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Journal articles on the topic 'Online Model Adaptive'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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Abouheaf, Mohammed, Wail Gueaieb, and Davide Spinello. "Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems." Robotics 8, no. 4 (September 22, 2019): 82. http://dx.doi.org/10.3390/robotics8040082.

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The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment.

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Yi, Sheng-Lun, Xue-Bo Jin, Ting-Li Su, Zhen-Yun Tang, Fa-Fa Wang, Na Xiang, and Jian-Lei Kong. "Online Denoising Based on the Second-Order Adaptive Statistics Model." Sensors 17, no. 7 (July 20, 2017): 1668. http://dx.doi.org/10.3390/s17071668.

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Wang, Peng, and Hong Qiao. "Online Appearance Model Learning and Generation for Adaptive Visual Tracking." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 2 (February 2011): 156–69. http://dx.doi.org/10.1109/tcsvt.2011.2105598.

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Sung Wook Baik and P. W. Pachowicz. "Online model modification for adaptive texture recognition in image sequences." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 32, no. 6 (November 2002): 625–39. http://dx.doi.org/10.1109/tsmca.2002.807039.

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Xiaorui Wang, Kai Ma, and Yefu Wang. "Adaptive Power Control with Online Model Estimation for Chip Multiprocessors." IEEE Transactions on Parallel and Distributed Systems 22, no. 10 (October 2011): 1681–96. http://dx.doi.org/10.1109/tpds.2011.39.

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Kang, Jeongho, and Kwangsue Chung. "HTTP Adaptive Streaming Framework with Online Reinforcement Learning." Applied Sciences 12, no. 15 (July 24, 2022): 7423. http://dx.doi.org/10.3390/app12157423.

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Dynamic adaptive streaming over HTTP (DASH) is an effective method for improving video streaming’s quality of experience (QoE). However, the majority of existing schemes rely on heuristic algorithms, and the learning-based schemes that have recently emerged also have a problem in that their performance deteriorates in a specific environment. In this study, we propose an adaptive streaming scheme that applies online reinforcement learning. When QoE degradation is confirmed, the proposed scheme adapts to changes in the client’s environment by upgrading the ABR model while performing video streaming. In order to adapt the adaptive bitrate (ABR) model to a changing network environment while performing video streaming, the neural network model is trained with a state-of-the-art reinforcement learning algorithm. The proposed scheme’s performance was evaluated using simulation-based experiments under various network conditions. The experimental results confirmed that the proposed scheme performed better than the existing schemes.

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Zhuang, Yan, Qi Liu, Zhenya Huang, Zhi Li, Shuanghong Shen, and Haiping Ma. "Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4734–42. http://dx.doi.org/10.1609/aaai.v36i4.20399.

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Computerized Adaptive Testing (CAT) refers to an efficient and personalized test mode in online education, aiming to accurately measure student proficiency level on the required subject/domain. The key component of CAT is the "adaptive" question selection algorithm, which automatically selects the best suited question for student based on his/her current estimated proficiency, reducing test length. Existing algorithms rely on some manually designed and pre-fixed informativeness/uncertainty metrics of question for selections, which is labor-intensive and not sufficient for capturing complex relations between students and questions. In this paper, we propose a fully adaptive framework named Neural Computerized Adaptive Testing (NCAT), which formally redefines CAT as a reinforcement learning problem and directly learns selection algorithm from real-world data. Specifically, a bilevel optimization is defined and simplified under CAT's application scenarios to make the algorithm learnable. Furthermore, to address the CAT task effectively, we tackle it as an equivalent reinforcement learning problem and propose an attentive neural policy to model complex non-linear interactions. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of NCAT compared with several state-of-the-art methods.

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Shao, Weijia, Lukas Friedemann Radke, Fikret Sivrikaya, and Sahin Albayrak. "Adaptive Online Learning for the Autoregressive Integrated Moving Average Models." Mathematics 9, no. 13 (June 29, 2021): 1523. http://dx.doi.org/10.3390/math9131523.

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This paper addresses the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and unsuitable for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models without hyperparameters. The regret analysis and experiments on both synthetic and real-world datasets show that the performance of the proposed algorithms can be guaranteed in both theory and practice.

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Yuan, Jing. "Noninvasive Model Independent Noise Control with Adaptive Feedback Cancellation." Advances in Acoustics and Vibration 2008 (March 17, 2008): 1–7. http://dx.doi.org/10.1155/2008/863603.

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An active noise control (ANC) system is model dependent/independent if its controller transfer function is dependent/independent on initial estimates of path models in a sound field. Since parameters of path models in a sound field will change when boundary conditions of the sound field change, model-independent ANC systems (MIANC) are able to tolerate variations of boundary conditions in sound fields and more reliable than model-dependent counterparts. A possible way to implement MIANC systems is online path modeling. Many such systems require invasive probing signals (persistent excitations) to obtain accurate estimates of path models. In this study, a noninvasive MIANC system is proposed. It uses online path estimates to cancel feedback, recover reference signal, and optimize a stable controller in the minimum H2 norm sense, without any forms of persistent excitations. Theoretical analysis and experimental results are presented to demonstrate the stable control performance of the proposed system.

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20

Basak, Jayanta. "Online Adaptive Decision Trees: Pattern Classification and Function Approximation." Neural Computation 18, no. 9 (September 2006): 2062–101. http://dx.doi.org/10.1162/neco.2006.18.9.2062.

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Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

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Sun, Yan Xia, and Zeng Hui Wang. "Adaptive Optimal Digital PID Controller." Applied Mechanics and Materials 789-790 (September 2015): 1021–26. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.1021.

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It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

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Singer, Yoram. "Adaptive Mixtures of Probabilistic Transducers." Neural Computation 9, no. 8 (November 1, 1997): 1711–33. http://dx.doi.org/10.1162/neco.1997.9.8.1711.

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We describe and analyze a mixture model for supervised learning of probabilistic transducers. We devise an online learning algorithm that efficiently infers the structure and estimates the parameters of each probabilistic transducer in the mixture. Theoretical analysis and comparative simulations indicate that the learning algorithm tracks the best transducer from an arbitrarily large (possibly infinite) pool of models. We also present an application of the model for inducing a noun phrase recognizer.

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Museba, Tinofirei, Fulufhelo Nelwamondo, and Khmaies Ouahada. "An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments." Computational Intelligence and Neuroscience 2021 (March 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6669706.

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In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES-AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real-world datasets with well-known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES-AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.

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Liu, Ya Lei, and Xiao Hui Gu. "Adaptive Interacting Multiple Model Unscented Particle Filter for Dynamic Acoustic Array." Applied Mechanics and Materials 300-301 (February 2013): 407–13. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.407.

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Abstract. In order to improve the tracking accuracy of 3D dynamic acoustic array to 2D maneuvering target in colored noise environment, the adaptive interacting multiple model unscented particle filter algorithm based on measured residual is proposed. The 3D motion acoustic 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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Kaptein, Maurits, Richard McFarland, and Petri Parvinen. "Automated adaptive selling." European Journal of Marketing 52, no. 5/6 (May 14, 2018): 1037–59. http://dx.doi.org/10.1108/ejm-08-2016-0485.

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Purpose This paper aims to develop and test a method of automating, for online retailers, the practice of adaptive selling, which is typically used by salespeople in face-to-face interactions. This method customizes persuasive messages for individual customers as they navigate a retailer’s website. Design/methodology/approach This paper demonstrates a method for the online implementation of automated adaptive selling using sales influence tactics. Automated adaptive selling is compared to nonadaptive selling in three e-commerce field studies. Findings The results reveal that adaptive selling is more effective than nonadaptive selling. The click-through rates increased significantly when adaptive selling was used. Research limitations/implications This paper highlights the effectiveness of existing theories concerning adaptive human-to-human selling and their utility to online selling. The authors demonstrate the added value of adaptive selling in e-commerce, thereby opening up a novel area of research into adaptive selling online. While the paper focuses on the adjustment of sales influence tactics, other factors could be investigated for adjustment in future research (e.g. prices). Practical implications The methods, described in detail, are readily available for implementation by online retailers. The implementations are timely and increasingly valuable as e-commerce expands into interpersonal channels (e.g. instant messengers and social media). Originality/value To the authors’ knowledge, this paper is the first to formally implement automated adaptive selling as described in the ISTEA model in an e-commerce setting.

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Kaltenbacher, Barbara, and Tram Thi Ngoc Nguyen. "A model reference adaptive system approach for nonlinear online parameter identification." Inverse Problems 37, no. 5 (April 16, 2021): 055006. http://dx.doi.org/10.1088/1361-6420/abf164.

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Xiaosuo Luo, Minghua Jiang, and Xuechang Chen. "Online Subspace-based Constrained Adaptive Predictive Control with State-space Model." Journal of Convergence Information Technology 8, no. 2 (January 31, 2013): 642–51. http://dx.doi.org/10.4156/jcit.vol8.issue2.77.

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Peherstorfer, Benjamin, and Karen Willcox. "Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates." SIAM Journal on Scientific Computing 37, no. 4 (January 2015): A2123—A2150. http://dx.doi.org/10.1137/140989169.

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Zhou, Ye, Erik-Jan van Kampen, and Qi Ping Chu. "Incremental model based online dual heuristic programming for nonlinear adaptive control." Control Engineering Practice 73 (April 2018): 13–25. http://dx.doi.org/10.1016/j.conengprac.2017.12.011.

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Zimmermann, Ralf, Benjamin Peherstorfer, and Karen Willcox. "Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction." SIAM Journal on Matrix Analysis and Applications 39, no. 1 (January 2018): 234–61. http://dx.doi.org/10.1137/17m1123286.

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Karami, Amir Hossein, Maryam Hasanzadeh, and Shohreh Kasaei. "Online adaptive motion model-based target tracking using local search algorithm." Engineering Applications of Artificial Intelligence 37 (January 2015): 307–18. http://dx.doi.org/10.1016/j.engappai.2014.09.018.

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Liu, Changxin, Jinliang Ding, Anthony J. Toprac, and Tianyou Chai. "Data-based adaptive online prediction model for plant-wide production indices." Knowledge and Information Systems 41, no. 2 (May 22, 2014): 401–21. http://dx.doi.org/10.1007/s10115-014-0757-8.

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Jin, Ying. "A Self-Adaptive Recommendation Method for Online Ideological and Political Teaching Resources Based on Deep Reinforcement Learning." Mobile Information Systems 2022 (August 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/1702657.

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The online ideological as well as the political teaching resource management system structure is established in the view of information management in colleges and universities. Furthermore, the online ideological as well as the political teaching information level is improved by combining the optimized design of resource recommendation model. In this paper, an online ideological as well as political teaching resource adaptive recommendation system and algorithm, which is designed on deep reinforcement learning, is suggested. The cost relationship model between online ideological as well as the political teaching resources and learning profitability is constructed. Similarly, the multidimensional constraint index parameter analysis method is adopted, and the adaptive matching model of online ideological as well as the political teaching resources is established. According to online ideological as well as the political teaching norms, combined with the analysis of high-quality educational resources of audience groups, the dynamic evaluation of online ideological as well as the political teaching resources and the adaptive matching model of interest preferences are established. Finally, the deep reinforcement learning method is adopted. By analyzing the characteristics of the resource structure model of online ideological as well as the political teaching resources, through benefit evaluation, resource supply and demand balance management analysis and balanced game control, the online ideological as well as the political teaching resources management system can be improved and self-adaptive recommended. The simulation outcomes indicate that this approach has noble adaptability and high correctness in recommending online ideological as well as the political teaching resources.

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Liu, Hao, Huixian Zhang, He Zhang, and Gaoqi Chen. "Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012052. http://dx.doi.org/10.1088/1742-6596/2258/1/012052.

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Abstract For the speed sensorless control of permanent magnet synchronous motor (PMSM), a design method based on model reference adaptive observer and single neuron PID speed loop was proposed. In this method, the model reference adaptive observer is used to identify the speed online, the Popov hyperstability is used to determine the reference adaptive law of the speed observer, the neural network module is used to control the q-axis current, and the LMS algorithm is selected to modify the weights of the neural network speed loop online. The simulation results show that the model reference adaptive speed observer control of PMSM based on single neuron PID is effective and robust.

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Chen, Mingang, Wenjun Cai, and Lizhuang Ma. "Cloud Computing Platform for an Online Model Library System." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/369056.

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The rapid developing of digital content industry calls for online model libraries. For the efficiency, user experience, and reliability merits of the model library, this paper designs a Web 3D model library system based on a cloud computing platform. Taking into account complex models, which cause difficulties in real-time 3D interaction, we adopt the model simplification and size adaptive adjustment methods to make the system with more efficient interaction. Meanwhile, a cloud-based architecture is developed to ensure the reliability and scalability of the system. The 3D model library system is intended to be accessible by online users with good interactive experiences. The feasibility of the solution has been tested by experiments.

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Huang, Quanzhen, Suxia Chen, Mingming Huang, and Zhuangzhi Guo. "Adaptive Active Noise Suppression Using Multiple Model Switching Strategy." Shock and Vibration 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/7289076.

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Active noise suppression for applications where the system response varies with time is a difficult problem. The computation burden for the existing control algorithms with online identification is heavy and easy to cause control system instability. A new active noise control algorithm is proposed in this paper by employing multiple model switching strategy for secondary path varying. The computation is significantly reduced. Firstly, a noise control system modeling method is proposed for duct-like applications. Then a multiple model adaptive control algorithm is proposed with a new multiple model switching strategy based on filter-u least mean square (FULMS) algorithm. Finally, the proposed algorithm was implemented on Texas Instruments digital signal processor (DSP) TMS320F28335 and real time experiments were done to test the proposed algorithm and FULMS algorithm with online identification. Experimental verification tests show that the proposed algorithm is effective with good noise suppression performance.

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He, Zhaoliang, Hongshan Li, Zhi Wang, Shutao Xia, and Wenwu Zhu. "Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–23. http://dx.doi.org/10.1145/3447878.

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With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the de facto compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an explore-exploit mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.

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Xiao, Jun, Lamei Wang, Jisheng Zhao, and Aizhen Fu. "Research on Adaptive Learning Prediction Based on XAPI." International Journal of Information and Education Technology 10, no. 9 (2020): 679–84. http://dx.doi.org/10.18178/ijiet.2020.10.9.1443.

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In the field of online learning, there is a problem of high student turnover rate. How to accurately identify learners and provide targeted teaching support services is an urgent problem for education researchers. In this paper, 1306 online learners majoring in finance from Shanghai Open University were selected as the subjects, and two kinds of data sets are adopted, which are learning data of online learning platform and learning behavior data of students based on xAPI, to analyze the relationship between learners' various online learning behaviors and learning achievements, and to determine the characteristics related to learning state of learners, describe the personalized learning state portrait, and select a variety of machine learning algorithms to build prediction model based on two data sets, to explore which data is more effective for building prediction models to identify potential risk learners. It is found that data mining analysis based on xAPI data has higher prediction accuracy than traditional online learning data.

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Lennox, J., and C. Rosen. "Adaptive multiscale principal components analysis for online monitoring of wastewater treatment." Water Science and Technology 45, no. 4-5 (February 1, 2002): 227–35. http://dx.doi.org/10.2166/wst.2002.0593.

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Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

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Komleva, N. V., and D. A. Vilyavin. "Digital Platform for Creating Personalized Adaptive Online Courses." Open Education 24, no. 2 (April 22, 2020): 65–72. http://dx.doi.org/10.21686/1818-4243-2020-2-65-72.

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The purpose of the research is to develop a digital platform for creating personalized adaptive online courses that can integrate into the university’s e-learning environment. The Digital Tutor platform is designed to provide the online learning process with tools that allow for the adaptation of the content of the electronic course in accordance with the individual level of student competency through adaptive testing tools in order to achieve the level of student competency established by educational and professional standards.Materials and research methods. The research methodological base consists of methods and technologies of system analysis and knowledge management. Conclusions and provisions of the work are based on the analysis of domestic and foreign literature on the use of digital technologies in education. In preparing the article, materials obtained by the authors during the scientific and practical development of the prototype of the Digital Tutor platform were used to create personalized adaptive online courses at Plekhanov Russian University of Economics.Results. The digital platform for hosting the repository of educational objects and the online courses themselves is available on the University’s information resources with the possibility of integration into the University’s electronic educational environment. The implementation of this project will allow: students and the audience to use educational content prepared on the basis of relevant educational material, as well as to participate in its creation and discussion; to develop more dynamic and high-quality training courses that contribute to the formation of the required competencies among students and the audience; significantly reduce the burden on lecturers when working with remote students, free up more time for updating the training material, the formation of practical and design tasks; implement the concept of personalization of training - the creation of educational material aimed at a particular student; provide support for the creation and updating of their own MOOC; transform the system of continuing education to the requirements and needs of the business; respond ahead of time to the needs of society for qualified personnel for the digital economy.Conclusion. A new model for the implementation of online education has been proposed and tested, which consists in the automatic construction of online courses from the educational objects of the repository in accordance with the monitoring of its activities and a personal trajectory to achieve the required learning outcomes. The concept of transformation of the model of online education is based on the creation of a modern educational based on advanced digital, intelligent technologies. Compared with existing analogues, the project has competitive advantages in the implementation of a new business model of education, based on the availability of a mechanism for automatic updating of educational content and preparing courses on the basis of a repository of educational objects that form the necessary competencies in accordance with the Federal State Educational Standard and approved professional standards.

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Natorina, Alona. "THE ADAPTIVE MANAGEMENT SYSTEM OF MARKETING COMMODITY POLICY*." Baltic Journal of Economic Studies 5, no. 1 (March 22, 2019): 131. http://dx.doi.org/10.30525/2256-0742/2019-5-1-131-136.

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The purpose of the article is to substantiate theoretical and methodological fundamentals of the formation of adaptive system for managing the marketing commodity policy through the example of online retailers, as well as to analyse results of approbation of conceptual model of adaptive management system of marketing commodity policy in practical activities of Ukrainian online retailers in various market segments. Methodology. Within the research, activities of Ukrainian online retailers were considered and analysed depending on their sphere and specialisation. Methodological basis of the study is comprised of the system of general scientific and special methods, namely: dialectical method of scientific cognition; methods of system analysis; methods of causeand- effect analysis; methods of comparative analysis; multidimensional statistical methods. Based on the results of the research conducted, the author has developed a conceptual model of adaptive system of management of marketing commodity policy of online retailers, the main goal of which is to form adaptive management system of marketing commodity policy of online retailers as a central reference point in the identification of marketing commodity strategy needed to be implemented, which mostly influences long-term sustainable functioning of online retailers in the market by means of the fullest satisfaction of typical demands, needs, and preferences of the target audience and occasional online buyers along with irrational online orders. List of components of the marketing environment, which have quantitative and qualitative character and different degree of impact on the activities of online retailers depending on their belonging to a certain market segment, is determined. Mechanism of adaptive management of marketing commodity policy of online retailers is proposed that is oriented to revealing the integrity of the process in the context of permanent changes in the market situation; is directed to providing a high-quality level of management of online activity of retailers. Practical importance. Based on the results of the approbation of conceptual model of adaptive management system of marketing commodity policy of Ukrainian online retailers in different segments, it is determined that its implementation in online activity of retailers contributes to their effective functioning and sustainable long-term development, in particular, by means of adjusting reference points of management of marketing commodity policy, and results into improving the process of making rational strategic decisions and substantiating tactical measures of implementation. Practical application of the mechanism of adaptive management of marketing commodity policy of online retailers promotes the implementation of a sound variant of achievement of their strategic goals. Value/originality. Implementation of the developed adaptive management system of marketing commodity policy may ensure effective performance and sustainable development of online retailers in the future, in particular, by means of relevant changes in the management of marketing commodity policy.

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Xi, Junfu, Yehua Chen, and Gang Wang. "Design of a Personalized Massive Open Online Course Platform." International Journal of Emerging Technologies in Learning (iJET) 13, no. 04 (March 30, 2018): 58. http://dx.doi.org/10.3991/ijet.v13i04.8470.

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Focusing on the massive open online course (MOOC) platform, the purpose of this study is to realize personalized adaptive learning according to the needs and abilities of each learner. To this end, the author created a personalized adaptive learning behaviour analysis model, and designed a personalized MOOC platform based on the model. Through the analysis of learning behaviours on the MOOC platform, the model digs deep into the pattern of learning behaviours, and lays the basis for personalized intervention in the learning process. The comparison ex-periments show that our prediction method is more accurate than the other predic-tion algorithms. This research sheds new light on the design of learner-specific MOOC platform.

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Thaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (June 11, 2022): 222–36. http://dx.doi.org/10.37936/ecti-cit.2022162.248019.

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Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.

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Thaipisutikul, Tipajin. "An Adaptive Temporal-Concept Drift Model for Sequential Recommendation." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 2 (June 7, 2022): 221–35. http://dx.doi.org/10.37936/ecticit.2022162.248019.

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Recently, owing to the great advances in Web 2.0 and mobile devices, various online commercial services have emerged. Recommendation systems play an important role in dealing with abundant product information from massive numbers of online e-commerce transactions. Providing an accurate recommendation at the correct time to customers can contribute to a surge in business success. In this paper, an adaptive temporal-concept drift learning-based recommendation system, ATCRec, is developed for precisely tackling the sequential recommendation problem. We embed sequences of items into the latent spaces and learn both general preferences and sequential patterns concurrently via a recurrent neural network. Specifically, ATCRec captures dynamic changes in the temporal and concept drift contexts by modifying the gate units in a traditional recurrent neural network. The proposed model provides a unified and flexible network structure to learn and reveal the opaque variation of user preferences over time. We evaluate the robustness and performance of ATCRec on two real-world datasets, and the experimental results demonstrate that ATCRec consistently outperforms existing sequential recommendation approaches on various metrics. This indicates that integrating users' temporal information and concept drift variation through time are indispensable in improving the performance of recommendation systems.

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Han, Qiaoling, Jianbo Su, and Yue Zhao. "More Adaptive and Updatable: An Online Sparse Learning Method for Face Recognition." Journal of Electrical and Computer Engineering 2019 (October 13, 2019): 1–7. http://dx.doi.org/10.1155/2019/8370835.

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In the actual face recognition applications, the sample sets are updated constantly. However, most of the face recognition models with learning strategy do not consider this fact and using a fixed training set to learn the face recognition models for once. Besides that, the testing samples are discarded after the testing process is completed. Namely, the training and testing processes are separated and the later does not give a feedback to the former for better recognition results. To attenuate these problems, this paper proposed an online sparse learning method for face recognition. It can update the salience evaluation vector in real time to construct a dynamical facial feature description model. Also, a strategy for updating the gallery set is proposed in this proposed method. Both the dynamical facial feature description model and the gallery set are employed to recognize faces. Experimental results show that the proposed method improves the face recognition accuracy, comparing with the classical learning models and other state-of-the-art face recognition methods.

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Zou, Wei, Dieter Froning, Yan Shi, and Werner Lehnert. "An online adaptive model for the nonlinear dynamics of fuel cell voltage." Applied Energy 288 (April 2021): 116561. http://dx.doi.org/10.1016/j.apenergy.2021.116561.

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Efendiev, Yalchin, Eduardo Gildin, and Yanfang Yang. "Online Adaptive Local-Global Model Reduction for Flows in Heterogeneous Porous Media." Computation 4, no. 2 (June 7, 2016): 22. http://dx.doi.org/10.3390/computation4020022.

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Han, Xu, Zhengru Ren, Bernt Johan Leira, and Svein Sævik. "Adaptive identification of lowpass filter cutoff frequency for online vessel model tuning." Ocean Engineering 236 (September 2021): 109483. http://dx.doi.org/10.1016/j.oceaneng.2021.109483.

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Dai, Jinling, Aiqiang Xu, Xing Liu, Chao Yu, and Yangyong Wu. "Online Sequential Model for Multivariate Time Series Prediction With Adaptive Forgetting Factor." IEEE Access 8 (2020): 175958–71. http://dx.doi.org/10.1109/access.2020.3026009.

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Park, C. W., and Y. W. Cho. "T–S Model Based Indirect Adaptive Fuzzy Control Using Online Parameter Estimation." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (December 2004): 2293–302. http://dx.doi.org/10.1109/tsmcb.2004.835079.

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