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Journal articles on the topic 'Artificial feedback'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Verbruggen, H. B., and K. J. Åström. "Artificial intelligence and feedback control." Annual Review in Automatic Programming 15 (January 1989): 1–11. http://dx.doi.org/10.1016/0066-4138(89)90002-5.

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2

Verbruggen, H. B., and K. J. Åström. "Artificial Intelligence and Feedback Control." IFAC Proceedings Volumes 22, no. 13 (1989): 1–11. http://dx.doi.org/10.1016/b978-0-08-040185-0.50006-3.

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3

Herzog, Sebastian, Christian Tetzlaff, and Florentin Wörgötter. "Evolving artificial neural networks with feedback." Neural Networks 123 (March 2020): 153–62. http://dx.doi.org/10.1016/j.neunet.2019.12.004.

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4

Joshi, Sandeep, and Satpal Singh Kushwaha. "Query Expansion using Artificial Relevance Feedback." International Journal of Computer Applications 44, no. 7 (2012): 41–45. http://dx.doi.org/10.5120/6279-8448.

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5

Pistohl, Tobias, Deepak Joshi, Gowrishankar Ganesh, Andrew Jackson, and Kianoush Nazarpour. "Artificial Proprioceptive Feedback for Myoelectric Control." IEEE Transactions on Neural Systems and Rehabilitation Engineering 23, no. 3 (2015): 498–507. http://dx.doi.org/10.1109/tnsre.2014.2355856.

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6

Li, Q. Q., Z. C. He, and Eric Li. "The feedback artificial tree (FAT) algorithm." Soft Computing 24, no. 17 (2020): 13413–40. http://dx.doi.org/10.1007/s00500-020-04758-2.

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7

Şahin, Savaş. "Learning Feedback Linearization Using Artificial Neural Networks." Neural Processing Letters 44, no. 3 (2015): 625–37. http://dx.doi.org/10.1007/s11063-015-9484-8.

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8

Rahman, Md Ataur, Sumeet Walia, Sumaiya Naznee, et al. "Artificial Somatosensors: Feedback Receptors for Electronic Skins." Advanced Intelligent Systems 2, no. 11 (2020): 2000094. http://dx.doi.org/10.1002/aisy.202000094.

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9

Rahman, Md Ataur, Sumeet Walia, Sumaiya Naznee, et al. "Artificial Somatosensors: Feedback Receptors for Electronic Skins." Advanced Intelligent Systems 2, no. 11 (2020): 2070106. http://dx.doi.org/10.1002/aisy.202070106.

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10

Badakva, A. M., N. V. Miller, and L. N. Zobova. "Artificial feedback for invasive brain–computer interfaces." Human Physiology 42, no. 1 (2016): 111–18. http://dx.doi.org/10.1134/s0362119716010023.

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11

Karami, Abir B., Karim Sehaba, and Benoit Encelle. "Adaptive artificial companions learning from users’ feedback." Adaptive Behavior 24, no. 2 (2016): 69–86. http://dx.doi.org/10.1177/1059712316634062.

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12

Liu, Hui, Qiuxia Yang, Ruizi Peng, et al. "Artificial Signal Feedback Network Mimicking Cellular Adaptivity." Journal of the American Chemical Society 141, no. 16 (2019): 6458–61. http://dx.doi.org/10.1021/jacs.8b13816.

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13

Egawa, Masakazu, Takumi Watanabe, and Taro Nakamura. "A 1-DOF Wearable Force Feedback Device with Pneumatic Artificial Muscles and MR Brake." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 243–44. http://dx.doi.org/10.1299/jsmeicam.2015.6.243.

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14

Park, Eunil, Ki Joon Kim, and Angel P. del Pobil. "The Effects of Multimodal Feedback and Gender on Task Performance of Stylus Pen Users." International Journal of Advanced Robotic Systems 9, no. 1 (2012): 30. http://dx.doi.org/10.5772/50187.

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As various interactive input devices for computers have become available, the role of multimodal feedbacks generated by the devices has gained an increasing emphasis in recent years, with debates surrounding the relative efficiency of different feedback types of input devices. To address this and related issues, the present study conducted a 4 (types of feedback: visual vs. tactile vs. auditory vs. combined feedback) x 2 (gender: male vs. female) within-subject experiment to examine the effects of the type of feedbacks and gender on the efficiency and accuracy of a multimodal stylus pen. Resul

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15

Bell, Audrey K., and Caroline G. L. Cao. "How Does Artificial Force Feedback Affect Laparoscopic Surgery Performance?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 11 (2007): 646–50. http://dx.doi.org/10.1177/154193120705101109.

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The use of haptic devices to provide force feedback in teleoperation has been shown to enhance performance. An experiment was conducted to examine whether artificial force feedback is utilized in the same manner as real force feedback in a simulated laparoscopic tissue-probing task. Forces in probing a double-layer silicon gel mass were replicated and exaggerated in a virtual environment using a haptic device. Ten subjects performed the probing task in four different conditions: 1) realistic force feedback, 2) exaggerated feedback, 3) disproportionately exaggerated forces, and 4) reversed and

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16

KIKUCHI, Satoru. "Development of Force-Feedback Device using Artificial Muscle." Journal of the Visualization Society of Japan 28-1, no. 1 (2008): 319. http://dx.doi.org/10.3154/jvs.28.319.

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17

BAO, Gang. "Force feedback dataglove based on pneumatic artificial muscles." Chinese Journal of Mechanical Engineering (English Edition) 19, no. 04 (2006): 588. http://dx.doi.org/10.3901/cjme.2006.04.588.

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18

Veltink, Peter H. "Sensory feedback in artificial control of human mobility." Technology and Health Care 7, no. 6 (1999): 383–91. http://dx.doi.org/10.3233/thc-1999-7602.

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19

Baddari, Kamel, Noureddine Djarfour, Tahar Aïfa, and Jalal Ferahtia. "Acoustic impedance inversion by feedback artificial neural network." Journal of Petroleum Science and Engineering 71, no. 3-4 (2010): 106–11. http://dx.doi.org/10.1016/j.petrol.2009.09.012.

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20

KIGUCHI, Kazuo, and Hiroshi SATO. "2A1-B15 Intelligent Artificial Arms considering Sensory Feedback." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _2A1—B15_1—_2A1—B15_2. http://dx.doi.org/10.1299/jsmermd.2008._2a1-b15_1.

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21

Yoshimi, A., K. Asahi, K. Sakai, et al. "Nuclear spin maser with an artificial feedback mechanism." Physics Letters A 304, no. 1-2 (2002): 13–20. http://dx.doi.org/10.1016/s0375-9601(02)01324-5.

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22

Abiri, Ahmad, Yen-Yi Juo, Anna Tao, et al. "Artificial palpation in robotic surgery using haptic feedback." Surgical Endoscopy 33, no. 4 (2018): 1252–59. http://dx.doi.org/10.1007/s00464-018-6405-8.

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23

Schlecht, Sebastian J., and Emanuël A. P. Habets. "Time-varying feedback matrices in feedback delay networks and their application in artificial reverberation." Journal of the Acoustical Society of America 138, no. 3 (2015): 1389–98. http://dx.doi.org/10.1121/1.4928394.

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24

Du, Jiang, and Ning Zheng. "Application of Two Dimensional Bar Code in One-Way Transmission System." Applied Mechanics and Materials 433-435 (October 2013): 1736–41. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1736.

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In the physical isolation network, users usually adopt the one-way transmission system to transmit data from low confidence network to high confidence network. But due to the one-way strictly, one-way transmission system can’t transmit the feedback message from high to low confidence network. What’s more, there are some imperfections in the SMS feedback mechanism and artificial feedback mechanism. Aiming at this issue, we put forward and introduce an improvement feedback mechanism based on the artificial feedback mechanism and the two dimensional bar code, at the same time , we illustrate the

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25

Mandal, Supriyo, and Abyayananda Maiti. "Explicit feedback meet with implicit feedback in GPMF: a generalized probabilistic matrix factorization model for recommendation." Applied Intelligence 50, no. 6 (2020): 1955–78. http://dx.doi.org/10.1007/s10489-020-01643-1.

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26

Van Dijk, Henk, and Hermie J. Hermens. "Artificial feedback for remotely supervised training of motor skills." Journal of Telemedicine and Telecare 12, no. 1_suppl (2006): 50–52. http://dx.doi.org/10.1258/135763306777978588.

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27

Rocchesso, D., and J. O. Smith. "Circulant and elliptic feedback delay networks for artificial reverberation." IEEE Transactions on Speech and Audio Processing 5, no. 1 (1997): 51–63. http://dx.doi.org/10.1109/89.554269.

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28

Wang, Hui-Ming, Chao Wang, and Derrick Wing Kwan Ng. "Artificial Noise Assisted Secure Transmission Under Training and Feedback." IEEE Transactions on Signal Processing 63, no. 23 (2015): 6285–98. http://dx.doi.org/10.1109/tsp.2015.2465301.

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29

Lam, T. M., H. W. Boschloo, M. Mulder, and M. M. van Paassen. "Artificial Force Field for Haptic Feedback in UAV Teleoperation." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 39, no. 6 (2009): 1316–30. http://dx.doi.org/10.1109/tsmca.2009.2028239.

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30

Schnermann, J., G. Schubert, and J. Briggs. "Tubuloglomerular feedback responses with native and artificial tubular fluid." American Journal of Physiology-Renal Physiology 250, no. 1 (1986): F16—F21. http://dx.doi.org/10.1152/ajprenal.1986.250.1.f16.

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Experiments were performed to compare the tubuloglomerular feedback response to native and artificial tubular fluid. The change in early proximal flow rate produced by changes in loop of Henle flow rate was measured in anesthetized rats using micropuncture techniques. Loop perfusion fluid was either an artificial solution with an electrolyte composition similar to that of proximal fluid (ATF) or native tubular fluid (NTF) collected from the late proximal tubule. In control rats, in rats on a low NaCl diet, in rats on restricted food intake, and in acutely saline-expanded rats no differences we

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31

Khalil, H. A., M. A. Franchek, K. A. Kadipasaoglu, et al. "FEEDBACK CONTROL OF THE CONTINUOUS FLOW TOTAL ARTIFICIAL HEART." ASAIO Journal 52, no. 2 (2006): 37A. http://dx.doi.org/10.1097/00002480-200603000-00162.

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32

Wang, Kexin, and Xianjun Sheng. "Grinding control using artificial neural networks with AE feedback." International Journal of Machine Intelligence and Sensory Signal Processing 1, no. 1 (2013): 55. http://dx.doi.org/10.1504/ijmissp.2013.052870.

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33

Olukunle Kolawole, Soyinka, and Duan Haibin. "Satellite formation keeping via chaotic artificial bee colony." Aircraft Engineering and Aerospace Technology 89, no. 2 (2017): 246–56. http://dx.doi.org/10.1108/aeat-02-2014-0019.

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Purpose Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms. Design/methodology/approach The chaotic artificial bee colony algor

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34

Zhang, Bo, Xiaoxuan Qi, and Xiaowei Han. "An Advanced User Intent Model Based On User Learning Process." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2019): 2050024. http://dx.doi.org/10.1142/s021800142050024x.

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User intent analysis is a continuous research hotspot in the field of query expansion. However, the big amount of irrelevant feedbacks in search log has negatively impacted the precision of user intent model. By observing the log, it can be found that tentative click is a major source of irrelevant feedback. It is also observed that a kind of new feedback information can be extracted from the log to recognize the characteristics of tentative clicks. With this new feedback information, this paper proposes an advanced user intent model and applies it into query expansion. Experiment results show

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35

More, Marcel, and Ondrej Liska. "Design of Active Feedback for Rehabilitation Robot." Applied Mechanics and Materials 611 (August 2014): 529–35. http://dx.doi.org/10.4028/www.scientific.net/amm.611.529.

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Sensor systems are an essential part of automated equipment. They are even more important in machines that come in contact with people, because they have a significant impact on safety. This paper describes the design of active feedback for rehabilitation device driven by pneumatic artificial muscles. Here are presented three methods for measuring the load of the robot. The first is a system composed of Force Sensitive Resistors (FSR) placed in the handle of the device. Two other methods are intended to measure the load of the actuator composed of artificial muscles. The principle of one metho

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36

Peterson, O. W., L. C. Gushwa, C. B. Wilson, and R. C. Blantz. "Tubuloglomerular feedback activity after glomerular immune injury." American Journal of Physiology-Renal Physiology 257, no. 1 (1989): F67—F71. http://dx.doi.org/10.1152/ajprenal.1989.257.1.f67.

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Tubuloglomerular feedback responses were examined in control euvolemic and glomerulonephritic rats 4 wk after administration of antiglomerular basement membrane antibody (AGBM). Single-nephron glomerular filtration rate (SNGFR) was reduced approximately 30% in AGBM rats. Tubuloglomerular feedback responses were also tested with both artificial and native tubular fluid. SNGFR was evaluated at 0, 10, 20, 30, and 40 nl/min. Tubuloglomerular feedback response was present in both control and AGBM rats, although there was a shift in turning point or perfusion rate down and leftward at which SNGFR de

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37

Kano, Hideaki, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, and Masashi Sugiyama. "Good arm identification via bandit feedback." Machine Learning 108, no. 5 (2019): 721–45. http://dx.doi.org/10.1007/s10994-019-05784-4.

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38

Truong, Quoc-Tuan, and Hady W. Lauw. "Variational learning from implicit bandit feedback." Machine Learning 110, no. 8 (2021): 2085–105. http://dx.doi.org/10.1007/s10994-021-06028-0.

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39

Tóthová, Mária, Ján Piteľ, and Jana Mižáková. "Electro-Pneumatic Robot Actuator with Artificial Muscles and State Feedback." Applied Mechanics and Materials 460 (November 2013): 23–31. http://dx.doi.org/10.4028/www.scientific.net/amm.460.23.

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Pneumatic position servo system with artificial muscles described in this paper represents feedback control system with non-linear compensation controller of state variables. The designed system demonstrates the operating characteristics that are significantly more favorable than the original characteristics without compensation and they are similar to the properties of the linear system. Such system has principally a shorter control time, significantly lower dynamic control error and it allows apply larger constants of the controller. Following an increased invariance of system against distur

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40

Stolbkov, Yu K., and I. V. Orlov. "Artificial vestibular feedback in conditions of a modified body scheme." Neuroscience and Behavioral Physiology 39, no. 2 (2009): 173–81. http://dx.doi.org/10.1007/s11055-009-9111-0.

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41

Schostek, Sebastian, Marc O. Schurr, and Gerhard F. Buess. "Review on aspects of artificial tactile feedback in laparoscopic surgery." Medical Engineering & Physics 31, no. 8 (2009): 887–98. http://dx.doi.org/10.1016/j.medengphy.2009.06.003.

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42

Nakada, Kazuki, Tetsuya Asai, and Yoshihito Amemiya. "Design of an Artificial Central Pattern Generator with Feedback Controller." Intelligent Automation & Soft Computing 10, no. 2 (2004): 185–92. http://dx.doi.org/10.1080/10798587.2004.10642876.

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43

Zhang, Xi, Matthew R. McKay, Xiangyun Zhou, and Robert W. Heath. "Artificial-Noise-Aided Secure Multi-Antenna Transmission With Limited Feedback." IEEE Transactions on Wireless Communications 14, no. 5 (2015): 2742–54. http://dx.doi.org/10.1109/twc.2015.2391261.

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44

Rocchesso, D. "Maximally diffusive yet efficient feedback delay networks for artificial reverberation." IEEE Signal Processing Letters 4, no. 9 (1997): 252–55. http://dx.doi.org/10.1109/97.623041.

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45

Joyce, Bryan S., and Pablo A. Tarazaga. "Developing an active artificial hair cell using nonlinear feedback control." Smart Materials and Structures 24, no. 9 (2015): 094004. http://dx.doi.org/10.1088/0964-1726/24/9/094004.

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46

Joseph, Jeffrey I., and Marc C. Torjman. "The Artificial Pancreas Symposium: Glucose Monitoring, Insulin Delivery, Feedback Control." Diabetes Technology & Therapeutics 1, no. 3 (1999): 323–36. http://dx.doi.org/10.1089/152091599317242.

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47

Walsh, Thomas J., Ali Nouri, Lihong Li, and Michael L. Littman. "Learning and planning in environments with delayed feedback." Autonomous Agents and Multi-Agent Systems 18, no. 1 (2008): 83–105. http://dx.doi.org/10.1007/s10458-008-9056-7.

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48

Dang, Hao, and Peter K. Allen. "Stable grasping under pose uncertainty using tactile feedback." Autonomous Robots 36, no. 4 (2013): 309–30. http://dx.doi.org/10.1007/s10514-013-9355-y.

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49

Jetchev, Nikolay, and Marc Toussaint. "Discovering relevant task spaces using inverse feedback control." Autonomous Robots 37, no. 2 (2014): 169–89. http://dx.doi.org/10.1007/s10514-014-9384-1.

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50

HSU, CHING-CHI, and CHIA-HUI CHANG. "WEBYACHT: A CONCEPT-BASED SEARCH TOOL FOR WWW." International Journal on Artificial Intelligence Tools 08, no. 02 (1999): 137–56. http://dx.doi.org/10.1142/s0218213099000105.

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This paper describes a Web information search tool called WebYacht. The goal of WebYacht is to solve the problem of imprecise search results in current Web search engines. Due to incomplete information given by users and the diversified information published on the Web, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given as in most cases. In order to clarify the ambiguity of the short queries given by users, WebYacht adopts cluster-based browsing model as well as relevance feedback to fa

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