Relevant bibliographies by topics / Online Model Adaptive

Academic literature on the topic 'Online Model Adaptive'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Online Model Adaptive"

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Harati, Hoda, Cherng-Jyh Yen, Chih-Hsiung Tu, Brandon J. Cruickshank, and Shadow William Jon Armfield. "Online Adaptive Learning." International Journal of Web-Based Learning and Teaching Technologies 15, no. 4 (October 2020): 18–35. http://dx.doi.org/10.4018/ijwltt.2020100102.

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Adaptive Learning (AL), a new web-based online learning environment, requires self-regulated learners who act autonomously. However, to date, there appears to be no existing model to conceptualize different aspects of SRL skills in Adaptive Learning Environments (ALE). The purpose of this study was to design and empirically evaluate a theoretical model of Self-Regulated Learning (SRL) in ALE's and the related questionnaire as a measurement tool. The proposed theoretical model, namely, “Adaptive Self-Regulated Learning (ASR)”, was specified to incorporate the SRL skills into ALE's. Based on this model, the Adaptive Self-regulated Learning Questionnaire (ASRQ) was developed. The reliability and validity of the ASRQ were evaluated via the review of a content expert panel, the Cronbach's alpha coefficients, and confirmatory factor analysis. Overall, the results supported the theoretical framework and the new ASRQ in an ALE. In this article, the theoretical and practical implications of the findings were discussed.
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Lam, Jostinah, Eko Supriyanto, Faris Yahya, Muhammad Haikal Satria, Suhaini Kadiman, Aizai Azan, and Amiliana Soesanto. "Online Adaptive Coronary Heart Disease Risk Prediction Model." MATEC Web of Conferences 125 (2017): 02071. http://dx.doi.org/10.1051/matecconf/201712502071.

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Cen, Zhaohui. "Capacitance Online Estimation Based on Adaptive Model Observer." MATEC Web of Conferences 77 (2016): 02006. http://dx.doi.org/10.1051/matecconf/20167702006.

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Vojir, Tomas, Jiri Matas, and Jana Noskova. "Online adaptive hidden Markov model for multi-tracker fusion." Computer Vision and Image Understanding 153 (December 2016): 109–19. http://dx.doi.org/10.1016/j.cviu.2016.05.007.

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Peherstorfer, Benjamin. "Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling." SIAM Journal on Scientific Computing 42, no. 5 (January 2020): A2803—A2836. http://dx.doi.org/10.1137/19m1257275.

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Meepung, Tippawan, Sajeewan Pratsri, and Prachyanun Nilsook. "Interactive Tool in Digital Learning Ecosystem for Adaptive Online Learning Performance." Higher Education Studies 11, no. 3 (July 12, 2021): 70. http://dx.doi.org/10.5539/hes.v11n3p70.

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The objective of this research was as follows: 1) to develop an interactive tool in a digital learning ecosystem for adaptive online learning performance; 2) to carry out a suitability assessment of this process. The documentary research method was used in this study. The results showed a model of an interactive tool in a digital learning ecosystem for adaptive online learning performance consisted of two phases. Phase 1: The development of an interactive tool in a digital learning ecosystem for adaptive online learning performance. This includes the following four design steps: 1) Reviewed literature and previous studies regarding an interactive tool, a digital learning ecosystem, and adaptive online learning performance to study the model, characteristics, and previous research. 2) Studied relevant research of an interactive tool in a digital learning ecosystem for adaptive online learning performance. 3) Designed an adaptive online learning performance model using an interactive tool in a digital learning ecosystem. 4) Developed a digital learning ecosystem. Phase 2: Evaluated the appropriateness of the interactive tool for an adaptive online learning performance model; this was checked for suitability by twelve experts and resulted in a conclusion. The results of the suitability evaluation revealed that the interactive tool for adaptive online learning performance was at the highest level.
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Kalhor, Ahmad, Babak N. Araabi, and Caro Lucas. "An online predictor model as adaptive habitually linear and transiently nonlinear model." Evolving Systems 1, no. 1 (July 6, 2010): 29–41. http://dx.doi.org/10.1007/s12530-010-9004-z.

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Zhao, Jun, Xian Wang, Guanbin Gao, Jing Na, Hongping Liu, and Fujin Luan. "Online Adaptive Parameter Estimation for Quadrotors." Algorithms 11, no. 11 (October 25, 2018): 167. http://dx.doi.org/10.3390/a11110167.

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The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be used for parameter estimation. Then, an expression of the parameter estimation error is derived by introducing a set of auxiliary filtered variables. Moreover, an augmented matrix is constructed based on the obtained auxiliary filtered variables, which is then used to design new adaptive laws to achieve exponential convergence under the standard persistent excitation (PE) condition. Finally, a simulation and an experimental verification for a typical quadrotor system are shown to illustrate the effectiveness of the proposed method.
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Liu, Hong Jiang. "Adaptive Interacting Multiple Model Unscented Particle Filter Tracking Algorithm." Applied Mechanics and Materials 190-191 (July 2012): 906–10. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.906.

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In order to study the tracking problem of maneuvering image sequence target in complex environment with multi-sensor array, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The motion array tracking system dynamic model is established, and initialized probability density function also is defined based on unscented transformation, after that, the measured covariance and state covariance are online adjusted by measured residual and adaptive factor, then the self-adapting capability of filter gain and the real-time capability of posterior probability density function are improved. Finally, the simulation results between different algorithms show the validity and superiority of the presented algorithm in tracking accuracy, stability and real-time capability.
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Xu, Zhihao, and Xuefeng Zhou. "Online optimization based adaptive tracking control for redundant manipulators with model uncertainties." Filomat 34, no. 15 (2020): 5049–58. http://dx.doi.org/10.2298/fil2015049x.

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Tracking control of robot manipulators is always a fundamental problem in robot control, especially for redundant manipulators with higher DOFs. This problem may become more complicated when there exist uncertainties in the robot model. In this paper, we propose an adaptive tracking controller considering the uncertain physical parameters. Based on the coordinate feedback, a Jacobian adaption strategy is firstly built by updating kinematic parameters online, in which neither cartesian velocity nor joint acceleration is required, making the controller much easier to built. Using the Pseudo-inverse method of Jacobian, the optimal repeatability solution is achieved as the secondary task. Using Lyapunov theory, we have proved that the tracking errors of the end-effector asymptotically converge to zero. Numerical simulations are provided to validate the effectiveness of the proposed tracking method.
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Dissertations / Theses on the topic "Online Model Adaptive"

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Onay, Durdu Pinar. "A Distributed Online Curriculum And Courseware Development Model." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608989/index.pdf.

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A distributed online curriculum and courseware (DONC2) development model is proposed in this study. Collaborative courseware development teams which may work in distributed academic or private institutions who need to develop higher quality, reduced cost, on time products are the users of DONC2 development model. The related features from the disciplines of instructional design and software engineering were combined and concepts like usability, especially in terms of formative and summative evaluation, interoperability and reusability were integrated into the model. The research is conducted as a collective case study, including four cases with distinctive characteristics to reveal the several practices in online curriculum and courseware development work. The DONC2 development model was proposed using the results gathered from the investigated cases and a literature survey. The model uses the iterative incremental and agile software development approaches in order to overcome the disadvantages of other linear system development approaches. This enables building releasable products in short time periods with increased quality. Furthermore, continuous communication, evaluation and feedback as well as good project management and readiness to adapt to changes are integrated as the essential characteristics. DONC2 development model is different than previous linear and non-adaptive models in all of these aspects.
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Davis, Darrel R. "The model-based systematic development of LOGIS online graphing instructional simulator." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002271.

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Feng, Jianshe. "Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267012325542.

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Rogers-Ostema, Patrick J. "Building and using a model of insurgent behavior to avoid IEDS in an online video game." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4112.

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Åfeldt, Tom. "Adaptive Steering Behaviour for Heavy Duty Vehicles." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215134.

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Today the majority of the driver assistance systems are rule-basedcontrol systems that help the driver control the truck. But driversare looking for something more personal and exible that can controlthe truck in a human way with their own preferences. Machine learningand articial intelligence can help achieve this aim. In this studyArticial Neural Networks are used to model the driver steering behaviourin the Scania Lane Keeping Assist. Based on this, trajectoryplanning and steering wheel torque response are modelled to t thedriver preference. A model predictive controller can be used to maintainstate limitations and to weigh the two modelled driver preferencestogether. Due to the diculties in obtaining an internal plant modelfor the model predictive controller a variant of a PI-controller is addedfor integral action instead. The articial neural network also containsan online learning feature to further customize the t to the driverpreference over time.
Idag används till största del regelbaserade reglersystem förförarassistanssystem i lastbilar. Men lastbilschaufförer vill ha någotmer personligt och flexibelt, som kan styra lastbilen på ett mänskligtsätt med förarens egna preferenser. Maskininlärning och artificiell intelligenskan hjälpa till för att uppnå detta mål. I denna studie användsartificiella neurala nätverk för att modellera förarens styrbeteende genomScania Lane Keeping Assist. Med användning av detta modellerasförarens preferenser med avseende på placering på vägbanan och momentpåslag på ratten. En modell prediktiv kontroller kan användas föratt begränsa tillstånd och för att väga de två modellerade preferensernamot varann. Eftersom det var mycket svårt att ta fram den internaprocessmodellen som krävdes för regulatorn används istället en variantav en PI-kontroller för att styra lastbilen. De artificiella neuralanätverken kan också tillåtas att lära sig under körning för att anpassasig till förarens preferenser över tid.
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Samwel, Emad. "Toward the Development and Implementation of Personalized, Adaptive, and Comprehensive E-learning Systems." Diss., NSUWorks, 2016. https://nsuworks.nova.edu/gscis_etd/373.

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Enrollment in online courses is increasing at a much higher rate than enrollment in on campus courses. Initially, online systems were developed by moving course content from in-class courses as is to an online platform. Later, Web 2.0 technology was implemented in order to improve students’ online engagement. These systems considered all students as one homogeneous group and ignored the fact that different students learn in different ways and at different speeds. Later, adaptive online learning systems were developed based on the assumption that if the instructional approach matches the student learning style, student performance and experience will improve. The use of these systems yielded mixed results because there is no agreement on what, how, and when to adapt instructions. The problem is that there is still a lack of empirical evidence about which online learning system’ design is the most effective, efficient, and engaging. There were two goals for this study. The first was to develop a new instructional theory and design model suitable for personalizing and adapting online learning. The first goal was achieved by developing student personalized, adaptive, and comprehensive e-learning spaces instructional theory and design model. This theory is based on finding the best fit among student characteristics, knowledge domain objectives, and technology used in delivering the online course. The second goal was to implement the newly developed theory and design model in an e-learning system prototype. This goal was achieved by developing and internally validating the e-learning system prototype by utilizing a panel of five instructional design experts. The Delphi method was used to solicit input from the expert panel in three rounds of validation. The validation process resulted in the experts’ consensus that the prototype incorporated the instructional theory and design model well and that this instructional theory holds the promise of increasing online learning courses’ effectiveness, efficiency, and student engagement.
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Oudin, Simon. "Commande adaptative pour avion de transport tolérante aux erreurs de modèle et aux pannes." Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0033.

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Cette thèse s'intéresse à l'adaptation des lois de pilotage d'un avion de transport civil aux différentes incertitudes qui peuvent affecter sa dynamique. Le procédé de pilotage adaptatif est censé fonctionner en temps réel à bord de l'avion afin d'optimiser la performance boucle fermée en fonction des conditions dans lesquelles il évolue. Les incertitudes peuvent être liées à la méconnaissance des conditions de vol (par exemple la vitesse et l'altitude), à des non-linéarités aérodynamiques inconnues ou encore à la méconnaissance du pilote aux commandes. Les procédés adaptatifs qui répondent à ces problèmes se doivent d'être performants sur l'ensemble du domaine opérationnel de l'avion en présence de perturbations réalistes. D'autres contraintes spécifiques peuvent être ajoutées en fonction du contexte (par exemple des charges limites, la stabilité aéroélastique, etc.). Plusieurs méthodes adaptatives sont testées afin d'adapter le système aux larges incertitudes qui le composent. Elles associent en général un estimateur en ligne (aussi appelé loi de mise-à-jour) à une loi de commande structurée. La synthèse de ces deux éléments peut être réalisée simultanément pour les méthodes adaptatives dites " directes ", comme par exemple le Model Reference Adaptative Control qui utilise la stabilité au sens de Lyapounov. Mais cette synthèse peut aussi être découplée pour les méthodes adaptatives dites "indirectes", ce qui offre un large choix de techniques pour chaque élément (comme les Moindres Carrés pour l'estimation de paramètres physiques incertains et la synthèse sous forme LFR pour le correcteur). Le choix de la méthode dépend fortement du contexte applicatif et des nombreuses contraintes associées. Trois applications sont au cœur de ce mémoire. Elles traitent de l'ajustement de lois de guidage à un modèle pilote inconnu, du contrôle longitudinal de non-linéarités de l'avion, et de la mise au point de lois longitudinale et latérale de pilotage manuel qui s'adaptent à des conditions de vol inconnues. Des méthodes avancées d'analyse linéaire et non-linéaire (dérivées de la µ-analyse et d'algorithmes d'optimisation) sont aussi mises en place pour valider ces systèmes sophistiqués adaptatifs en temps réel. D'une façon générale, les méthodes adaptatives indirectes ont donné le plus de satisfaction. Leur performance est aussi bonne que celle des méthodes directes, mais le fait qu'elles estiment en ligne des paramètres physiques facilite la surveillance temps réel du procédé adaptatif et sa validation
This thesis deals with adapting flight control laws of a civil transport aircraft to various incertainties which can affect its behaviour. The adaptive flight control system is supposed to run in real time onboard the airplane so that its closed-loop performance is optimized with respect to the current conditions. These incertainties may be linked to unknown flight conditions (e.g. unknown airspeed and altitude), or unknown aerodynamics non-linearities or even unknown behaviour of the pilot in command. The adaptive schemes that are derived to answer these problems must be valid on the whole flight envelope with realistic disturbances but other additional contraints may exist depending on the context (e.g. loads limits, aeroelastic stability, etc.). To accommodate for large uncertainties on the system, adaptive methods are investigated. They usually combine an online estimator (also called an update law) with a structured flight control law. The synthesis of both elements may be simultaneous on 'direct' adaptive methods, e.g. on Model Reference Adaptive Control, using Lyapunov's stability theory. But it can also be decoupled on 'indirect' adaptive methods, giving a full spectrum of techniques for both elements (such as Least-Squares for estimating unknown physical parameters and the LFR framework for designing controllers). The choice of a specific method really depends on the application context and the related constraints.Three applications are the core of this report. They deal with adjusting guidance law to the pilot's unknown behaviour, controlling a longitudinal non-linearity, and providing manual longitudinal and lateral flight control laws which adapt to unknown flight conditions. Advanced linear and non-linear analysis techniques (based on µ-analysis or on optimization algorithms) are also applied to validated these sophisticated real-time adaptive systems. Results showed that indirect adaptive schemes were generally the most satisfactory. Their performance is similar to the one of direct schemes but as indirect methods provide physical parameter estimates, real-time monitoring and offline validation seem quite easier
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Sagha, Hossein. "Development of innovative robust stability enhancement algorithms for distribution systems containing distributed generators." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/91052/1/Hossein_Sagha_Thesis.pdf.

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This project was a step forward in improving the voltage profile of traditional low voltage distribution networks with high photovoltaic generation or high peak demand. As a practical and economical solution, the developed methods use a Dynamic Voltage Restorer or DVR, which is a series voltage compensator, for continuous and communication-less power quality enhancement. The placement of DVR in the network is optimised in order to minimise its power rating and cost. In addition, new approaches were developed for grid synchronisation and control of DVR which are integrated with the voltage quality improvement algorithm for stable operation.
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Ahlberg, Jesper, and Esbjörn Blomquist. "Online Identification of Running Resistance and Available Adhesion of Trains." Thesis, Linköpings universitet, Fordonssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71301.

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Two important physical aspects that determine the performance of a running train are the total running resistance that acts on the whole train moving forward, and the available adhesion (utilizable wheel-rail-friction) for propulsion and breaking. Using the measured and available signals, online identification of the current running resistance and available adhesion and also prediction of future values for a distance ahead of the train, is desired. With the aim to enhance the precision of those calculations, this thesis investigates the potential of online identification and prediction utilizing the Extended Kalman Filter. The conclusions are that problems with observability and sensitivity arise, which result in a need for sophisticated methods to numerically derive the acceleration from the velocity signal. The smoothing spline approximation is shown to provide the best results for this numerical differentiation. Sensitivity and its need for high accuracy, especially in the acceleration signal, results in a demand of higher sample frequency. A desire for other profound ways of collecting further information, or to enhance the models, arises with possibilities of future work in the field.
Två viktiga fysikaliska aspekter som bestämmer prestandan för ett tåg i drift är det totala gångmotståndet som verkar på hela tåget, samt den tillgängliga adhesionen (användbara hjul-räl-friktionen) för framdrivning och bromsning. Från de tillgängliga signalerna önskas identifiering, samt prediktering, av dessa två storheter, under drift. Med målet att förbättra precisionen av dessa skattningar undersöker detta examensarbete potentialen av skattning och prediktering av gångmotstånd och adhesion med hjälp av Extended KalmanFiltering. Slutsatsen är att problem med observerbarhet och känslighet uppstår, vilket resulterar i ett behov av sofistikerade metoder att numeriskt beräkna acceleration från en hastighetssignal. Metoden smoothing spline approximation visar sig ge de bästa resultaten för denna numeriska derivering. Känsligheten och dess medförda krav på hög precision, speciellt på accelerationssignalen, resulterar i ett behov av högre samplingsfrekvens. Ett behov av andra adekvata metoder att tillföra ytterligare information, eller att förbättra modellerna, ger upphov till möjliga framtida utredningar inom området.
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Rehder, Eike, and Jürgen Karla. "Adaption des Technology Acceptance Model für den Onlinevertrieb von Versicherungsprodukten." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-142797.

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Books on the topic "Online Model Adaptive"

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Ban, Jae-Chun. Data sparseness and online pretest item calibration/scaling methods in CAT. Iowa City, Iowa: ACT, Inc., 2002.

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Ban, Jae-Chun. Data sparseness and online pretest item calibration/scaling methods in CAT. Iowa City, Iowa: ACT, Inc., 2002.

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Vaez-Zadeh, Sadegh. Parameter Estimation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198742968.003.0007.

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In this chapter, the estimation of permanent magnetic synchronous (PMS) motor parameters, including stator winding resistance, motor inductances, and magnitude of permanent magnet flux linage, is presented in two main categories, i.e., offline and online. Several offline schemes, including DC and AC standstill tests, no-load test, load test, and vector control schemes, are presented for estimation of all the motor parameters. Major online schemes used in the estimation of PMS motor parameters are also presented in this chapter. They include closed-loop observer-based estimation, model reference adaptive system (MRAS)-based estimation, recursive least-squares (RLS) estimation, and extended Kalman filter scheme. The online schemes take into account the motor parameter variations during motor operation. The motor model, estimation procedure, and the connection of estimation systems to the motor control system are discussed for each parameter estimation scheme.
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Jiménez, Catalina, Julen Requejo, Miguel Foces, Masato Okumura, Marco Stampini, and Ana Castillo. Silver Economy: A Mapping of Actors and Trends in Latin America and the Caribbean. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003237.

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Latin America and the Caribbean, unlike other regions, is still quite young demographically: people over age 60 make up around 11% of the total population. However, the region is expected to experience the fastest rate of population aging in the world over the coming decades. This projected growth of the elderly population raises challenges related to pensions, health, and long-term care. At the same time, it opens up numerous business opportunities in different sectorshousing, tourism, care, and transportation, for examplethat could generate millions of new jobs. These opportunities are termed the “silver economy,” which has the potential to be one of the drivers of post-pandemic economic recovery. Importantly, women play key roles in many areas of this market, as noted in the first report published by the IDB on this subject (Okumura et al., 2020). This report maps the actors whose products or services are intended for older people and examines silver economy trends in the region by sector: health, long-term care, finance, housing, transportation, job market, education, entertainment, and digitization. The mapping identified 245 actors whose products or services are intended for older people, and it yielded three main findings. The first is that the majority of the actors (40%) operate in the health and care sectors. The prevalence of these sectors could be due to the fact that they are made up of many small players, and it could also suggest a still limited role of older people in active consumption, investment, and the job market in the region. The second finding is that 90% of the silver economy actors identified by the study operate exclusively in their countries of origin, and that Mexico has the most actors (47), followed by the Southern Cone countriesBrazil, Chile, and Argentinawhich have the regions highest rates of population aging. The third finding is that private investment dominates the silver economy ecosystem, as nearly 3 out of every 4 actors offering services to the elderly population are for-profit enterprises. The sectors and markets of the silver economy differ in size and degree of maturity. For example, the long-term care sector, which includes residential care settings, is the oldest and has the largest number of actors, while sectors like digital, home automation, and cohousing are still emerging. Across all sectors, however, there are innovative initiatives that hold great potential for growth. This report examines the main development trends of the silver economy in the region and presents examples of initiatives that are already underway. The health sector has a wealth of initiatives designed to make managing chronic diseases easier and to prevent and reduce the impact of functional limitations through practices that encourage active aging. In the area of long term careone of the most powerful drivers of job creationinitiatives to train human resources and offer home care services are flourishing. The financial sector is beginning to meet a wide range of demands from older people by offering unique services such as remittances or property management, in addition to more traditional pensions, savings, and investment services. The housing sector is adapting rapidly to the changes resulting from population aging. This shift can be seen, for example, in developments in the area of cohousing or collaborative housing, and in the rise of smart homes, which are emerging as potential solutions. In the area of transportation, specific solutions are being developed to meet the unique mobility needs of older people, whose economic and social participation is on the rise. The job market offers older people opportunities to continue contributing to society, either by sharing their experience or by earning income. The education sector is developing solutions that promote active aging and the ongoing participation of older people in the regions economic and social life. Entertainment services for older people are expanding, with the emergence of multiple online services. Lastly, digitization is a cross-cutting and fundamental challenge for the silver economy, and various initiatives in the region that directly address this issue were identified. Additionally, in several sectors we identified actors with a clear focus on gender, and these primarily provide support to women. Of a total of 245 actors identified by the mapping exercise, we take a closer look at 11 different stories of the development of the silver economy in the region. The featured organizations are RAFAM Internacional (Argentina), TeleDx (Chile), Bonanza Asistencia (Costa Rica), NudaProp (Uruguay), Contraticos (Costa Rica), Maturi (Brazil), Someone Somewhere (Mexico), CONAPE (Dominican Republic), Fundación Saldarriaga Concha (Colombia), Plan Ibirapitá (Uruguay), and Canitas (Mexico). These organizations were chosen based on criteria such as how innovative their business models are, the current size and growth potential of their initiatives, and their impact on society. This study is a first step towards mapping the silver economy in Latin America and the Caribbean, and the hope is to broaden the scope of this mapping exercise through future research and through the creation of a community of actors to promote the regional integration of initiatives in this field.
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Book chapters on the topic "Online Model Adaptive"

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Anagnostopoulos, Theodoros, Christos Anagnostopoulos, and Stathes Hadjiefthymiades. "An Online Adaptive Model for Location Prediction." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 64–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11482-3_5.

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Pepelyshev, Andrey, Yuri Staroselskiy, and Anatoly Zhigljavsky. "Adaptive Designs for Optimizing Online Advertisement Campaigns." In mODa 11 - Advances in Model-Oriented Design and Analysis, 199–208. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31266-8_23.

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Sousa, Ricardo, and João Gama. "Online Multi-label Classification with Adaptive Model Rules." In Advances in Artificial Intelligence, 58–67. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44636-3_6.

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Lei, Guoping, Xiuying Luo, Sen Yang, and Ke Xiao. "Adaptive Online Learning Model Based on Big Data." In Application of Intelligent Systems in Multi-modal Information Analytics, 643–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51431-0_92.

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Chang, KaiYeuh, and Shang-Hong Lai. "Adaptive Object Tracking with Online Statistical Model Update." In Computer Vision – ACCV 2006, 363–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_37.

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Tang, Shuo, Longfei Zhang, Xiangwei Tan, Jiali Yan, and Gangyi Ding. "Online Adaptive Multiple Appearances Model for Long-Term Tracking." In Communications in Computer and Information Science, 501–16. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_42.

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Wang, Hui, Daoying Pi, Youxian Sun, Chi Xu, and Sizhen Chu. "Fast Online SVR Algorithm Based Adaptive Internal Model Control." In Advances in Neural Networks - ISNN 2006, 922–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_136.

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Wang, Shi, Wen Gao, Tiejun Huang, Jiyong Ma, Jintao Li, and Hui Xie. "Adaptive Online Retail Web Site Based on Hidden Markov Model." In Web-Age Information Management, 177–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45151-x_17.

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Metzger, Andreas, Clément Quinton, Zoltán Ádám Mann, Luciano Baresi, and Klaus Pohl. "Feature Model-Guided Online Reinforcement Learning for Self-Adaptive Services." In Service-Oriented Computing, 269–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65310-1_20.

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Yi, Shenglun, and Xuemei Ren. "Robust Online Filter Based on a Second-Order Adaptive Model." In Lecture Notes in Electrical Engineering, 691–98. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8458-9_74.

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Conference papers on the topic "Online Model Adaptive"

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Wei, Xiaotao, Houkuan Huang, Shengfeng Tian, Xiaohui Yang, and Baomin Xu. "An Online Adaptive Network Anomaly Detection Model." In 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.97.

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Kostadinov, Dimche, and Davide Scaramuzza. "Online Weight-adaptive Nonlinear Model Predictive Control." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341495.

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Suh, Namjoon, Ruizhi Zhang, and Yajun Mei. "Adaptive Online Monitoring of the Ising model." In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2019. http://dx.doi.org/10.1109/allerton.2019.8919824.

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Wartiningsih and Herman Dwi Surjono. "Adaptive E-Learning Model in Learning Personality Characters." In International Conference on Online and Blended Learning 2019 (ICOBL 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200521.004.

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Mariya, M. R., S. V. Shashank, and M. Mamta. "Robust Control of Adaptive Model Predictive Control using Online Model Estimation." In 2022 Australian & New Zealand Control Conference (ANZCC). IEEE, 2022. http://dx.doi.org/10.1109/anzcc56036.2022.9966969.

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Yang, Peng, Peilin Zhao, and Xin Gao. "Bandit Online Learning on Graphs via Adaptive Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/415.

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Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full information label is provided for each node, which is violated in many practical applications where labeling is expensive and the oracle may only tell whether the prediction is correct or not. To address this issue, we propose a bandit online algorithm on graphs. It derives per-instance confidence region of the prediction, from which the model can be learned adaptively to minimize the online regret. Experiments on benchmark graph datasets show that the proposed bandit algorithm outperforms state-of-the-art competitors, even sometimes beats the algorithms using full information label feedback.
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Arevalo-Castiblanco, Miguel, Cesar Uribe, and Eduardo Mojica-Nava. "Model Reference Adaptive Control for Online Policy Adaptation and Network Synchronization." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/202107242.

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We propose an online adaptive synchronization method for leader-follower networks of heterogeneous agents. Synchronization is achieved using a distributed Model Reference Adaptive Control (DMRAC-RL) that enables the improved performance of Reinforcement Learning (RL)-trained policies on a reference model. The leader observes the performance of the reference model, and the followers observe the states and actions of the agents they are connected to, but not the reference model. Notably, both the leader and followers models might differ from the reference model the RL-control policy was trained. DMRAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of an augmented input to solve the distributed control problem. Numerical examples of the synchronization of a network of inverted pendulums support our theoretical findings.
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Arevalo-Castiblanco, Miguel, Cesar Uribe, and Eduardo Mojica-Nava. "Model Reference Adaptive Control for Online Policy Adaptation and Network Synchronization." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202107242.

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We propose an online adaptive synchronization method for leader follower networks of heterogeneous agents. Synchronization is achieved using a distributed Model Reference Adaptive Control (DMRAC-RL) that enables the improved performance of Reinforcement Learning (RL)-trained policies on a reference model. The leader observes the performance of the reference model, and the followers observe the states and actions of the agents they are connected to, but not the reference model. Notably, both the leader and followers models might differ from the reference model the RL-control policy was trained. DMRAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of an augmented input to solve the distributed control problem. Numerical examples of the synchronization of a network of inverted pendulums support our theoretical findings
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Todorova, Margarita, Oleg Asenov, and Donika Valcheva. "A 3D model for adaptive multi-lingual online assessment." In the 15th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2659532.2659595.

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Liu, Zhu, and Qian Huang. "Adaptive anchor detection using online trained audio/visual model." In Electronic Imaging, edited by Minerva M. Yeung, Boon-Lock Yeo, and Charles A. Bouman. SPIE, 1999. http://dx.doi.org/10.1117/12.373545.

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Reports on the topic "Online Model Adaptive"

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Wandeler, Christian, and Steve Hart. The Central Valley Transportation Challenge. Mineta Transportation Institute, December 2022. http://dx.doi.org/10.31979/mti.2022.2029.

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The Central Valley Transportation Challenge provides underserved minority students, who are primarily from rural areas, with high quality transportation-related educational experiences so that they learn about transportation-related topics and opportunities in transportation careers. The CVTC is a project-based learning program that brings university faculty and students to K–12 classrooms in rural areas. The project operated with three main objectives: (1) support K–12 teachers’ understanding and implementation of the CVTC programs; (2) connect K–12 students with university faculty and students, and transportation professionals through the CVTC program; and (3) develop an online hub with transportation-related lesson plans and sequences. The results of this study are reported as five case studies and a description of the online hub. The case studies illustrate how different pedagogical approaches and uses of technology were implemented and how the project connections between the schools, community members and professionals from transportation-related fields were developed. In addition, to support the sustainability of transportation-related learning across subsequent years, the research team created an online transportation resource repository. This hub was populated with lessons and units developed by pedagogical and content experts. The lessons cover the grades K–12 and range from brief lessons to very engaging and holistic two-week-long lesson sequences. The CVTC has proven to be a highly flexible and adaptive model due to the use of technology and the teachers’ experience and pedagogical expertise. The timing of the program during the COVID-19 pandemic also provided the students that were learning from home with an engaging learning experience and some relief for teachers who were already dealing with a lot of adjustments. In that sense, the program reached traditionally underserved students, but did so in a critical time where these students faced even more obstacles.
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Comparative Analysis on Fuel Consumption Between Two Online Strategies for P2 Hybrid Electric Vehicles: Adaptive-RuleBased (A-RB) vs Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0740.

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Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made. Second, the two sub-optimal strategies are described in detail and then implemented on the HEV model to derive the fuel-optimal control strategy managing the power split between the thermal and electric engine to satisfy the driver's power request, including the engine on/off operating mode and the best gear selection. Finally, the two proposed strategies are tested on different driving cycles and then compared to other commercial strategies available in literature, such as the Equivalent Consumption Minimization Strategy (ECMS) and a RuleBased (RB) strategy. The results show that the A-ECMS is more conservative in terms of state of charge (SoC) compared to the A-RB. In fact, in the A-ECMS the SoC is always within the admissible range with considerable margin from the upper and lower limits for tested cycles, while in the A-RB a deep discharge of the battery is allowed. This behavior leads to a better fuel consumption of the A-RB compared to the A-ECMS, both in the WLTC and in the FTP-75 cycle.
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