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Journal articles on the topic 'Adaptive environment'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Specht, Marcus, and Reinhard Oppermann. "ACE - adaptive courseware environment." New Review of Hypermedia and Multimedia 4, no. 1 (January 1998): 141–61. http://dx.doi.org/10.1080/13614569808914699.

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2

Ewais, Ahmed, and Olga De Troyer. "Usability Evaluation of an Adaptive 3D Virtual Learning Environment." International Journal of Virtual and Personal Learning Environments 4, no. 1 (January 2013): 16–31. http://dx.doi.org/10.4018/jvple.2013010102.

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Using 3D virtual environments for educational purposes is becoming attractive because of their rich presentation and interaction capabilities. Furthermore, dynamically adapting the 3D virtual environment to the personal preferences, prior knowledge, skills and competence, learning goals, and the personal or (social) context in which the learning takes place becomes interesting, as there is a bulk of research demonstrating that individualized instruction is superior to the uniform approach of more traditional and one-size-fits-all teaching approaches. However, although such adaptive 3D Virtual Learning Environments (3D VLE) seem to be promising, this needs to be evaluated in practice. Usability of adaptive 3D VLE could be a problem since the user interface could become relatively complex. In this paper, the authors describe an experiment performed to validate the issues of usability and acceptability of an adaptive 3D VLE. This pilot evaluation reveals some important recommendations and improvements.

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Ewais, Ahmed, and Olga De Troyer. "Authoring Adaptive 3D Virtual Learning Environments." International Journal of Virtual and Personal Learning Environments 5, no. 1 (January 2014): 1–19. http://dx.doi.org/10.4018/ijvple.2014010101.

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The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the activities that he performs in the environment. In this paper, the authors discuss adaptive 3D virtual leaning environments and explain how a course author can specify such an environment (i.e., authoring). The approach and tool that the authors present allow authors to create adaptive 3D virtual learning environments without the need to be an expert in 3D or using programming or scripting languages. The authors also conducted an evaluation to validate the approach and the usability and acceptability of the authoring tool. Based on the results, recommendations for authoring adaptive 3D virtual learning environments have been formulated.

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Santoianni, Flavia, and Alessandro Ciasullo. "Adaptive Design for Educational Hypermedia Environments and Bio-Educational Adaptive Design for 3D Virtual Learning Environments." Research on Education and Media 10, no. 1 (June 1, 2018): 30–41. http://dx.doi.org/10.1515/rem-2018-0005.

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Abstract Adaptive learning environments design has been originally influenced by the adaptive learning environments model, which has been recently re-shaped by the bio-educational adaptive approach. Adaptive and bio-educational models share the common main idea that education should be adaptive. Since the 90’s the adoption of an adaptive educational point of view have been at the base of adaptive educational hypermedia systems, which design joins interest towards learners’ individual differences with adaptive learning environments research. Educational hypermedia systems have been overcome by the technology of 3D Virtual Learning Environments. Some emerging questions are related to the design criteria of adaptive learning environments. Which lessons learned from adaptive hypermedia systems design could be now applied to VLEs’ design? How a virtual learning environment should be designed to be adaptive? This research tries to answer to these questions by describing Federico 3DSU, an educational University 3D Virtual Learning Environment which has been designed with adaptive criteria, according to bio-educational model

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5

Yang, Eunjung, Iltak Han, Junho Song, Heeyoung Lee, and Dongjin Yeom. "Adaptive Sidelobe Blanker for Interference Environment." Journal of Korean Institute of Electromagnetic Engineering and Science 26, no. 3 (March 31, 2015): 317–25. http://dx.doi.org/10.5515/kjkiees.2015.26.3.317.

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6

Itao, Tomoko, and Masato Matsuo. "DANSE: Dynamically adaptive networking service environment." Electronics and Communications in Japan (Part II: Electronics) 84, no. 11 (November 2001): 30–40. http://dx.doi.org/10.1002/ecjb.1070.

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7

Rainey, Paul B., and Michael Travisano. "Adaptive radiation in a heterogeneous environment." Nature 394, no. 6688 (July 1998): 69–72. http://dx.doi.org/10.1038/27900.

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8

Glinert, Ephraim P., R. Lindsay Todd, and G. Bowden Wise. "The adaptive multi-interface multimodal environment." ACM Computing Surveys 28, no. 4es (December 1996): 134. http://dx.doi.org/10.1145/242224.242394.

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9

Mozer, M. C. "An Intelligent Environment Must Be Adaptive." IEEE Intelligent Systems and their Applications 14, no. 2 (March 1999): 11–13. http://dx.doi.org/10.1109/mis.1999.757623.

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10

Jhwueng, D. C., and V. Maroulas. "Adaptive trait evolution in random environment." Journal of Applied Statistics 43, no. 12 (February 2, 2016): 2310–24. http://dx.doi.org/10.1080/02664763.2016.1140729.

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11

Sum, John, Chi-sing Leung, Gilbert H. Young, Lai-wan Chan, and Wing-kay Kan. "An Adaptive Bayesian Pruning for Neural Networks in a Non-Stationary Environment." Neural Computation 11, no. 4 (May 1, 1999): 965–76. http://dx.doi.org/10.1162/089976699300016539.

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Pruning a neural network to a reasonable smaller size, and if possible to give a better generalization, has long been investigated. Conventionally the common technique of pruning is based on considering error sensitivity measure, and the nature of the problem being solved is usually stationary. In this article, we present an adaptive pruning algorithm for use in a nonstationary environment. The idea relies on the use of the extended Kalman filter (EKF) training method. Since EKF is a recursive Bayesian algorithm, we define a weight-importance measure in term of the sensitivity of a posteriori probability. Making use of this new measure and the adaptive nature of EKF, we devise an adaptive pruning algorithm called adaptive Bayesian pruning. Simulation results indicate that in a noisy nonstationary environment, the proposed pruning algorithm is able to remove network redundancy adaptively and yet preserve the same generalization ability.

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12

Vohra, Veena. "Organizational environments and adaptive response mechanisms in India." Journal of Indian Business Research 7, no. 1 (March 16, 2015): 21–44. http://dx.doi.org/10.1108/jibr-01-2014-0001.

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Purpose – The purpose of this paper is to investigate the nature of the organizational environments of Indian business organizations and to identify the adaptive response mechanisms that organizations use to cope with their environments. This paper also examines in detail the causal texture of the organizational environments and attempts to build a conceptual model mapping adaptive responses of organizations to different types of organizational environments. Design/methodology/approach – A constructivist stance was adopted in this exploratory study to capture the perceptions of the organizational leaders through the multiple case study design to capture the features of the organizational environments and their causal texture. The multiple case study design used an embedded mixed-methods approach to collect data. Within-case analysis and cross-case analysis were conducted to draw out prominent themes across cases ordered for particular organizational environment types. The study was conducted by following construct validity, internal reliability and external validity guidelines. Findings – The study highlights and describes in detail the characteristics of the different organizational environment types in India. It is revealed that a majority of Indian organizations exist in turbulent environments. There are differences in the adaptive response mechanisms of organizations in the environment types studied. The study specially focuses on the strategies adopted by Indian organizations to adapt to turbulent environments. Practical implications – This study maps the causal texture of organizational environments in India and maps the organizational adaptive responses to the environment for greater effectiveness. This study offers various strategies to cope with turbulent organizational environments and adds to the research focus on causal texture and adaptive capacities of organizations across different types of environments. Originality/value – This study contributes to an ignored subject area of organizational environments. Managing organizations in uncertain and turbulent environments is complex, and this study provides an understanding about the various types of adaptive mechanism that are used to cope with environmental turbulence. This study also attempts to answer several questions that previous research works have raised about strategies that organizations use when they fail to cope with environmental turbulence.

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13

Asama, Hajime, and Jun Ota. "Special Issue on Mobiligence: Emergence of Adaptive Motor Function Through Interaction Among the Body, Brain and Environment." Journal of Robotics and Mechatronics 19, no. 4 (August 20, 2007): 363. http://dx.doi.org/10.20965/jrm.2007.p0363.

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Animals behave adaptively in diverse environments. Adaptive behavior, which is one of intelligent sensory-motor functions, is disturbed in patients with neurological disorders. Mechanisms for the generation of intelligent adaptive behaviors are not well understood. Such an adaptive function is considered to emerge from the interaction of the body, brain, and environment, which requires that a subject acts or moves. Intelligence for generating adaptive motor functions is thus called mobiligence. This special issue features papers dealing with mobiligence. The 18 papers were selected after a thorough peer review. The scope of these papers extends from analytical studies close to biology to synthetic studies close to engineering. Subjects are diverse – insects, monkeys, human beings, robots, networks. All papers play a part in mobiligence studies. We thank the Editorial Board of Journal of Robotics and Mechatronics for giving us the opportunity for publishing this special issue. We also thank the authors for their perseverance and expertise, and deeply appreciate the timely and helpful comments of the reviewers.

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14

Wang, Bin, Jin Kuan Wang, and Xin Song. "Mutual Information Model of Adaptive Waveform Design." Key Engineering Materials 460-461 (January 2011): 207–12. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.207.

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Traditional radar systems are lack of adaptivity to the environment. Modern radar systems should transmit different waveforms according to different environment. In this paper, mutual information model of adaptive waveform design is proposed. With this model, different waveforms can be designed adaptively under different radar working conditions. Simulation results demonstrate the validity of our model. Finally, the whole paper is summarized.

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15

Lopez, Tabbetha, Katherine R. Arlinghaus, and Craig A. Johnston. "Developing Adaptive Learning Environments to Support Long-Term Health Promotion." American Journal of Lifestyle Medicine 13, no. 1 (November 25, 2018): 30–32. http://dx.doi.org/10.1177/1559827618807389.

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Health promotion strategies typically include changing the environment, providing supervision to decrease the likelihood an unhealthy behavior will occur, and increasing skills to make decisions supporting health in environments in which such choices are challenging to make. The first two strategies are important in improving the environment to promote healthy decision making. However, the creation of restrictive environments has repeatedly shown to not support disease prevention in the long term. Restrictive environments do not support the development of skills to make healthy choices when restrictions are not in place. This is particularly true for children who are learning to navigate their environment and make health decisions. The creation of adaptive learning environments should be prioritized to help individuals develop the skills needed for long-term health promotion.

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16

KAKIZOE, Yuki, Hisakazu NAKAMURA, and Hirokazu NISHITANI. "Remote Hybrid Controller with Adaptive Environment Observer." SICE Journal of Control, Measurement, and System Integration 1, no. 6 (2008): 443–51. http://dx.doi.org/10.9746/jcmsi.1.443.

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17

Hadany, Lilach. "Adaptive peak shifts in a heterogenous environment." Theoretical Population Biology 63, no. 1 (February 2003): 41–51. http://dx.doi.org/10.1016/s0040-5809(02)00011-4.

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18

Tsujimoto, Keita, Kohei Okamoto, Ryuichi Miyamoto, Maki Maeda, Kiyokazu Agata, and Naoyuki Fuse. "Adaptive behaviors of Drosophila in dark environment." Neuroscience Research 71 (September 2011): e343-e344. http://dx.doi.org/10.1016/j.neures.2011.07.1507.

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19

Nakamori, Y., and K. Suzuki. "Interactive Modeling Environment for Adaptive Process Control." IFAC Proceedings Volumes 26, no. 2 (July 1993): 149–52. http://dx.doi.org/10.1016/s1474-6670(17)48243-7.

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20

A., Mohammed, and Syed Hamid. "Cloud Environment – Task Schedule and Adaptive Cost." International Journal of Computer Applications 178, no. 3 (November 15, 2017): 29–34. http://dx.doi.org/10.5120/ijca2017915787.

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21

Yu, Jinqiao, Y. V. Ramana Reddy, Shufen Liu, Zhilin Yao, Vijayanand Bharadwaj, Sumitra Reddy, and Srinivas Kankanahalli. "Adaptive collaboration in an ever-changing environment." Multiagent and Grid Systems 4, no. 2 (July 15, 2008): 125–40. http://dx.doi.org/10.3233/mgs-2008-4201.

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22

Costabile, M. F., F. Esposito, G. Semeraro, and N. Fanizzi. "An adaptive visual environment for digital libraries." International Journal on Digital Libraries 2, no. 2-3 (September 1999): 124–43. http://dx.doi.org/10.1007/s007990050042.

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23

Zug, Sebastian, André Dietrich, Christoph Steup, and Jörg Kaiser. "Adaptive environment perception in cyber-physical systems." ACM SIGBED Review 12, no. 3 (August 17, 2015): 14–18. http://dx.doi.org/10.1145/2815482.2815484.

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24

Wang, Wei, Lihong Xu, and Haigen Hu. "Neuron adaptive PID control for greenhouse environment." Journal of Industrial and Production Engineering 32, no. 5 (May 29, 2015): 291–97. http://dx.doi.org/10.1080/21681015.2015.1048752.

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25

Kang, Changgu, and Sung-Hee Lee. "Environment-Adaptive Contact Poses for Virtual Characters." Computer Graphics Forum 33, no. 7 (October 2014): 1–10. http://dx.doi.org/10.1111/cgf.12468.

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26

Sprague, William W., Emily A. Cooper, Ivana Tošić, and Martin S. Banks. "Stereopsis is adaptive for the natural environment." Science Advances 1, no. 4 (May 2015): e1400254. http://dx.doi.org/10.1126/sciadv.1400254.

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Humans and many animals have forward-facing eyes providing different views of the environment. Precise depth estimates can be derived from the resulting binocular disparities, but determining which parts of the two retinal images correspond to one another is computationally challenging. To aid the computation, the visual system focuses the search on a small range of disparities. We asked whether the disparities encountered in the natural environment match that range. We did this by simultaneously measuring binocular eye position and three-dimensional scene geometry during natural tasks. The natural distribution of disparities is indeed matched to the smaller range of correspondence search. Furthermore, the distribution explains the perception of some ambiguous stereograms. Finally, disparity preferences of macaque cortical neurons are consistent with the natural distribution.

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27

Maharjan, R. "Clonal Adaptive Radiation in a Constant Environment." Science 313, no. 5786 (July 28, 2006): 514–17. http://dx.doi.org/10.1126/science.1129865.

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28

Schatz, Sae, Robert Wray, Jeremiah Folsom-Kovarik, and Denise Nicholson. "Adaptive Perceptual Training in a Virtual Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 2472–76. http://dx.doi.org/10.1177/1071181312561503.

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29

Jang, Seok-Woo. "Environment-Adaptive Image Segmentation Using Color Invariants." Journal of the Korea Society of Computer and Information 15, no. 10 (October 31, 2010): 71–78. http://dx.doi.org/10.9708/jksci.2010.15.10.071.

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30

Catalbas, Cem. "Environment Adaptive Lighting Systems for Smart Homes." Advances in Science and Technology Research Journal 11, no. 3 (September 3, 2017): 172–78. http://dx.doi.org/10.12913/22998624/76449.

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31

Lamarche, Luc, Christian Giguère, Wail Gueaieb, Tyseer Aboulnasr, and Hisham Othman. "Adaptive environment classification system for hearing aids." Journal of the Acoustical Society of America 127, no. 5 (May 2010): 3124–35. http://dx.doi.org/10.1121/1.3365301.

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32

Jena, Saumya Ranjan, Kumudini Meher, and Arjun Kumar Paul. "Approximation of analytic functions in adaptive environment." Beni-Suef University Journal of Basic and Applied Sciences 5, no. 4 (December 2016): 306–9. http://dx.doi.org/10.1016/j.bjbas.2016.10.001.

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33

Steel, J. A. "Modelling adaptive phytoplankton in a variable environment." Ecological Modelling 78, no. 1-2 (March 1995): 117–27. http://dx.doi.org/10.1016/0304-3800(94)00122-x.

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34

Amoon, Mohammed. "Adaptive Framework for Reliable Cloud Computing Environment." IEEE Access 4 (2016): 9469–78. http://dx.doi.org/10.1109/access.2016.2623633.

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35

Papamarkou, Theodore, Alexey Lindo, and Eric B. Ford. "Geometric adaptive Monte Carlo in random environment." Foundations of Data Science 3, no. 2 (2021): 201. http://dx.doi.org/10.3934/fods.2021014.

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36

Takayama, Koji, Daniel J. Crawford, Patricio López-Sepúlveda, Josef Greimler, and Tod F. Stuessy. "Factors driving adaptive radiation in plants of oceanic islands: a case study from the Juan Fernández Archipelago." Journal of Plant Research 131, no. 3 (March 13, 2018): 469–85. http://dx.doi.org/10.1007/s10265-018-1023-z.

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Abstract Adaptive radiation is a common evolutionary phenomenon in oceanic islands. From one successful immigrant population, dispersal into different island environments and directional selection can rapidly yield a series of morphologically distinct species, each adapted to its own particular environment. Not all island immigrants, however, follow this evolutionary pathway. Others successfully arrive and establish viable populations, but they remain in the same ecological zone and only slowly diverge over millions of years. This transformational speciation, or anagenesis, is also common in oceanic archipelagos. The critical question is why do some groups radiate adaptively and others not? The Juan Fernández Islands contain 105 endemic taxa of angiosperms, 49% of which have originated by adaptive radiation (cladogenesis) and 51% by anagenesis, hence providing an opportunity to examine characteristics of taxa that have undergone both types of speciation in the same general island environment. Life form, dispersal mode, and total number of species in progenitors (genera) of endemic angiosperms in the archipelago were investigated from literature sources and compared with modes of speciation (cladogenesis vs. anagenesis). It is suggested that immigrants tending to undergo adaptive radiation are herbaceous perennial herbs, with leaky self-incompatible breeding systems, good intra-island dispersal capabilities, and flexible structural and physiological systems. Perhaps more importantly, the progenitors of adaptively radiated groups in islands are those that have already been successful in adaptations to different environments in source areas, and which have also undergone eco-geographic speciation. Evolutionary success via adaptive radiation in oceanic islands, therefore, is less a novel feature of island lineages but rather a continuation of tendency for successful adaptive speciation in lineages of continental source regions.

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37

Huang, Guo Rui, Fei Liu, Xu Cheng, and Chao Fan Qiu. "The ERM-Based Self-Adaptive Agent Model." Advanced Materials Research 341-342 (September 2011): 494–98. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.494.

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Inspired by the mechanism of behaviour control of organisms, this paper attempts to build an underlying model of their physical and psychological activities in a dynamic environment with the purpose to achieve the self-adaption of robot behaviour in a complex, changeable environment. It designs an internal environment and emotion- & motive-generating models based upon the endocrine regulation mechanism (ERM). The robot relies upon the dynamic stability of the internal environment to accomplish its self-adaption to the external environment. The emotion-generating model is applied to in vivo and in vitro contingencies of various kinds; by releasing hormones, the emotion generated influences the robot’s in vivo and in vitro perception, and then its behavioural choice. The motive-generating model is employed to maintain the relative stability of the internal environment, and the motive generated leads to the corresponding behavioural choice directly. The experimental results prove the effectiveness of this ERM-based self-adaptive robot model.

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38

Dehghan, Seyed Ali Mohamad, Mohammad Danesh, Farid Sheikholeslam, and Maryam Zekri. "Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network." Applied Soft Computing 28 (March 2015): 527–40. http://dx.doi.org/10.1016/j.asoc.2014.12.021.

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39

Zhang, Weizhan, Hao He, Shuyan Ye, Zhiwen Wang, and Qinghua Zheng. "Enhancing QoE for Mobile Users by Environment-Aware HTTP Adaptive Streaming." Sensors 18, no. 11 (October 27, 2018): 3645. http://dx.doi.org/10.3390/s18113645.

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HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.

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Gomez-Mestre, Ivan, and Roger Jovani. "A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation, population viability and genetic variation." Proceedings of the Royal Society B: Biological Sciences 280, no. 1771 (November 22, 2013): 20131869. http://dx.doi.org/10.1098/rspb.2013.1869.

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An ongoing new synthesis in evolutionary theory is expanding our view of the sources of heritable variation beyond point mutations of fixed phenotypic effects to include environmentally sensitive changes in gene regulation. This expansion of the paradigm is necessary given ample evidence for a heritable ability to alter gene expression in response to environmental cues. In consequence, single genotypes are often capable of adaptively expressing different phenotypes in different environments, i.e. are adaptively plastic. We present an individual-based heuristic model to compare the adaptive dynamics of populations composed of plastic or non-plastic genotypes under a wide range of scenarios where we modify environmental variation, mutation rate and costs of plasticity. The model shows that adaptive plasticity contributes to the maintenance of genetic variation within populations, reduces bottlenecks when facing rapid environmental changes and confers an overall faster rate of adaptation. In fluctuating environments, plasticity is favoured by selection and maintained in the population. However, if the environment stabilizes and costs of plasticity are high, plasticity is reduced by selection, leading to genetic assimilation, which could result in species diversification. More broadly, our model shows that adaptive plasticity is a common consequence of selection under environmental heterogeneity, and hence a potentially common phenomenon in nature. Thus, taking adaptive plasticity into account substantially extends our view of adaptive evolution.

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41

Tu, Hung-Ming. "The Attractiveness of Adaptive Heritage Reuse: A Theoretical Framework." Sustainability 12, no. 6 (March 18, 2020): 2372. http://dx.doi.org/10.3390/su12062372.

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Adaptive heritage reuse is a useful method to bring new meaning into a culture, manage heritage sites, and promote tourism development. However, it is not always successful, and there is no theoretical framework to understand its attractiveness and value. This study aimed at developing such a theoretical framework based on the analysis of nine cases of adaptive heritage reuse in Taiwan. The probe question technique of qualitative interview was used to assess the attraction framework. A total of 90 respondents were interviewed based on constant comparative analysis with the sampling strategy of theoretical saturation. The results illustrate the heritage and activities of the reuse environments, including natural and regional environments. These environments produce recreational values, including self-growth, health benefits, and social benefits. As promoting activities is an important attraction for tourists in the heritage reuse environment, the natural environment can be used to plan and design heritage outdoor activities. Finally, the regional environment can be an important basis for assessing the feasibility of adaptive heritage reuse, including historical streets, surrounding tourist attractions, and high transportation accessibility. This theoretical framework can be used to achieve sustainable management of heritage sites.

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42

Dunning, David. "Misbelief and the neglect of environmental context." Behavioral and Brain Sciences 32, no. 6 (December 2009): 517–18. http://dx.doi.org/10.1017/s0140525x09991208.

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AbstractFocusing on the individual's internal cognitive architecture, McKay & Dennett (M&D) provide an incomplete analysis because they neglect the crucial role played by the external environment in producing misbeliefs and determining whether those misbeliefs are adaptive. In some environments, positive illusions are not adaptive. Further, misbeliefs often arise because the environment commonly fails to provide crucial information needed to form accurate judgments.

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43

Tang, Hongyan, Dan Zhang, and Zhongxue Gan. "Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion." Sensors 20, no. 16 (August 7, 2020): 4411. http://dx.doi.org/10.3390/s20164411.

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Vertical take-off and landing unmanned aerial vehicles (VTOL UAV) are widely used in various fields because of their stable flight, easy operation, and low requirements for take-off and landing environments. To further expand the UAV’s take-off and landing environment to include a non-structural complex environment, this study developed a landing gear robot for VTOL vehicles. This article mainly introduces the adaptive landing control of the landing gear robot in an unstructured environment. Based on the depth camera (TOF camera), IMU, and optical flow sensor, the control system achieves multi-sensor data fusion and uses a robotic kinematical model to achieve adaptive landing. Finally, this study verifies the feasibility and effectiveness of adaptive landing through experiments.

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44

Seo, Changwoo, Akash Guru, Michelle Jin, Brendan Ito, Brianna J. Sleezer, Yi-Yun Ho, Elias Wang, et al. "Intense threat switches dorsal raphe serotonin neurons to a paradoxical operational mode." Science 363, no. 6426 (January 31, 2019): 538–42. http://dx.doi.org/10.1126/science.aau8722.

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Survival depends on the selection of behaviors adaptive for the current environment. For example, a mouse should run from a rapidly looming hawk but should freeze if the hawk is coasting across the sky. Although serotonin has been implicated in adaptive behavior, environmental regulation of its functional role remains poorly understood. In mice, we found that stimulation of dorsal raphe serotonin neurons suppressed movement in low- and moderate-threat environments but induced escape behavior in high-threat environments, and that movement-related dorsal raphe serotonin neural dynamics inverted in high-threat environments. Stimulation of dorsal raphe γ-aminobutyric acid (GABA) neurons promoted movement in negative but not positive environments, and movement-related GABA neural dynamics inverted between positive and negative environments. Thus, dorsal raphe circuits switch between distinct operational modes to promote environment-specific adaptive behaviors.

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45

Connolly, Martin, Ivana Dusparic, Georgios Iosifidis, and Melanie Bouroche. "Adaptive Reward Allocation for Participatory Sensing." Wireless Communications and Mobile Computing 2018 (August 7, 2018): 1–15. http://dx.doi.org/10.1155/2018/6353425.

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Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. However, as an environment is constantly changing (for example, an accident causing a buildup of traffic and elevated pollution levels), the value of a given data item to the service provider is likely to change significantly over time, and therefore an incentivization scheme must be able to adapt the rewards it offers in real-time to match the environmental conditions and current participation rates, thereby optimizing the consumption of the service provider’s budget. This paper presents adaptive reward allocation (ARA), which uses the Lyapunov Optimization method to provide adaptive reward allocation that optimizes the consumption of the service provider’s budget. ARA is evaluated using a simulated participatory sensing environment with experimental results showing that the rewards offered to participants are adjusted so as to ensure that the data captured matches the dynamic changes occurring in the sensing environment and takes the response rate into account while also seeking to optimize budget consumption.

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Lyu, Weiwei, Xianghong Cheng, and Jinling Wang. "Adaptive Federated IMM Filter for AUV Integrated Navigation Systems." Sensors 20, no. 23 (November 28, 2020): 6806. http://dx.doi.org/10.3390/s20236806.

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High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

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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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Jin, Chengming, Baigen Cai, Jian Wang, and Allison Kealy. "DTM-Aided Adaptive EPF Navigation Application in Railways." Sensors 18, no. 11 (November 9, 2018): 3860. http://dx.doi.org/10.3390/s18113860.

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The diverse operating environments change GNSS measurement noise covariance in real time, and different GNSS techniques hold different measurement noise covariance as well. Mismodelling the covariance causes undependable filtering results and even degenerates the GNSS/INS Particle Filter (PF) process, due to the fact that INS error-state noise covariance is much smaller than that of GNSS measurement noise. It also makes the majority of existing methods for adaptively adjusting filter parameters incapable of performing well. In this paper, a feasible Digital Track Map-aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is also estimated along with every sampling period through making full use of DTM information. The proposed approach is successfully examined in a railway environment, and the on-site experimental results reveal that the adaptive approach holds better positioning performance in comparison to the methods without adaptive adjustment. Improvements of 62.4% and 14.9% in positioning accuracy are obtained in contrast to Standard Point Positioning (SPP) and Precise Point Positioning (PPP), respectively. The proposed adaptive method takes advantage of DTM information and is able to automatically adapt to complex railway environments and different GNSS techniques.

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Nikonova, A. A. "Synthesis of Adaptive Systems in an Unstable Environment." MIR (Modernization. Innovation. Research) 11, no. 2 (July 23, 2020): 162–78. http://dx.doi.org/10.18184/2079-4665.2020.11.2.162-178.

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Purpose: the article is devoted to the ways to create adaptive production systems, taking into account the experience of the economies that have successfully emerged from the terminal crisis. One of the tasks is to improve the adaptability of the production system or, at the best, of the socio-economic system as a whole, in a now situation of uncertainty and unpredictable social, technological, structural, economic dynamics.Methods: an approach is based on the provisions of systemic economic theory, specifically on the representation of the socio-economic system in the form of a tetrad, comprising several key subsystems (sectors) that exchange resources (powers) that they possess.Results: noticeable contemporary conditions and the characteristics of the global socio-economic system are identified as a terminal crisis, which is determined by the depth and breadth to cover the regions, countries, industries with serious changes of a new quality in the functioning of economic objects at the different hierarchy levels. Such a crisis, in contrast to a structural crisis, affects all social groups, spheres of subject affairs and actors relations. The paper argues the quite relevant issue of how to make a systemic transition to a new model and paradigm of the economy. With this purpose, we have identified three groups of factors on which a model of the economy in a post-pandemic world can be based at least. Theoretical grounds and practical conclusions are given in order to carry out system assembly (synthesis) of a production system based on the model of the tetrad.Conclusions and Relevance: it is shown that the synthesis of all types of systems – socio-economic, industrial, innovation system – should be carried out on the basis on systemic economic paradigm, taking into account both the new current phenomena and peculiarities on the different economic levels. According to the author conclusion, some of the principles, proposed to organize and manage the corporate systems by Deming, can be used to increase adaptive ability of the production systems at least at the micro level of the Russian economy, which will inevitably be being transformed in a post-pandemic world.

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Meroufel, Bakhta, and Ghalem Belalem. "Adaptive checkpointing with reliable storage in cloud environment." Multiagent and Grid Systems 13, no. 3 (September 28, 2017): 253–68. http://dx.doi.org/10.3233/mgs-170270.

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