that only a person skilled in data restoration may access it in its original format. Cryptography offers modern computer systems a robust and costeffective foundation for maintaining data secrecy and confirming data indignity. While our traditional cryptography techniques, like RSA signature and AES encryption, function well on computers with respectable amounts of RAM and computing capacity, they are not well suited to the realm of embedded systems and sensor networks. As a result, techniques for lightweight cryptography are put forth to address numerous issues with traditional cryptography. This work develops a new hybrid method of plaintext encryption with the goal of adding to the body of knowledge in the field of classical cryptography. For an additional degree of protection, the cryptosystem employs three distinct numerical and alphabetical keys in cipher. Super Cipher is the name given for the new proposed cipher.
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Ma, Xiaoping, Lili Deng, Manting Lu, Yi He, Shuai Zou, and Yu Xin. "Heterostructure of core–shell IrCo@IrCoO x as efficient and stable catalysts for oxygen evolution reaction." Nanotechnology 33, no. 12 (December 24, 2021): 125702. http://dx.doi.org/10.1088/1361-6528/ac4068.
Abstract:
Abstract Although researches on non-noble metal electrocatalysts have been made some progress recently, their performance in proton exchange membrane water electrolyzer is still incomparable to that of noble-metal-based catalysts. Therefore, it is a more practical way to improve the utilization of precious metals in electrocatalysts for oxygen evolution reaction (OER) in the acidic medium. Herein, nanostructured IrCo@IrCoO x core–shell electrocatalysts composed of IrCo alloy core and IrCoO x shell were synthesized through a simple colloidally synthesis and calcination method. As expected, the hybrid IrCo-200 NPs with petal-like morphology show the best OER activities in acidic electrolytes. They deliver lower overpotential and better electrocatalytic kinetics than pristine IrCo alloy and commercial Ir/C, reaching a low overpotential (j = 10 mA cm−2) of 259 mV (versus RHE) and a Tafel slope of 59 mV dec−1. The IrCo-200 NPs displayed robust durability with life time of about 55 h in acidic solution under a large current density of 50 mA cm−2. The enhanced electrocatalytic activity may be associated with the unique metal/amorphous metal oxide core–shell heterostructure, allowing the improved charge transferability. Moreover, the *OH-rich amorphous shell functions as the active site for OER and prevents the further dissolution of the metallic core and thus ensures high stability.
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Townsend, Jamie F., László Könözsy, and Karl W. Jenkins. "On the development of a rotated-hybrid HLL/HLLC approximate Riemann solver for relativistic hydrodynamics." Monthly Notices of the Royal Astronomical Society 496, no. 2 (June 13, 2020): 2493–505. http://dx.doi.org/10.1093/mnras/staa1648.
Abstract:
ABSTRACT This work presents the development of a rotated-hybrid Riemann solver for solving relativistic hydrodynamics (RHD) problems with the hybridization of the HLL and HLLC (or Rusanov and HLLC) approximate Riemann solvers. A standalone application of the HLLC Riemann solver can produce spurious numerical artefacts when it is employed in conjunction with Godunov-type high-order methods in the presence of discontinuities. It has been found that a rotated-hybrid Riemann solver with the proposed HLL/HLLC (Rusanov/HLLC) scheme could overcome the difficulty of the spurious numerical artefacts and presents a robust solution for the Carbuncle problem. The proposed rotated-hybrid Riemann solver provides sufficient numerical dissipation to capture the behaviour of strong shock waves for RHD. Therefore, in this work, we focus on two benchmark test cases (odd–even decoupling and double-Mach reflection problems) and investigate two astrophysical phenomena, the relativistic Richtmyer–Meshkov instability and the propagation of a relativistic jet. In all presented test cases, the Carbuncle problem is shown to be eliminated by employing the proposed rotated-hybrid Riemann solver. This strategy is problem-independent, straightforward to implement and provides a consistent robust numerical solution when combined with Godunov-type high-order schemes for RHD.
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Zhu, Qiang, Hui-Qiang Wang, Guang-Sheng Feng, Hong-Wu Lv, Zhen-Dong Wang, Xiu-Xiu Wen, and Wei Jiang. "A Hybrid Reliable Heuristic Mapping Method Based on Survivable Virtual Networks for Network Virtualization." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/316801.
Abstract:
The reliable mapping of virtual networks is one of the hot issues in network virtualization researches. Unlike the traditional protection mechanisms based on redundancy and recovery mechanisms, we take the solution of the survivable virtual topology routing problem for reference to ensure that the rest of the mapped virtual networks keeps connected under a single node failure condition in the substrate network, which guarantees the completeness of the virtual network and continuity of services. In order to reduce the cost of the substrate network, a hybrid reliable heuristic mapping method based on survivable virtual networks (Hybrid-RHM-SVN) is proposed. In Hybrid-RHM-SVN, we formulate the reliable mapping problem as an integer linear program. Firstly, we calculate the primary-cut set of the virtual network subgraph where the failed node has been removed. Then, we use the ant colony optimization algorithm to achieve the approximate optimal mapping. The links in primary-cut set should select a substrate path that does not pass through the substrate node corresponding to the virtual node that has been removed first. The simulation results show that the acceptance rate of virtual networks, the average revenue of mapping, and the recovery rate of virtual networks are increased compared with the existing reliable mapping algorithms, respectively.
Dissertations / Theses on the topic "Robust Hybrid Method (RHM)":
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Bouguila, Maissa. "Μοdélisatiοn numérique et οptimisatiοn des matériaux à changement de phase : applicatiοns aux systèmes cοmplexes." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR05.
Abstract:
Les matériaux à changement de phase MCP révèlent des performances importantes dans le domaine de la gestion thermique. Ces matériaux ont une capacité importante de stockage thermique. L’excès de la chaleur dissipée par les composantes électroniques peut causer des graves défaillances. Un système de refroidissement développé basé sur les matériaux à changement de phase est l’une des solutions les plus recommandées afin d’assurer un fonctionnement sécurisé de ces composants microélectroniques. Bien que la faible conductivité de ces matériaux soit considérée comme la limitation majeure de leurs utilisations dans les applications de gestion thermique. L’objectif principal de cette thèse est l’amélioration de la conductivité thermique de ces matériaux et l’optimisation des dissipateurs thermiques. Dans les premiers chapitres, des modélisations numériques sont effectuées afin de déterminer la configuration optimale d’un dissipateur à partir de l’étude de plusieurs paramètres comme l’insertion des ailettes, la dispersion des nanoparticules et l’utilisation de multiples matériaux à changement de phase. L’innovation de cette étude est la modélisation du transfert thermique des matériaux à changement de phase avec une concentration des nanoparticules relativement importante par rapport à la littérature et plus précisément les travaux scientifiques expérimentaux. Des conclusions intéressantes sont extraites de cette étude paramétrique qui va nous permettre parla suite de proposer un nouveau modèle basé sur des multiples des matériaux à changement de phase améliorés avec les nanoparticules. Des études d’optimisation fiabiliste sont après réalisées.En premier lieu, une étude d’optimisation fiabiliste mono-objective a été réalisé dans le but est de proposer un modèle du dissipateur fiable à multiple NANOMCPS avec des dimensions optimales. Donc l’objectif est d'optimiser (minimiser) le volume total du dissipateur tout en considérant les différents contraintes géométriques et fonctionnels. La méthode hybride robuste (RHM) montre une efficacité à proposer un modèle fiable et optimal comparant à la méthode d’optimisation déterministe (DDO) et les différentes méthodes d’optimisation de la conception basée sur la fiabilité (RBDO) considérées. En plus de la nouveauté de modèle proposée basé sur des multiples NANOMCPs, l’intégration d’une méthode de RBDO développée (RHM) pour l’application de gestion thermique est considérée comme une innovation dans la littérature récente.En deuxième lieu, une étude d’optimisation fiabiliste multi objective est proposée. Deux objectives sont considérées : le volume total du dissipateur et le temps de décharge pour atteindre la température ambiante. De plus, l’utilisation d’une méthode d’optimisation RHM, et l’algorithme génétique de tri non dominée, sont adoptées afin de chercher le modèle optimal et fiable qui offre le meilleur compromis entre les deux objectifs. En outre, un modèle de substitution avancée est établi dans le but de réduire le temps de simulation vu que le nombre important des itérations jusqu'à aboutir à un modèle optimalPhase-change materials exhibit considerable potential in the field of thermal management.These materials offer a significant thermal storage capacity. Excessive heat dissipated by miniature electronic components could lead to serious failures. A cooling system based on phase-change materials is among the most recommended solutions to guarantee the reliable performance of these microelectronic components. However, the low conductivity of these materials is considered a major limitation to their use in thermal management applications. The primary objective of this thesis is to address the challenge of improving the thermal conductivity of these materials. Numerical modeling is conducted, in the first chapters, to determine the optimal configuration of a heat sink, based on the study of several parameters such as fin insertion, nanoparticle dispersion, and the use of multiple phase-change materials. The innovation in this parametric study lies in the modeling of heat transfer from phase-change materials with relatively high nanoparticle concentration compared to the low concentration found in recent literature (experimental researchs). Significant conclusions are deducted from this parametric study, enabling us to propose a new model based on multiple phase-change materials improved with nanoparticles (NANOMCP). Reliable optimization studies are then conducted. Initially, a mono-objective reliability optimization study is carried out to propose a reliable and optimal model based on multiple NANOMCPs. The Robust Hybrid Method (RHM)proposes a reliable and optimal model, compared with the Deterministic Design Optimization method (DDO) and various Reliability Design Optimization methods (RBDO). Furthermore,the integration of a developed RBDO method (RHM) for the thermal management applicationis considered an innovation in recent literature. Additionally, a reliable multi-objective optimization study is proposed, considering two objectives: the total volume of the heat sink and the discharge time to reach ambient temperature. The RHM optimization method and the non-dominated sorting genetics algorithm (C-NSGA-II) were adopted to search for the optimal and reliable model that offers the best trade-off between the two objectives. Besides, An advanced metamodel is developed to reduce simulation time, considering the large number of iterations involved in finding the optimal model
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Muhibullah. "Computation experimental DIC hybrid strategy for robust 3D ductile plastic law identification." Phd thesis, INSA de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00838763.
Abstract:
The objective of the thesis is to formulate a strategy that gives a robust identification of constitutive law from full-field measurements taking into account 3D effects. Model validation from global response of samples or structures has shortcomings that can be overcome by the use of full-field measurement techniques. Full-field measurement techniques offer the opportunity to acquire large amount of experimental data that might be useful in the context of identification of constitutive law parameters. Among the full field measurement techniques the most popular is digital and stereo image correlation. The existing strategies to make use of full-field data like the Virtual Field Method, the Equilibrium Gap Method, the Constitutive Equation Gap Method and the Reciprocity Method were limited to 2D applications. However, for a specimen with finite thickness 3D effects must be included. Most importantly, for the case of plasticity, stress triaxiality plays an important role. Its effect must therefore be accounted for in the modelling of the constitutive behaviour of the material. Thus in this thesis we propose a method to identify the parameters of an elasto-plastic constitutive law in which the mechanical model can have 3D kinematics. The strategy has been shown to be noise robust, almost independent of initial parameter guess and mesh refinement and allows differentiating between constitutive models with same global response on the basis of mean correlation error. The identification is shown to be good for both single and multiple cameras. The strategy validation is done for stainless steel. The global identified load displacement response of the strategy for mono and stereo mechanical image correlation is very close to the experiments. Lastly, the material parameters have been identified with very different initial guess but all converge to the same final values which show the robustness of the proposed strategy.
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Yaich, Ahmed. "Analyse de l’endommagement par fatigue et optimisation fiabiliste des structures soumises à des vibrations aléatoires." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR05.
Abstract:
Cette thèse porte sur l'analyse de l'endommagement par fatigue et optimisation fiabiliste des structures soumises à des vibrations aléatoires. Le but de l'optimisation fiabiliste est de trouver le compromis entre le coût et la fiabilité. Plusieurs méthodes, telles que la méthode hybride et la méthode OSF ont été développées. Ces méthodes ont été appliquées dans des cas statiques et certains cas dynamiques spécifiques. Dans la réalité les structures sont soumises à des vibrations aléatoires qui peuvent provoquer un endommagement par fatigue. Dans cette thèse on présente la stratégie numérique de calcul de l'endommagement par fatigue dans le domaine fréquentiel et on propose une extension des méthodes RBDO dans le cas des structures soumises à des vibrations aléatoires. Aussi, une méthode RHM est développée. Enfin,une application industrielle qui porte sur la modélisation de la partie mécanique du banc HALT est présentéThis thesis deals with the fatigue damage analysis and reliability-based design optimization (RBDO) of structures under random vibrations. The purpose of an RBDO method is to find the best compromise between cost and safety. Several methods, such as Hybrid method and OSF method have been developed. These methods have been applied in static cases and some specific dynamic cases. In fact, structures are subject to random vibrations which can cause a fatigue damage. In this thesis we present the strategy of calculation of the fatigue damage based on the Sines criterion in the frequency domain developed in our laboratory. Then, an extension of the RBDO methods in the case of structures subjected to random vibrations is proposed. Also, an RHM method is developed. Finally, we present an industrial application where we propose a model of the mechanical part of the HALT chamber
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Pouzet, Mathieu. "Détection et segmentation robustes de cibles mobiles par analyse du mouvement résiduel, à l'aide d'une unique caméra, dans un contexte industriel. Une application à la vidéo-surveillance automatique par drone." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLV002.
Abstract:
Nous proposons dans cette thèse une méthode robuste de détection d’objets mobiles depuis une caméra en mouvement montée sur un vecteur aérien de type drone ou hélicoptère. Nos contraintes industrielles sont particulièrement fortes : robustesse aux grands mouvements de la caméra, robustesse au flou de focus ou de bougé, et précision dans la détection et segmentation des objets mobiles. De même, notre solution doit être optimisée afin de ne pas être trop consommatrice en termes de puissance de calcul. Notre solution consiste en la compensation du mouvement global, résultant du mouvement de la caméra, puis en l’analyse du mouvement résiduel existant entre les images pour détecter et segmenter les cibles mobiles. Ce domaine a été particulièrement exploré dans la littérature, ce qui se traduit par une richesse des méthodes proposées fondamentalement différentes. Après en avoir étudié un certain nombre, nous nous sommes aperçus qu’elles avaient toutes un domaine d’applications restreint, malheureusement incompatible avec nos préoccupations industrielles. Pour pallier à ce problème, nous proposons une méthodologie consistant à analyser les résultats des méthodes de l’état de l’art de manière à en comprendre les avantages et inconvénients de chacune. Puis, des hybridations de ces méthodes sont alors mis en place. Ainsi, nous proposons trois étapes successives : la compensation du mouvement entre deux images successives, l’élaboration d’un arrière plan de la scène afin de pouvoir segmenter de manière correcte les objets mobiles dans l’image et le filtrage de ces détections par confrontation entre le mouvement estimé lors de la première étape et le mouvement résiduel estimé par un algorithme local. La première étape consiste en l’estimation du mouvement global entre deux images à l’aide d’une méthode hybride composée d’un algorithme de minimisation ESM et d’une méthode de mise en correspondance de points d’intérêt Harris. L’approche pyramidale proposée permet d’optimiser les temps de calcul et les estimateursrobustes (M-Estimateur pour l’ESM et RANSAC pour les points d’intérêt) permettent de répondre aux contraintes industrielles. La deuxième étape établit un arrière plan de la scène à l’aide d’une méthode couplant les résultats d’une différence d’images successives (après compensation) et d’une segmentation en régions. Cette méthode réalise une fusion entre les informations statiques et dynamiques de l’image. Cet arrière plan est ensuite comparé avec l’image courante afin de détecter les objets mobiles. Enfin, la dernière étape confronte les résultats de l’estimation de mouvement global avec le mouvement résiduel estimé par un flux optique local Lucas-Kanade afin de valider les détections obtenues lors de la seconde étape. Les expériences réalisées dans ce mémoire sur de nombreuses séquences de tests (simulées ou réelles) permettent de valider la solution retenue. Nous montrons également diverses applications possibles de notre méthode proposéeWe propose a robust method about moving target detection from a moving UAV-mounted or helicopter-mounted camera. The industrial solution has to be robust to large motion of the camera, focus and motion blur in the images, and need to be accurate in terms of the moving target detection and segmentation. It does not have to need a long computation time. The proposed solution to detect the moving targets consists in the global camera motion compensation, and the residual motion analysis, that exists between the successive images. This research domain has been widely explored in the literature, implying lots of different proposed methods. The study of these methods show us that they all have a different and limited application scope, incompatible with our industrial constraints. To deal with this problem, we propose a methodology consisting in the analysis of the state-of-the-art method results, to extract their strengths and weaknesses. Then we propose to hybrid them. Therefore, we propose three successive steps : the inter-frame motion compensation, thecreation of a background in order to correctly detect the moving targets in the image and then the filtering of these detections by a comparison between the estimated global motion of the first step and the residual motion estimated by a local algorithm. The first step consists in the estimation of the global motion between two successive images thanks to a hybrid method composed of a minimization algorithm (ESM) and a feature-based method (Harris matching). The pyramidal implementation allows to optimize the computation time and the robust estimators (M-Estimator for the ESM algorithm and RANSAC for the Harris matching) allow to deal with the industrial constraints. The second step createsa background image using a method coupling the results of an inter-frame difference (after the global motion compensation) and a region segmentation. This method merges the static and dynamic information existing in the images. This background is then compared with the current image to detect the moving targets. Finally, the last step compares the results of the global motion estimation with the residual motion estimated by a Lucas-Kanade optical flow in order to validate the obtained detections of the second step. This solution has been validated after an evaluation on a large number of simulated and real sequences of images. Additionally, we propose some possible applications of theproposed method
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VU, THANH-NAM, and 武成南. "Development of Robust Method for Deformation Measurement Using Hybrid Hexagonal Pattern." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sdhmex.
Abstract:
碩士國立中正大學
機械工程系研究所
106
In this thesis, a new robust grid-based method using the hybrid hexagonal pattern to measure the in-plane displacement and strain is proposed. Although digital image correlation (DIC), a non-contact strain measurement method, is a well-developed approach for this particular application, the measurement accuracy strongly depended on the size of random speckle and distribution of grey levels on the sample surface. Typically, inconsistent illumination and large deformation between two deformation states will cause large correlation errors. In contrast, the proposed grid-based method using the hexagonal pattern is relatively robust to the non-uniform illumination and suitable for the case under large deformation. The grid points of the pattern between each state are registered based on corner features. In this study, various types of distortion were applied to both the proposed pattern and DIC pattern with illumination and noise inference. The measurement errors of them are measured and compared. The experimental results show that when the specimen suffers severe deformations, the proposed method performances better than DIC. Keywords: displacement measurement, grid-based, hexagonal pattern, M-array
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LIANG, You-Yi, and 梁祐倚. "Optimal Design of Projector Lens with Free Form Surface by Hybrid Taguchi-Robust Multiple Criterion Optimization Method." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/76280973559700223265.
Abstract:
碩士高苑科技大學
電子工程研究所
101
With the trend of volume minimization, some of methods to improve the traditional zoom lens gradually raised, such as Liquid Lens and free-form surfaces. In the optical design,when the light passes through the optical system, the system path is zigzag and complex, so we can find some aberration from imaging surface. These aberrations include 1. Spherical aberration 2. Coma aberration 3. Distortion aberrations. We need free-form surface prism design by means of revise aberration.In this paper a U-type 2X zoom of free-form surfaces projector lens design with twoprisms will have less overall length than a general optical design , and use the Taguchi method were analyzed lens parameter optimization of Spherical Aberration and Coma Aberration by analysis of the optimal control parameters, and optimum level , the design of the type of the prism free-form surfaces polynomial order, finally hybrid Taguchi-RMCO(Robust Multiple Criterion Optimization) the best lens configuration combinations.The purpose of this study using hybrid Taguchi method - robust multi-objective optimization ways to improve its spherical aberration, coma aberration.To improve the efficiency, the ninth group is the best of this experimental group, spherical aberration improved 0.15%, while the coma is improved 0.33%.
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Cheng, Chao-Hsi, and 鄭兆希. "DEVELOPMENT OF A ROBUST HYBRID METHOD FOR IDENTIFICATION OF A ROTOR SYSTEM WITH HYDRODYNAMIC BEARING UNDER HIGH MEASUREMENT NOISE." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/50758986768798961993.
Abstract:
博士國立中正大學
機械工程所
95
This thesis proposes a specific methodology for hybrid system identification. This system can be used with multi-input, multi-output (MIMO), noise-polluted rotor-bearing systems, so as to increase identification accuracy. This hybrid method, which integrates a Kalman filter with an eigensystem realization algorithm (ERA), was developed to determine state space models. To further increase the accuracy of identifying high-frequency modes of systems, another identification methodology was developed to enhance the eigensystem realization algorithm (ERA), which entails doping an optimum signal, thereby providing all the methodology necessary for dealing with such systems. The doping signal is obtained by integrating an optimization technique with the singular value decomposition (SVD) technique. Through theoretical derivation, we can interpret the effectiveness of the optimum signal and the sufficient conditions for removing the noise by SVD. To demonstrate the capability of proposed methods, a rotor-bearing system has been adopted to explain and show the high noise-immunity, the capability of using a steady state response and obtaining a MIMO state space model, no matter the system is coupled or with high frequency mode. Simulation results show that the proposed ERA with a novel optimum signal can significantly improve system identification accuracy in comparison with the SVD technique alone or with the SVD technique combined with a traditional Butterworth filter in system identification of high frequency modes. Finally, the noise effect can be attenuated even under a low signal/noise ratio (S/N).
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Wang, Fang-Fang, and 王芳芳. "The evaluation of robust supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/38703404810470752184.
Abstract:
碩士國立成功大學
製造工程研究所碩博士班
95
The advance of internet and information technology has prompted the development of supply chain and related management techniques. Many information sharing strategies have been created, such as electronic point of sales, vendor manage inventory, e-shopping, emergency transportation and so on. The variation of environment will produce uncertainty and the levels of different performance criteria which are affected by different environments are distinct. As a result, it will increase decision difficulties when enterprises choose supply chain information sharing strategies. An effective and efficient supply chain strategy should not only have the ability to reduce cost, raise customer service level, but also maintain the robustness characteristic under uncertain environments to make sure the business operation efficiency and competitivity. Our research constructs uncertain scenarios by compounding noise factors which was proposed by Taguchi. And we observe the performance of different supply chain strategies under different uncertain environments by simulation of the Beer Game. Then we calculate Signal-to-Noise ratio of each criteria as the robustness performance index and make an overall evaluation among each criteria by multiple criteria decision making methods. To improve decision quality, reduce supply chain costs, and decision risks, we provide a systematic and efficient evaluation process of robust supply chain information strategies for decision makers. And the result of this research shows that combines electronic point of sales with emergency transportation and reduced supply chain strategy to be an integrated strategy will have better robustness characteristic under uncertain environments.
Books on the topic "Robust Hybrid Method (RHM)":
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Robust hybrid finite element methods for antennas and microwave circuits. Ann Arbor, Mich: University of Michigan, Radiation Laboratory, Dept. of Electrical Engineering and Computer Science, 1996.
Book chapters on the topic "Robust Hybrid Method (RHM)":
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Zhang, Zhenya, Deyun Lyu, Paolo Arcaini, Lei Ma, Ichiro Hasuo, and Jianjun Zhao. "Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness." In Computer Aided Verification, 595–618. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_29.
Abstract:
AbstractHybrid system falsification is an important quality assurance method for cyber-physical systems with the advantage of scalability and feasibility in practice than exhaustive verification. Falsification, given a desired temporal specification, tries to find an input of violation instead of a proof guarantee. The state-of-the-art falsification approaches often employ stochastic hill-climbing optimization that minimizes the degree of satisfaction of the temporal specification, given by its quantitative robust semantics. However, it has been shown that the performance of falsification could be severely affected by the so-called scale problem, related to the different scales of the signals used in the specification (e.g., rpm and speed): in the robustness computation, the contribution of a signal could be masked by another one. In this paper, we propose a novel approach to tackle this problem. We first introduce a new robustness definition, called QB-Robustness, which combines classical Boolean satisfaction and quantitative robustness. We prove that QB-Robustness can be used to judge the satisfaction of the specification and avoid the scale problem in its computation. QB-Robustness is exploited by a falsification approach based on Monte Carlo Tree Search over the structure of the formal specification. First, tree traversal identifies the sub-formulas for which it is needed to compute the quantitative robustness. Then, on the leaves, numerical hill-climbing optimization is performed, aiming to falsify such sub-formulas. Our in-depth evaluation on multiple benchmarks demonstrates that our approach achieves better falsification results than the state-of-the-art falsification approaches guided by the classical quantitative robustness, and it is largely not affected by the scale problem.
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El Hazzat, Soulaiman, and Mostafa Merras. "Robust Method for Estimating the Fundamental Matrix by a Hybrid Optimization Algorithm." In Lecture Notes on Data Engineering and Communications Technologies, 399–406. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15191-0_38.
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Kawaye, Floney P., and Michael F. Hutchinson. "Maize, Cassava, and Sweet Potato Yield on Monthly Climate in Malawi." In African Handbook of Climate Change Adaptation, 617–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_120.
Abstract:
AbstractClimate change and climate variability in Malawi have negatively affected the production of maize, a staple food crop. This has adversely affected food security. On the other hand, there have been increases in growing area, production, yield, consumption, and commercialization of both cassava and sweet potato. Factors behind these increases include the adaptive capacity of these crops in relation to climate change and variability, structural adjustment programs, population growth and urbanization, new farming technologies, and economic development. Cassava and sweet potato are seen to have the potential to contribute to food security and alleviate poverty among rural communities.This study used a simple generic growth index model called GROWEST to model observed yields of maize, cassava, and sweet potato across Malawi between 2001 and 2012. The method can be viewed as a hybrid approach between complex process-based crop models and typical statistical models. For each food crop, the GROWEST model was able to provide a robust correlation between observed yields and spatially interpolated monthly climate. The model parameters, which included optimum growing temperatures and growing seasons, were well determined and agreed with known values. This indicated that these models could be used with reasonable confidence to project the impacts of climate change on crop yield. These projections could help assess the future of food security in Malawi under the changing climate and assist in planning for this future.
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Ratschek, Helmut, and Jon Rokne. "The SCCI-Hybrid Method for 2D-Curve Tracing." In Geometric Computations with Interval and New Robust Methods, 185–218. Elsevier, 2003. http://dx.doi.org/10.1533/9780857099518.185.
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Kalia, R. "Recent Advances and Trends in ZnO Hybrid Nanostructures." In ZnO and Their Hybrid Nano-Structures, 86–131. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902394-4.
Abstract:
ZnO nanostructures are excellent candidates for use in the production of functional devices because to their low toxicity, robust thermal stability, excellent corrosion resistance, biocompatibility, high specific surface area, and high conductivity. In this chapter we have discussed the various nanostructures of ZnO and their kind, synthesis of hybrid ZnO nanostructure, modification in nanostructure of ZnO, Hybrid nanostructure of ZnO. In addition, we have discussed the various methods like Sol-gel method, Hydrothermal method and Green Synthesis method in detail for the synthesis of ZnO nanostructures. The effect of these methods on variation of nanostructures have been discussed in detail. It has been observed that because of its great sensitivity to the chemical environment, ZnO nanostructures have been extensively used in sensing applications. Also, the ZnO nano structures have been widely used in light emitting diodes and in solar cells because of its semiconducting nature. The ZnO is a n-type semiconductors and has a perfect bandgap of 3.37eV for its use in solar cell applications. Thus, this chapter provides a detailed discussion about the various nano structures of ZnO, the synthesis methods and various applications.
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Gupta, Shital, and Megha Kamble. "A Robust Algorithm of Digital Video Watermarking using ELM." In New Frontiers in Communication and Intelligent Systems, 649–59. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-66.
Abstract:
Image watermarking for copyright protection has become a widely studied subject with the sprawl of pirated content. A color, a monochrome – gray scale or a binary may be a watermark. The insertion of watermarks may be performed in a video domain unencoded or encoded. A robust technique of digital video watermarking using machine learning approach is based on Extreme Learning Machine (ELM) is proposed. Using the properties of hybrid transformations, robust features and robust zero watermarks can be extracted from videos can be built. Experimental findings show that the proposed algorithm is robust in high-efficiency video coding attacks with various parameters of quantization due the fast processing of frames. Regular image processing assaults, geometric attacks, frame-based attacks, and hybrid attacks can all be thwarted by this approach. Comparatively, the suggested video watermarking method can more accurately and completely recreate watermarking pictures.
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Gilbert, Hugo, Mohamed Ouaguenouni, Meltem Öztürk, and Olivier Spanjaard. "A Hybrid Approach to Preference Learning with Interaction Terms." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230351.
Abstract:
Preference learning is an essential component in numerous applications, such as recommendation systems, decision-making processes, and personalized services. We propose here a novel approach to preference learning that interleaves Gaussian Processes (GP) and Robust Ordinal Regression (ROR). A Gaussian process gives a probability distribution on the latent function values that generate users’ preferences. Our method extends the traditional non-parametric Gaussian process framework by approximating the latent function by a very flexible parameterized function, that we call θ-additive function, where θ is the parameter set. The set θ reflects the degree of sophistication of the generalized additive model that can potentially represent the user’s preferences. To learn what are the components of θ, we update a probability distribution on the space of all possible sets θ, depending on the ability of the parameterized function to approximate the latent function. We predict pairwise preferences by using the parameter set θ that maximizes the posterior distribution and by performing robust ordinal regression based on this parameter set. Experimental results on synthetic data demonstrate the effectiveness and robustness of our proposed methodology.
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Vacio, Rubén Jaramillo, Carlos Alberto Ochoa Ortiz Zezzatti, and Armando Rios. "Data Mining Applications in the Electrical Industry." In Logistics Management and Optimization through Hybrid Artificial Intelligence Systems, 380–402. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0297-7.ch015.
Abstract:
This chapter describes the experimental study partial discharges (PD) activities with artificial intelligent tools. The results present different patterns using a hybrid system with Self Organizing Maps (SOM) and Hierarchical clustering, this combination constitutes an excellent tool for exploration analysis of massive data such a partial discharge on underground power cables and electrical equipment. The SOM has been used for nonlinear feature extraction and the hierarchical clustering to visualization. The hybrid system is trained with different dataset using univariate phase-resolved distributions. The results show that the clustering method is fast, robust, and visually efficient.
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Moreno, Ramón, Manuel Graña, and Kurosh Madani. "A Robust Color Watershed Transformation and Image Segmentation Defined on RGB Spherical Coordinates." In Robotic Vision, 112–28. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2672-0.ch007.
Abstract:
The representation of the RGB color space points in spherical coordinates allows to retain the chromatic components of image pixel colors, pulling apart easily the intensity component. This representation allows the definition of a chromatic distance and a hybrid gradient with good properties of perceptual color constancy. In this chapter, the authors present a watershed based image segmentation method using this hybrid gradient. Oversegmentation is solved by applying a region merging strategy based on the chromatic distance defined on the spherical coordinate representation. The chapter shows the robustness and performance of the approach on well known test images and the Berkeley benchmarking image database and on images taken with a NAO robot.
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Chandran, Bhuvaneswari, P. Aruna, and D. Loganathan. "Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach." In Medical Imaging, 1885–910. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch077.
Abstract:
The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.
Conference papers on the topic "Robust Hybrid Method (RHM)":
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Samuel, Olusegun D., Venkateshwar R. Pathapalli, and Christopher C. Enweremadu. "Optimizing and Modelling Performance Parameters of IC Engine Fueled With Palm-Castor Biodiesel and Diesel Blends Combination Using RSM, ANN, MOORA and WASPAS Technique." In ASME 2022 16th International Conference on Energy Sustainability collocated with the ASME 2022 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/es2022-81146.
Abstract:
Abstract Biodiesel fuel properties and engine characteristics can be improved by using hybrid biodiesel and robust optimization tools. This study predicts the performance parameters of diesel engines fueled with castor-palm kernel biodiesel (CPKB) and diesel fuel blend using response surface methodology (RSM) and artificial neural network (ANN). Optimization of the performance parameters was also carried out using multi-criteria decision-making methods (WASPA S and MOORA) for the first time. The RSM and ANN were employed in predicting the performance parameters such as CPKB fuel blends (FB) (0–20 vol.%), engine load (EL) (0–50%), and engine speed (ES) (1000–2000 rpm) on the performance indicators viz. brake torque (BT), brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE). The Box-Behnken design was used for performing the experimental trials. The RSM model predicted the BT of 107.95 Nm, BP of 11.300 kW, BSFC of 0.057 kg/kWh, BTE of 15.147%, at the optimal level of CPKB blends of 20% (B20), engine load of 50%, and an engine speed of 1000 rpm, respectively. Results showed that based on the values of R2and average absolute deviation (AAD) obtained, the predictive capability of both RSM and ANN were within acceptable limits. The best experimental trial from the WASPAS method is the #20 experimental run and the parameter combination are FB-10%, EL-25, and ES-1500 rpm, whereas for the MOORA method, five such experimental trials were observed viz., #1 run: FB-0%, EL-0, and ES-1000 rpm, #2 run: FB-20%, EL-0 and ES-1000 rpm, #5 run: FB-0%, EL-0, and ES-2000 rpm, #6 run: FB-20%, EL-0, and ES-2000 rpm #11 run: FB-10%, EL-0, and ES-1500 rpm.
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Sheth, Prasham, Indranil Roychoudhury, Crispin Chatar, and José Celaya. "A Hybrid Physics-Based and Machine-Learning Approach for Stick/Slip Prediction." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/208760-ms.
Abstract:
Abstract Stick/slip causes the slowing down or speeding up of the Bottom Hole Assembly (BHA) that occurs when the energy generated by the rotary system on a drilling rig (in the form of surface rotation) fails to reach the drill bit. This condition results in a buildup of energy in the drillstring, which causes the bit rotation to speed up and slow down. In extreme cases, this release of energy can cause the BHA to stop or even reverse the BHA rotation. This rotation variation can also damage downhole tools, stabilizers, and produce belled connections. Currently, one option to alleviate this problem is for wellsite personnel to manually adjust the weight on bit (WOB) and rotations per minute (RPM) to reduce stick/slip. This option is based on the experience of the personnel. There is also an array of tools and techniques to reduce stick/slip. The aim of the work presented in this paper is to predict stick/slip using surface and downhole sensors for the next stand of drilling, which includes predicting WOB and rotations per minute (RPM) ranges associated with stick/slip classes and their probabilities. This paper presents a method that uses a physics-guided neural network (PGNN). The network leverages the physics models of drillstring systems and reinforces it with machine-learning models, such as fully connected neural networks, recurrent neural networks (RNN) (e.g. long short-term memory (LSTM), and Markov Recurrent Neural Network (MarkovRNN)), and different ensembles of these approaches. Hence the method used here is a hybrid of a physics based and a data-driven solution. The advantage of the hybrid method allows for an estimation of system response beyond the historical parameters, permitting a much better estimation of parameters. This results in a more robust solution for the prediction. These predictions will provide the drilling operator with an understanding of the different WOB and RPM ranges allowing them to transfer the maximum energy to the bit for rock destruction, without exciting the system into stick/slip. Results presented are developed for these approaches that have been applied to data collected over the last decade from different wellsites.
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Aslam, Billal, Bicheng Yan, Knut-Andreas Lie, Stein Krogstad, Olav Moyner, and Xupeng He. "DiagNet-A Hybrid Physics-Data Driven Model for Fractured Reservoir Simulation." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219110-ms.
Abstract:
Abstract Fractured reservoir simulation is important and applicable to understand many processes related to subsurface geo-energy recovery and storage, such as shale gas/oil, enhanced geothermal systems, and CO2 sequestration in basaltic rocks. However, such simulations are often computationally expensive to capture the high contrast of permeability and pore volume between matrix and fracture. Therefore, a reduced order model (ROM) can be extremely beneficial for fractured reservoir simulation to reduce computational costs for iterative history-matching and reservoir optimization workflows. In this study, we propose a novel ROM that flexibly honors fracture representations, namely DiagNet. In DiagNet, we generate the coarse matrix nodes based on the reservoir outlines, and add extra diagonal connections between non-neighboring matrix nodes if intersecting fractures traverse them, which avoids the inclusion of additional fracture nodes. Since dimensionality reduction methods, such as Principal Component Analysis (PCA) can facilitate the model parameterization, we adopt PCA to generate quality priors for sampling of the matrix transmissibility and pore volume arrays. Further, the DiagNet models are then calibrated to the well observation data (such as well flow rates and bottom-hole pressures), and a gradient-based optimization method is implemented in a general automatic-differentiable simulator framework to tune the model parameters (e.g., matrix/fracture transmissibilities, pore volumes, and well indices). Our results show that we can perform robust calibration of DiagNet based on the synthetic well observation data from a fine-scale reference simulation model. We have found that it is necessary to incorporate the dominant flow physics (i.e., water breakthrough) from the observation data to improve the training convergence and the prediction accuracy for DiagNet. Further, PCA for parameterization improves the convergence rate of model calibration, as compared to random initialization. Interpretation of the calibrated transmissibility field from DiagNet aligns with high connectivity regions from the fine-scale reference model. As a result, the new DiagNet is interpretable in terms of reservoir connectivity or geology and thus can be used for reservoir history-matching using observation data from the field and the further optimization.
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Chen, Aron. "A Hybrid Control Method for Robust Vehicle Platooning." In 2021 3rd International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT). IEEE, 2021. http://dx.doi.org/10.1109/isrimt53730.2021.9596716.
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Fazli, Saeid, Hamed Moradi Pour, and Hamed Bouzari. "A robust hybrid movement detection method in dynamic background." In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2009. http://dx.doi.org/10.1109/ecticon.2009.5137244.
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Gencturk, Beste, Vasif Nabiyev, Guzin Ulutas, and Mustafa Ulutas. "A hybrid image authentication method robust to JPEG attacks." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830549.
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Wang, Mengfan, Ling Wang, Jian Xie, Tao Zhang, Chuang Han, and Yanyun Gong. "A Hybrid Interference Suppression Method Based On Robust Beamforming." In 2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021. http://dx.doi.org/10.1109/iwcmc51323.2021.9498890.
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Abdolrasol, Maher G. M., and Saad Mekhilef. "Robust hybrid anti-islanding method for inverter-based distributed generation." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5685901.
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Fujio, Shunsuke, Chikara Kojima, Toshihiro Shimura, Kenichi Nishikawa, Kazuyuki Ozaki, Zhengyi Li, Atsushi Honda, et al. "Robust beamforming method for SDMA with interleaved subarray hybrid beamforming." In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2016. http://dx.doi.org/10.1109/pimrc.2016.7794677.
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Cho, Youngchae, Takayuki Ishizaki, and Jun-Ichi Imura. "Hybrid Method of Two-Stage Stochastic and Robust Unit Commitment." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796153.
Reports on the topic "Robust Hybrid Method (RHM)":
1
Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
Abstract:
Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.
Abstract:
The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.