Effects of partial replacement of fish meal by yeast hydrolysate on complement system and stress resistance in juvenile Jian carp (Cyprinus carpio var. Jian).

Science.gov (United States)

Yuan, Xiang-Yang; Liu, Wen-Bin; Liang, Chao; Sun, Cun-Xin; Xue, Yun-Fei; Wan, Zu-De; Jiang, Guang-Zhen

2017-08-01

A 10-week feeding trial was carried out to investigate the effects of dietary fish meal replacement by yeast hydrolysate (YH) on growth performance, complement system and stress resistance of juvenile Jian carp (Cyprinus carpio var. Jian) (initial average weight 19.44 ± 0.06 g). In the study, there were five groups: one control group was fed with a basal diet (YH0), and four treatment groups were fed with dietary fish meal replaced by 1% YH (YH1), 3% (YH3), 5% (YH5) and 7% (YH7), respectively. Each group had four replicates. At the end of feeding trial, twelve fish from each group (three fish per replicate) were randomly selected for assessing the growth and immunity. Meanwhile, 20 fish per replicate were injected by Aeromonas hydrophila. The results showed that (1) Replacement levels of YH significantly affected the growth of the fish with the highest values of weight gain (WG) occurred in fish fed YH3 diet. However, no significant difference in feed conversion ratios (FCR) was observed among all groups. (2) Pre-stressed plasma lysozyme activity, total protein and albumin contents and complement component 3 (C3) and complement component 4 (C4) levels of fish fed YH3 diet were significantly higher than those of fish fed YH0 diet. However, post-stressed immune parameters of fish in all groups were significantly lower. (3) There was a trend that the expression levels of the complement-related genes (c1r/s-A, c4-1, c3-H1, c5-1, fb/c2-A, mbl-2 and masp) initially increased and then decreased except mbl-2 and masp, with the maximum values observed in fish fed YH3 diet. Before stress, the expression levels of the inflammation-related genes (alp, il-1β and tnf-α) in the hepatopancreas and spleen of fish fed YH1 diet and YH7 diet were significant higher than that of fish fed YH0 diet. After stress, no significant difference in the expression levels of those genes was observed among all groups. These results indicated that FM replacement by YH could improve growth

XU Jian-zhong (许建中)——An Outstanding Doctor of Integrative Medicine on Respiratory Diseases

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

@@Professor XU Jian-zhong was born in a family of traditional Chinese doctor in Beijing, August, 1930. His native place is Putian, Fujian Province. Prof. XU has engaged for more than 40 years in clinical medicine since he was graduated from Shanxi Medical University in 1954. From 1958 to 1961, he was released from work to attend the TCM Learning Class for Doctors of Western Medicine for 3 years and granted the first grade prize by the Ministry of Health at graduation. Since then, the integration of traditional Chinese and western medicine (TCM-WM) has been the cause he devoted himself to. Now, he is Chief Doctor in Xiyuan Hospital, China Academy of Traditional Chinese Medicine, visiting professor of postgraduate education Department of China Academy of TCM, and Director of Specialty Committee of Respiratory Diseases, Chinese Association of Integration of Traditional and Western Medicine (CAIM).

Comparative analysis of the main bioactive components of Xin-Sheng-Hua granule and its single herbs by ultrahigh performance liquid chromatography with tandem mass spectrometry.

Science.gov (United States)

Pang, Hanqing; Wang, Jun; Tang, Yuping; Xu, Huiqin; Wu, Liang; Jin, Yi; Zhu, Zhenhua; Guo, Sheng; Shi, Xuqin; Huang, Shengliang; Sun, Dazheng; Duan, Jin-Ao

2016-11-01

Xin-Sheng-Hua granule, a representative formula for postpartum hemorrhage, has been used clinically to treat postpartum diseases. Its main bioactive components comprise aromatic acids, phthalides, alkaloids, flavonoids, and gingerols among others. To investigate the changes in main bioactive constituents in its seven single herbs before and after compatibility, a rapid, simple, and sensitive method was developed for comparative analysis of 27 main bioactive components by using ultrahigh-performance liquid chromatography with triple quadrupole electrospray tandem mass spectrometry for the first time. The sufficient separation of 27 target constituents was achieved on a Thermo Scientific Hypersil GOLD column (100 mm × 3 mm, 1.9 μm) within 20 min under the optimized chromatographic conditions. Compared with the theoretical content, the observed content of each analyte showed remarkable differences in Xin-Sheng-Hua granule except thymine, p-coumaric acid, senkyunolide I, senkyunolide H, and ligustilide; the total contents of 27 components increased significantly, and the content variation degrees for the different components were gingerols > flavonoids > aromatic acids > alkaloids > phthalides. The results could provide a good reference for the quality control of Xin-Sheng-Hua granule and might be helpful to interpret the drug interactions based on variation of bioactive components in formulae. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Cultivating a "Jian Zi": Exploring an Art Teacher's Responsive Pedagogy for the Development of Art Talent in Taiwan

Science.gov (United States)

Chen, Wei-Ren

2017-01-01

This qualitative case study explored the operational curricula of an art teacher's responsive pedagogy that focused on the differentiation aspects for artistically talented students in an elementary school in Taiwan. Findings indicate that cultivating a "Jian Zi" is the heart of the value system embedded in the art teacher's responsive…

Synthesis and Physical Properties of Nanocomposites (SnO2x(In2O31-x (x = 0 – 1 for Gas Sensors and Optoelectronics

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Stanislav REMBEZA

2010-11-01

Full Text Available Experimental results on synthesis of thin film (< 1 μm nanocomposites (SnO2x(In2O31-x in the whole range of x = (0 – 1 mass. % are presented. Film nanocomposites were prepared by high-frequency magnetron sputtering of metal oxide targets in the controlled ambient Ar+O2. Films were deposited on the hot substrate (400 0C and investigated by X-ray phase analysis, atomic-force microscopy, optical and electrical methods. Influence of synthesis regimes and film composition on the grain size of crystals, the band-gap width, the transparency in the visible range of light, concentration and mobility of free charge carriers were determined. It was shown that films with composition (SnO2x(In2O31-x x = 0.9 are perspective for using as gas sensors, films of the same composition but with x = 0.1 can be applied as transparent current conducting electrodes for solar cells.

[Textual research on Guang dong xin yu (New Sayings of Guangdong) quoted in Ben cao gang mu shi yi (Supplements to Compendium of Materia Medica].

Science.gov (United States)

Zhang, Ruixian; Zhang, Wei; Li, Jian; Liang, Fei

2014-05-01

Altogether 15 terms for Guang dong xin yu (New Sayings of Guangdong) were used in Ben cao gang mu shi yi (Supplements to Compendium of Materia Medica), including Yue yu (Cantonese sayings), Chong yu (Sayings from Insect Drug), Jie yu (Sayings from Crustacean Drug), Xin yu (New Sayings), Yue hai xiang yu (Fragrant Sayings from Cantonese Region), Yue zhi mu yu (Sayings from Plants in Cantonese Annals), Guang dong suo yu (Trivial Sayings from Guangdong), Yue shan lu (Records of Cantonese Mountains), Yue lu (Cantonese Records), Jiao guang lu (Joint Guangdong Records), Yue cao zhi (Records of Cantonese Grasses), Guang guo lu (Records of Guangdong Fruits), Nan yue suo ji (Trivial Records of Southern Canton), Guang zhi (Guangdong Records), Yue zhi (Cantonese Records) etc. dealing with 57 sorts of drugs (with individual overlapping ones), the author of Xin yu was Qu Dajun, a surviving fogy of the Ming Dynasty actively involved in the activities to restore the old dynasty and resist the Qing Dynasty, and was persecuted in the literary inquisition in which his works were burnt so that Zhao Xuemin, when quoting his texts, had to go in a roundabout way.

Genoprotective effect of the Chinese herbal decoction xiao jian zhong tang.

Science.gov (United States)

Szeto, Yim-Tong; Cheng, Ngok-Fung; Pak, Sok-Cheon; Kalle, Wouter

2013-03-01

The Chinese herbal decoction formula Xiao Jian Zhong Tang (XJZT) is one of the classic formulas from the classic traditional Chinese medicine (TCM). Previous studies on XJZT found that it is effective for treating peptic ulcer, irritable bowel syndrome, functional gastroenteritis and similar psychosomatic disorders of the digestive organs. It has also been shown that all the herbs used in XJZT contain antioxidants. In this study, we investigated the in vitro DNA protection effect of the individual herb extracts and the whole formula. Water extract of the herbs and XJZT were used to pre-treat human lymphocytes. The lymphocytes were then exposed to hydrogen peroxide. The in vitro DNA protection effect of the herbs was investigated by comet assay. No DNA protective effect (P < 0.05) was found for individual herb extracts, but XJZT showed protection of human lymphocytic DNA upon oxidative stress (P < 0.05). The in vitro DNA protection effect of XJZT was conferred by the synergistic effect of the herbs, while the individual herbs had no such effect.

Hybridizing scholastic psychology with Chinese medicine: a seventeenth-century Chinese Catholic's conceptions of xin (mind and heart).

Science.gov (United States)

Zhang, Qiong

2008-01-01

This paper explores the dynamics of cultural interactions between early modern China and Europe initiated by the Jesuits and other Catholic missionaries through a case study of Wang Honghan, a seventeenth-century Chinese Catholic who systematically sought to integrate European learning introduced by the missionaries with pre-modern Chinese medicine. Focusing on the ways in which Wang combined his Western and Chinese sources to develop and articulate his views on xin (mind and heart), this paper argues that Wang arrived at a peculiar hybrid between scholastic psychology and Chinese medicine, not so much through a course of haphazard misunderstanding as through his conscious and patterned use and abuse of his Western sources, which was motivated most possibly by a wish to define a theoretical position that most suited his social roles as a Catholic convert and a Chinese medical doctor. Thus, rather than seeing Wang as an epitome of"transmission failure," this paper offers it as a showcase for the tremendous dynamism and creativity occurring at this East-West "contact zone as representatives of both cultures sought to appropriate and transform the symbolic and textual resources of the other side.

辛弃疾的“事功派”思想及在稼轩词中的体现%Xin Qi-ji’s Utilitarianism Ethics Thought And influence on Jiaxuan Ci-poems

Institute of Scientific and Technical Information of China (English)

宋学达

2013-01-01

事功派哲学家陈亮与辛弃疾私交甚厚，其哲学思想必然会对辛弃疾产生影响，而历来对辛弃疾的研究中，似乎很少关注其思想哲学的相关问题。本文旨在论证辛弃疾的哲学思想乃是以陈亮为代表的事功派哲学一脉，并探讨事功派哲学对稼轩词艺术特质及词史地位所产生的深刻影响。%Chen Liang, the philosopher of Utilitarianism Ethics Thought, has a good relationship with Xin Qi-ji. So his philosophical thoughts certainly would have an influence on Xin Qiji. But in the past, on the study of Xin Qi-ji, we can seldom found researches concerning his thought of philosophy. This dissertation is aimed to argue that Xin's philosophical thoughts is a brunch of Chen’s Utilitarianism Ethics Thought and the latter had great influence on Jiaxuan Ci-Poems, including its artistic particularities and the status in Ci-poetry history.

新力正骨喷雾剂治疗踝关节扭挫伤的临床观察%Clinical Observation of Xin Li Orthopedic Spray in the Treatment of Ankle Sprain

Institute of Scientific and Technical Information of China (English)

苟中生; 张丽文

2013-01-01

目的：观察新力正骨喷雾剂治疗踝关节扭挫伤的临床疗效。方法：入选2010年2月~2013年2月笔者所在科室就诊的140例踝关节扭挫伤患者，随机分为治疗组和对照组，各70例，治疗组患者应用新力正骨喷雾剂，对照组患者应用云南白药气雾剂。结果：治疗组患者经治疗后痊愈11例，显效46例，有效10例，无效3例，总有效率为95.71%，与对照组比较无统计学差异(P>0.05)。结论：新力正骨喷雾剂治疗踝关节扭挫伤临床疗效满意。%Objective:To observe the clinical efficacy of Xin Li orthopedic spray in the treatment of ankle sprain.Methods:140 patients with ankle sprain in the author department from February 2010 to February 2013 were selected and randomly divided into treatment group and control group.70 cases in the treatment group were given Xin Li orthopedic sprays, patients in the control group were given Yun Nan Bai Yao aerosol.Results:There were 11 cases recovered,46 cases effective,10 cases valid and 3 cases invalid after treatment. The total effective rate of the treatment group was 95.71%,there was no significant difference compared with the control group (P>0.05). Conclusion:The clinical efficacy of Xin Li orthopedic spray in the treatment of ankle sprain was desirable.

Effect of “Jian-Pi-Zhi-Dong Decoction” on Gamma-Aminobutyric Acid in a Mouse Model of Tourette Syndrome

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Wen Zhang

2014-01-01

Full Text Available The purpose of this study was to explore the positive effects of Jian-Pi-Zhi-Dong Decoction (JPZDD on Tourette syndrome (TS by investigating the expression of gamma-aminobutyric acid (GABA and its type A receptor (GABAAR in the striatum of a TS mice model. The model was induced by 3,3′-iminodipropionitrile (IDPN treatment; then mice were divided into 4 groups (n=22, each; control and IDPN groups were gavaged with saline and the remaining 2 groups were gavaged with tiapride and JPZDD. We recorded the stereotypic behaviors of TS mice and measured the content of GABA in striatum by HPLC and GABAAR expression by immunohistochemistry and real-time PCR. Our results showed that JPZDD inhibited the abnormal behaviors of TS model mice and decreased GABA levels and GABAAR protein and mRNA expression in the striatum of TS model mice. In brief, the mechanism by which JPZDD alleviates TS symptoms may be associated with GABAAR expression downregulation in striatum which may regulate GABA metabolism.

新力正骨喷雾剂治疗踝关节扭挫伤65例的疗效分析%Efifcts Analysis of Xinli Orthopedic Sprays in the Treatment of 65 Cases of Ankle Sprain

Institute of Scientific and Technical Information of China (English)

张兵; 褚福明

2014-01-01

Objective:To observe the clinical efficacy of Xinli Orthopedic Sprays in the treatment of ankle sprain.Methods:130 patients with ankle sprain in our hospital from May 2011 to May 2013 were randomly divided into treatment group and control group,each group had 65 cases respectively.The control group were given Yun Nan Bai Yao aerosol,while the experimental group were given Xinli Orthopedic Sprays,the clinical efficacy of treatment were evaluated.Results:There was no statistically significant difference between the cure rate,show efficiency rate,total efficiency rate and analgesic effective time,etc (P>0.05).Conclusion:The clinical efficacy of Xinli Orthopedic Sprays was equal as that of Yun Nan Bai Yao Aerosol.%目的：观察新力正骨喷雾剂治疗踝关节扭挫伤的临床疗效。方法：选择2011年5月~2013年5月期间我院收治的踝关节扭挫伤患者130例，随机分为试验组和对照组，各65例，对照组应用云南白药气雾剂，试验组应用新力正骨气雾剂，评价治疗后的临床疗效。结果：两组患者治疗后痊愈率，显效率，有效率，总有效率，及止痛显效时间等疗效比较差异均无统计学意义(P>0.05)。结论：新力正骨喷雾剂治疗踝关节扭挫伤的临床疗效与云南白药气雾剂相当。

Influence of nonmagnetic disorder on specific heat and electrical resistivity in Kondo lattice system CePd{sub 1−x}Ge{sub x}In

Energy Technology Data Exchange (ETDEWEB)

Gnida, D., E-mail: d.gnida@int.pan.wroc.pl [Institute of Low Temperature and Structure Research, Polish Academy of Sciences, 50-950 Wrocław (Poland); Dominyuk, N.; Zaremba, V. [Inorganic Chemistry Department, Ivan Franko Lviv National University, Kyryla and Mephodiya Str. 6, 79005 Lviv (Ukraine); Kaczorowski, D. [Institute of Low Temperature and Structure Research, Polish Academy of Sciences, 50-950 Wrocław (Poland)

2015-02-15

Highlights: • Interplay of Kondo and RKKY interactions in the presence of nonmagnetic disorder. • Suppression of the coherent Kondo state by nonmagnetic impurities. • Observation of quantum interference phenomena in Ce-based Kondo system. • Coexistence of incoherent Kondo effect and Altshuler-Aronov quantum correction. - Abstract: The alloy system CePd{sub 1−x}Ge{sub x}In with 0.1⩽x⩽0.4 was investigated by means of heat capacity and electrical resistivity measurements. Its low-temperature behavior has been found to be governed by the interplay of Kondo effect and Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions in the presence of atomic disorder in nonmagnetic atoms sublattice. The coherent Kondo state, observed for CePdIn, gradually vanishes with increasing the Ge-content. The incoherent Kondo state, which characterizes Ge-rich alloys, appears very sensitive to applied magnetic field. The observed systematic changes in the temperature- and field-dependent electrical transport in CePd{sub 1−x}Ge{sub x}In manifest the important role of quantum correction due to electron-electron interactions in weakly localized regime.

Impact assessment of projected climate change on diffuse phosphorous loss in Xin'anjiang catchment, China.

Science.gov (United States)

Zhai, Xiaoyan; Zhang, Yongyong

2018-02-01

.5 scenario (2020s, - 9.00~2.63%; 2030s, - 11.16~7.89%), followed by RCP2.6 (2020s, - 10.00~2.90%; 2030s, - 9.00~6.63%) and RCP4.5 (2020s, - 6.81~5.49%, 2030s, - 10.00~9.09%) scenarios. Decreasing of DP load mainly aggregated in the western and eastern mountainous regions, while it tended to increase in the northern and middle regions. This study was expected to provide insights into diffuse nutrient loss control and management in Xin'anjiang catchment, and scientific references for the implementation of water environmental protection in China.

Qing-Xin-Jie-Yu Granules in addition to conventional treatment for patients with stable coronary artery disease (QUEST Trial): study protocol for a randomized controlled trial.

Science.gov (United States)

Li, Shengyao; Guo, Ming; Mao, Huimin; Gao, Zhuye; Xu, Hao; Shi, Dazhuo

2016-09-15

Recurrent cardiovascular event remains high in stable coronary artery disease (SCAD), especially in patients with multiple risk factors, despite a high rate of use conventional treatment. Traditional Chinese Medicine (TCM) is a promising complementary and alternative medicine for treating SCAD, while evidence for its effect on long-term survival is limited. This study was designed to test if Chinese herbal medicine in addition to conventional treatment is more effective than conventional treatment alone in reducing major adverse cardiac event (MACE) for SCAD patients with multiple risk factors during a 1-year follow-up. This is a multicenter, placebo-controlled, double-blinded, randomized controlled clinical trial. A total of 1500 patients are randomized in a 1:1 ratio to receive the Qing-Xin-Jie-Yu Granules (QXJYG) or the placebo granules, twice daily for 6 months. The primary outcome is the combined outcomes including cardiac death, nonfatal myocardial infarction and revascularization. The secondary outcome is the combined outcomes including all-cause mortality, re-admission for acute coronary syndrome (ACS), heart failure, malignant supraventricular and ventricular arrhythmia influencing hemodynamics, ischemic stroke, and other thromboembolic events during 1-year follow-up. The assessment is performed at baseline (before randomization), 1, 3, 6, 9, and 12 months after randomization. This is the first multicenter trial sponsored by the national funding of China to evaluate TCM in combination with conventional treatment on 1-year survival in high-risk SCAD patients. If successful, it will provide an evidence-based complementary therapeutic approach for reducing MACE from SCAD. The trial was registered in the Chinese Clinical Trial Registry on December 28, 2013. The registration number is ChiCTR-TRC-13004370 .

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Simultaneous determination of phenolic acids and diterpenoids and their comparative pharmacokinetic study in normal and acute blood stasis rats by UFLC-MS/MS after oral administration of Guan-Xin-Shu-Tong capsules.

Science.gov (United States)

Gao, Xun; Mu, Jingqing; Guan, Shaoyi; Li, Qing; Du, Yiyang; Zhang, Huifen; Bi, Kaishun

2018-01-01

Guan-Xin-Shu-Tong capsules are one of the well-known and first-line Chinese traditional herbal formula for treating coronary heart disease. A validated and sensitive method via ultra fast liquid chromatography-tandem mass spectrometry (UFLC-MS/MS) was established to simultaneously determinate five phenolic acids and four diterpenoids in rats in order to investigate their pharmacokinetic profiles firstly. Analytes were extracted by ethyl acetate and determined via multiple reaction monitoring mode in both positive and negative ion modes. The values for limit of quantification were in range of 0.025-1.250ng/ml. Inter- and intra-day precisions were no more than 10.9% with accuracy of -11.0%-10.6%, meanwhile the stable and suitable extraction recoveries were also obtained. And finally such excellent method was used to compare the pharmacokinetics of nine compounds in normal and acute blood stasis rats after oral administration of Guan-Xin-Shu-Tong capsules. Copyright © 2017 Elsevier B.V. All rights reserved.

Wireless Power Control for Tactical MANET: Power Rate Bounds

Science.gov (United States)

2016-09-01

J; P. Hande; T. Lan; C. W. Tan [2008] Power Control in Cellular Networks, Now Publishers Inc., Hanover, MA. 12. Chaves, Fabiano de Sousa; F. R. P...Asymptotically Fast Convergence, IEEE Selected Areas in Communications, 18(3). 36. Jian, Tao; Nicholas D. Sidiropoulos; Georgios B. Giannakis [2003] Kalman...Power Control and Its Imperfections in CDMA Cellular Systems, IEEE Transactions on Vehicular Technologies, 48, pages 1706–1777. 75. Tan , Chee Wei

Taiwanese Composers and Piano Works in the XX Century: Traditional Chinese Culture and the Taiwan Xin Yinyue

Directory of Open Access Journals (Sweden)

Luca Pisano

2005-01-01

Full Text Available The paper will try to prove how piano music can be considered a main tool for understanding the evolution of XX century Taiwanese New Music. Starting from the first generation of Taiwanese composers and on the basis of their piano works, the meaning of "xin yinyue" will be analysed; the paper will then try to identify the influences of both so-called "traditional culture" and western music in the development of the new music written by the subsequent generations of composers. The role of Xuanyin yaji (Atelier de creation musicale "Formusica" in the spreading of new piano works in the 90s will be also taken into consideration, as it allows us to draw out an outline of present-day piano music trends in Taiwan.

Procedural learning during declarative control.

Science.gov (United States)

Crossley, Matthew J; Ashby, F Gregory

2015-09-01

There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control? Behavioral, neuroimaging, and neuroscience data are somewhat mixed with respect to these questions. Human neuroimaging and animal lesion studies suggest independent learning and are mostly agnostic with respect to control. Human behavioral studies suggest active inhibition of behavioral output but have little to say regarding learning. The results of two perceptual category-learning experiments are described that strongly suggest that procedural learning does occur while the explicit system is in control of behavior and that this learning might be just as good as if the procedural system was controlling the response. These results are consistent with the idea that declarative memory systems inhibit the ability of the procedural system to access motor output systems but do not prevent procedural learning. (c) 2015 APA, all rights reserved).

The Many Sides of Happy Lim: aka Hom Ah Wing, Lin Jian Fu, Happy Lum, Lin Chien Fu, Hom Yen Chuck, Lam Kin Foo, Lum Kin Foo, Hom, Lim Goon Wing, Lim Gin Foo, Gin Foo Lin, Koon Wing Lim, Henry Chin, Lim Ying Chuck, Lim Ah Wing, et. al.

Directory of Open Access Journals (Sweden)

Gordon H. Chang

2010-03-01

Full Text Available

Known to the FBI, INS, and IRS as Mr. Ah Wing Hom, he was also Lin Jian Fu, Jian Fu (Tough Guy, or just Fu to the readers of the many poems and short stories he published in Chinese over four decades in the twentieth century. And to still others, primarily English-readers of his writing, he was Happy Lim, an ironic, even tragically bizarre name, as his life was far from pleasant. On New Year's Day 1986, he died alone in a dingy San Francisco Chinatown bachelor hotel suffering from a bacterial infection and a chronic blood disorder that had required the amputation of all his toes the year before. He was seventy-eight years of age and had spent most of his life within blocks of where he died.

Indirect learning control for nonlinear dynamical systems

Science.gov (United States)

Ryu, Yeong Soon; Longman, Richard W.

1993-01-01

In a previous paper, learning control algorithms were developed based on adaptive control ideas for linear time variant systems. The learning control methods were shown to have certain advantages over their adaptive control counterparts, such as the ability to produce zero tracking error in time varying systems, and the ability to eliminate repetitive disturbances. In recent years, certain adaptive control algorithms have been developed for multi-body dynamic systems such as robots, with global guaranteed convergence to zero tracking error for the nonlinear system euations. In this paper we study the relationship between such adaptive control methods designed for this specific class of nonlinear systems, and the learning control problem for such systems, seeking to converge to zero tracking error in following a specific command repeatedly, starting from the same initial conditions each time. The extension of these methods from the adaptive control problem to the learning control problem is seen to be trivial. The advantages and disadvantages of using learning control based on such adaptive control concepts for nonlinear systems, and the use of other currently available learning control algorithms are discussed.

Design of fuzzy learning control systems for steam generator water level control

International Nuclear Information System (INIS)

Park, Gee Yong

1996-02-01

A fuzzy learning algorithm is developed in order to construct the useful control rules and tune the membership functions in the fuzzy logic controller used for water level control of nuclear steam generator. The fuzzy logic controllers have shown to perform better than conventional controllers for ill-defined or complex processes such as nuclear steam generator. Whereas the fuzzy logic controller does not need a detailed mathematical model of a plant to be controlled, its structure is to be made on the basis of the operator's linguistic information experienced from the plant operations. It is not an easy work and also there is no systematic way to translate the operator's linguistic information into quantitative information. When the linguistic information of operators is incomplete, tuning the parameters of fuzzy controller is to be performed for better control performance. It is the time and effort consuming procedure that controller designer has to tune the structure of fuzzy logic controller for optimal performance. And if the number of control inputs is many and the rule base is constructed in multidimensional space, it is very difficult for a controller designer to tune the fuzzy controller structure. Hence, the difficulty in putting the experimental knowledge into quantitative (or numerical) data and the difficulty in tuning the rules are the major problems in designing fuzzy logic controller. In order to overcome the problems described above, a learning algorithm by gradient descent method is included in the fuzzy control system such that the membership functions are tuned and the necessary rules are created automatically for good control performance. For stable learning in gradient descent method, the optimal range of learning coefficient not to be trapped and not to provide too slow learning speed is investigated. With the optimal range of learning coefficient, the optimal value of learning coefficient is suggested and with this value, the gradient

Improving the Xin'anjiang hydrological model based on mass–energy balance

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Y.-H. Fang

2017-07-01

Full Text Available Conceptual hydrological models are preferable for real-time flood forecasting, among which the Xin'anjiang (XAJ model has been widely applied in humid and semi-humid regions of China. Although the relatively simple mass balance scheme ensures a good performance of runoff simulation during flood events, the model still has some defects. Previous studies have confirmed the importance of evapotranspiration (ET and soil moisture content (SMC in runoff simulation. In order to add more constraints to the original XAJ model, an energy balance scheme suitable for the XAJ model was developed and coupled with the original mass balance scheme of the XAJ model. The detailed parameterizations of the improved model, XAJ-EB, are presented in the first part of this paper. XAJ-EB employs various meteorological forcing and remote sensing data as input, simulating ET and runoff yield using a more physically based mass–energy balance scheme. In particular, the energy balance is solved by determining the representative equilibrium temperature (RET, which is comparable to land surface temperature (LST. The XAJ-EB was evaluated in the Lushui catchment situated in the middle reach of the Yangtze River basin for the period between 2004 and 2007. Validation using ground-measured runoff data proves that the XAJ-EB is capable of reproducing runoff comparable to the original XAJ model. Additionally, RET simulated by XAJ-EB agreed well with moderate resolution imaging spectroradiometer (MODIS-retrieved LST, which further confirms that the model is able to simulate the mass–energy balance since LST reflects the interactions among various processes. The validation results prove that the XAJ-EB model has superior performance compared with the XAJ model and also extends its applicability.

How can we reduce phosphorus export from lowland polders? Implications from a sensitivity analysis of a coupled model.

Science.gov (United States)

Huang, Jiacong; Gao, Junfeng; Yan, Renhua

2016-08-15

Phosphorus (P) export from lowland polders has caused severe water pollution. Numerical models are an important resource that help water managers control P export. This study coupled three models, i.e., Phosphorus Dynamic model for Polders (PDP), Integrated Catchments model of Phosphorus dynamics (INCA-P) and Universal Soil Loss Equation (USLE), to describe the P dynamics in polders. Based on the coupled models and a dataset collected from Polder Jian in China, sensitivity analysis were carried out to analyze the cause-effect relationships between environmental factors and P export from Polder Jian. The sensitivity analysis results showed that P export from Polder Jian were strongly affected by air temperature, precipitation and fertilization. Proper fertilization management should be a strategic priority for reducing P export from Polder Jian. This study demonstrated the success of model coupling, and its application in investigating potential strategies to support pollution control in polder systems. Copyright © 2016. Published by Elsevier B.V.

Repetitive learning control of continuous chaotic systems

International Nuclear Information System (INIS)

Chen Maoyin; Shang Yun; Zhou Donghua

2004-01-01

Combining a shift method and the repetitive learning strategy, a repetitive learning controller is proposed to stabilize unstable periodic orbits (UPOs) within chaotic attractors in the sense of least mean square. If nonlinear parts in chaotic systems satisfy Lipschitz condition, the proposed controller can be simplified into a simple proportional repetitive learning controller

Learning-based identification and iterative learning control of direct-drive robots

NARCIS (Netherlands)

Bukkems, B.H.M.; Kostic, D.; Jager, de A.G.; Steinbuch, M.

2005-01-01

A combination of model-based and Iterative Learning Control is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the

Donor states in a semimagnetic Cd1 -xinMnxin Te /Cd1 -xoutMnxout Te Double Quantum Well

Science.gov (United States)

Kalpana, Panneer Selvam; Nithiananthi, Perumal; Jayakumar, Kalyanasundaram

2017-02-01

The theoretical investigation has been carried out on the binding energy of donor associated with the electrons confined in a Cd1 -xinMnxin Te /Cd1 -xoutMnxout Te Double Quantum Well (DQW) as a function of central barrier width for various well dimensions and impurity locations in the barrier and the well. The magnetic field can act as a tool to continuously change the interwell coupling inside this DQW systems and its effect on donor binding has also been studied. Moreover, the polaronic corrections, which is due to the strong exchange interaction between the magnetic moment of Mn2+ ion and the spin of the confined carrier, to the binding energy of the hydrogenic donor impurity has also been estimated with and without the application of magnetic field. The binding energy of the donor impurity is determined by solving the Schrodinger equation variationally in the effective mass approximation and the effect due to Bound Magnetic Polaron (BMP) is included using mean field theory with the modified Brillouin function. The results are reported and discussed.

Learning styles: The learning methods of air traffic control students

Science.gov (United States)

Jackson, Dontae L.

In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

Directory of Open Access Journals (Sweden)

Chao Lu

2016-08-01

Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

Characteristics of GHG flux from water-air interface along a reclaimed water intake area of the Chaobai River in Shunyi, Beijing

Science.gov (United States)

He, Baonan; He, Jiangtao; Wang, Jian; Li, Jie; Wang, Fei

2018-01-01

To understand greenhouse gas (GHG) flux in reclaimed water intake area impact on urban climate, 'static chamber' method was used to investigate the spatio-diurnal variations and the influence factors of GHG fluxes at water-air interface from Jian River to Chaobai River. Results showed that the average fluxes of CO2 from the Jian River and the Chaobai River were 73.46 mg(m2·h)-1 and -64.75 mg(m2·h)-1, respectively. CO2 was emitted the most in the Jian River, but it was absorbed from the atmosphere in the Chaobai River. Unary linear regression analyses demonstrated that Chlorophyll a (Chl a) and pH variation controlled the carbon source and sink from the Jian River to the Chaobai River. The diurnal variation of CO2 fluxes was higher at night than in the daytime in the Jian River, and it was the inverse in the Chaobai River, which highly correlated with dissociative CO2 and HCO3- transformation to CO32-. The average fluxes of CH4 from the Jian River and Chaobai River were 0.973 mg(m2·h)-1 and 5.556 mg(m2·h)-1, respectively, which increased along the water flow direction. Unary and multiple linear regression analyses demonstrated that Chl a and total organic carbon (TOC) controlled the increase of CH4 along the flow direction. The diurnal variation of CH4 fluxes was slightly higher in the daytime than at night due to the effect of water temperature.

Mosaic model for sensorimotor learning and control.

Science.gov (United States)

Haruno, M; Wolpert, D M; Kawato, M

2001-10-01

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

Learning to Control Advanced Life Support Systems

Science.gov (United States)

Subramanian, Devika

2004-01-01

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

Fuzzy self-learning control for magnetic servo system

Science.gov (United States)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

A fuzzy controller with a robust learning function

International Nuclear Information System (INIS)

Tanji, Jun-ichi; Kinoshita, Mitsuo

1987-01-01

A self-organizing fuzzy controller is able to use linguistic decision rules of control strategy and has a strong adaptive property by virture of its rule learning function. While a simple linguistic description of the learning algorithm first introduced by Procyk, et al. has much flexibility for applications to a wide range of different processes, its detailed formulation, in particular with control stability and learning process convergence, is not clear. In this paper, we describe the formulation of an analytical basis for a self-organizing fuzzy controller by using a method of model reference adaptive control systems (MRACS) for which stability in the adaptive loop is theoretically proven. A detailed formulation is described regarding performance evaluation and rule modification in the rule learning process of the controller. Furthermore, an improved learning algorithm using adaptive rule is proposed. An adaptive rule gives a modification coefficient for a rule change estimating the effect of disturbance occurrence in performance evaluation. The effect of introducing an adaptive rule to improve the learning convergency is described by using a simple iterative formulation. Simulation tests are presented for an application of the proposed self-organizing fuzzy controller to the pressure control system in a Boiling Water Reactor (BWR) plant. Results with the tests confirm the improved learning algorithm has strong convergent properties, even in a very disturbed environment. (author)

Quality evaluation of Guan-Xin-Ning injection based on fingerprint analysis and simultaneous separation and determination of seven bioactive constituents by capillary electrophoresis.

Science.gov (United States)

Xu, Liying; Chang, Ruimiao; Chen, Meng; Li, Lou; Huang, Yayun; Zhang, Hongfen; Chen, Anjia

2017-12-01

The purpose of this study was to develop a comprehensive, rapid and practical capillary electrophoresis (CE) method for quality control (QC) of Guan-Xin-Ning (GXN) injection based on fingerprint analysis and simultaneous separation and determination of seven constituents. In fingerprint analysis, a capillary zone electrophoresis (CZE) method with a running buffer of 30 mM borate solution (pH 9.3) was established. Meanwhile, ten batches of samples were used to establish the fingerprint electropherogram and 34 common peaks were obtained within 20 min. The RSD of relative migration times (RMT) and relative peak areas (RPA) were less than 5%. In order to further evaluate the quality of GXN injection, a micellar electrokinetic chromatography (MEKC) method was developed for simultaneous separation and determination of bioactive constituents. Seven components reached baseline separation with a running buffer containing 35 mM SDS and 45 mM borate solution (pH 9.3). A good linearity was obtained with correlation coefficients from 0.9906 to 0.9997. The LOD and LOQ ranged from 0.12 to 1.50 μg/mL and from 0.40 to 4.90 μg/mL, respectively. The recoveries ranged between 99.0 and 104.4%. Therefore, it was concluded that the proposed method can be used for full-scale quality analysis of GXN injection. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

Science.gov (United States)

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

El model de difusió de la llengua, la societat i la cultura xineses de l'Institut Confuci : El cas de la revista bilingüe xinès-espanyol

OpenAIRE

Arroyo Viguer, Germà

2016-01-01

Estudi del model de difusió de la llengua, la cultura i la societat xineses de l'Institut Confuci (IC). Analitza, a més, el cas de l'Institut Confuci de la Universitat de València (ICUV), així com la revista bimestral "Instituto Confucio", que edita en versió bilingüe xinès-espanyol l'ICUV. Estudio del modelo de difusión de la lengua, la cultura y la sociedad chinas del Instituto Confucio (IC). Analiza, además, el caso del Instituto Confucio de la Universidad de Valencia (ICUV), así como l...

Episodic reinforcement learning control approach for biped walking

Directory of Open Access Journals (Sweden)

Katić Duško

2012-01-01

Full Text Available This paper presents a hybrid dynamic control approach to the realization of humanoid biped robotic walk, focusing on the policy gradient episodic reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: a conventional computed torque controller and an episodic reinforcement learning controller. The reinforcement learning part includes fuzzy information about Zero-Moment- Point errors. Simulation tests using a medium-size 36-DOF humanoid robot MEXONE were performed to demonstrate the effectiveness of our method.

Investigation of Drive-Reinforcement Learning and Application of Learning to Flight Control

Science.gov (United States)

1993-08-01

WL-TR-93-1153 INVESTIGATION OF DRIVE-REINFORCEMEN% LEARNING AND APPLICATION OF LEARNING TO FLIGHT CONTROL AD-A277 442 WALTER L. BAKER (ED), STEPHEN ...OF LEARNING TO FUIGHT CONTROL PE 62204 ___ ___ ___ ___ __ ___ ___ ___ ___ ___ ___ __ PR 2003 6. AUTHOR(S) TA 05 WALTER L. BAKER (ED), STEPHEN C. ATKINS...34 Computers and Thought, E. A. Freigenbaum and J. Feldman (eds.), Mc- Graw Hill, New York, (1959). [19] Holland, J. H., "Escaping Brittleness: The Possibility

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

Science.gov (United States)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Methods for control over learning individual trajectory

Science.gov (United States)

Mitsel, A. A.; Cherniaeva, N. V.

2015-09-01

The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

Self-learning fuzzy logic controllers based on reinforcement

International Nuclear Information System (INIS)

Wang, Z.; Shao, S.; Ding, J.

1996-01-01

This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance

Impact on learning of an e-learning module on leukaemia: a randomised controlled trial.

Science.gov (United States)

Morgulis, Yuri; Kumar, Rakesh K; Lindeman, Robert; Velan, Gary M

2012-05-28

e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group's evaluation of the module was overwhelmingly positive. A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines.

Linear System Control Using Stochastic Learning Automata

Science.gov (United States)

Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.

1998-01-01

This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.

Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

Directory of Open Access Journals (Sweden)

Morgulis Yuri

2012-05-01

Full Text Available Abstract Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines.

Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

Science.gov (United States)

2012-01-01

Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines. PMID:22640463

Project-Based Learning in Programmable Logic Controller

Science.gov (United States)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

Directory of Open Access Journals (Sweden)

Wenjie Lou

2016-02-01

Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

The oral bioavailability, excretion and cytochrome P450 inhibition properties of epiberberine: an in vivo and in vitro evaluation

Directory of Open Access Journals (Sweden)

Chen N

2017-12-01

Full Text Available Ning Chen,1 Xiao-yan Yang,1,2 Chang-e Guo,1 Xin-ning Bi,1 Jian-hua Chen,1 Hong-ying Chen,1 Hong-pin Li,1 Hong-ying Lin,1 Yu-jie Zhang1 1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 2Nanjing Sanhome Pharmaceutical Co., Ltd., Nanjing, China Abstract: Epiberberine (EPI is a novel and potentially effective therapeutic and preventive agent for diabetes and cardiovascular disease. To evaluate its potential value for drug development, a specific, sensitive and robust high-performance liquid chromatography-tandem mass spectrometry assay for the determination of EPI in rat biological samples was established. This assay was used to study the pharmacokinetics, bioavailability and excretion of EPI in rats after oral administration. In addition, a cocktail method was used to compare the inhibition characteristics of EPI on cytochrome P450 (CYP450 isoforms in human liver microsomes (HLMs and rat liver microsomes (RLMs. The results demonstrated that EPI was rapidly absorbed and metabolized after oral administration (10, 54 or 81 mg/kg in rats, with Tmax of 0.37–0.42 h and T1/2 of 0.49–2.73 h. The Cmax and area under the curve values for EPI increased proportionally with the dose, and the oral absolute bioavailability was 14.46%. EPI was excreted mainly in bile and feces, and after its oral administration to rats, EPI was eliminated predominantly by the kidneys. A comparison of the current half-maximal inhibitory concentration and Ki values revealed that EPI demonstrated an obvious inhibitory effect on CYP2C9 and CYP2D6. Furthermore, its effect was stronger in HLM than in RLM, more likely to be a result of noncompetitive inhibition. Keywords: epiberberine, bioavailability, excretion kinetics, CYP inhibition type

Predictive Variable Gain Iterative Learning Control for PMSM

Directory of Open Access Journals (Sweden)

Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

Adaptive learning fuzzy control of a mobile robot

International Nuclear Information System (INIS)

Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

1989-11-01

In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

E-learning: controlling costs and increasing value.

Science.gov (United States)

Walsh, Kieran

2015-04-01

E-learning now accounts for a substantial proportion of medical education provision. This progress has required significant investment and this investment has in turn come under increasing scrutiny so that the costs of e-learning may be controlled and its returns maximised. There are multiple methods by which the costs of e-learning can be controlled and its returns maximised. This short paper reviews some of those methods that are likely to be most effective and that are likely to save costs without compromising quality. Methods might include accessing free or low-cost resources from elsewhere; create short learning resources that will work on multiple devices; using open source platforms to host content; using in-house faculty to create content; sharing resources between institutions; and promoting resources to ensure high usage. Whatever methods are used to control costs or increase value, it is most important to evaluate the impact of these methods.

The USAF Stability and Control Digital DATCOM. Volume I. Users Manual

Science.gov (United States)

1979-04-01

8217 Increment in lift due to thrust, 13 Eqn. 4.6.1-c 21 XBARP Y 13 22 DEUDA 3e /3a( 4.6.1 Eqn. 4.6.i-m 13: ~u 23-42 DCLNP (ACL)Np 4.6.1 Eqn. 4.6.1-I 13 43-62...T) 30 22 XBARIN XIN 30 23 XIN0CR IN/Cr 30 24 DEUDA ac /a 4.4.1 Eqn. 4.6.1-m 30u 25 EPSLON 30 26 ATJ (cT)j 4.6.1 Eqn. 4.6.1-a 30 27-46 DCLNJ (ACL)N

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

Science.gov (United States)

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Recent developments in learning control and system identification for robots and structures

Science.gov (United States)

Phan, M.; Juang, J.-N.; Longman, R. W.

1990-01-01

This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

Traditional Chinese Medicine ShenZhuGuanXin Granules Mitigate Cardiac Dysfunction and Promote Myocardium Angiogenesis in Myocardial Infarction Rats by Upregulating PECAM-1/CD31 and VEGF Expression

Directory of Open Access Journals (Sweden)

Dan-Ping Xu

2017-01-01

Full Text Available Background. Myocardial infarction (MI is the main cause of global mortality and morbidity despite the development of therapeutic approaches. ShenZhuGuanXin granules (SG have been shown to possess cardioprotective effects against coronary heart disease (CHD. However, little is known about its specific mechanism. The present study aimed to investigate the therapeutic effect of SG in cardiac dysfunction and to demonstrate whether SG can promote myocardium angiogenesis by establishing a rat model of myocardial infarction with left anterior descending ligating. Methods and Results. Three days after MI, rats were randomly divided into six groups: sham group (sham, MI group (MI, MI + low dose SG (SG-L group, MI + middle dose SG (SG-M group, MI + high dose SG (SG-H group, and MI + compound Danshen dropping pills (CDDP group as a positive control. Four weeks after administration, rats underwent hemodynamics and echocardiography study. Ventricle tissues were processed for histology and immunohistochemistry studies. Compared with MI group, SG treatment dose-dependently improved cardiac hemodynamic function, attenuated infarct size, increased microvessel density, and increased the expression of PECAM-1/CD31 and VEGF. Conclusions. SG dose-dependently improved cardiac hemodynamic function and attenuated infarct size by promoting angiogenesis through upregulating PECAM-1/CD31 and VEGF expression.

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

Science.gov (United States)

Yen, John; Wang, Haojin; Daugherity, Walter C.

1992-01-01

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

Learning from authoritarian teachers: Controlling the situation or controlling yourself can sustain motivation

Directory of Open Access Journals (Sweden)

Kathryn Everhart Chaffee

2014-01-01

Full Text Available Positive psychology encompasses the study of positive outcomes, optimal functioning, and resilience in difficult circumstances. In the context of language learning, positive outcomes include academic engagement, self-determined motivation, persistence in language learning, and eventually becoming a proficient user of the language. These questionnaire studies extend previous research by addressing how these positive outcomes can be achieved even in adverse circumstances. In Study 1, the primary and secondary control scales of interest were validated using 2468 students at a Canadian university. Study 2 examined the capacity of 100 Canadian language learners to adjust themselves to fit in with their environment, termed secondary control, and how it was related to their motivation for and engagement in language learning and their feelings of anxiety speaking in the classroom. Secondary control in the form of adjusting one’s attitude towards language learning challenges through positive reappraisals was positively associated with self-determined motivation, need satisfaction, and engagement. analyses, positive reappraisals were also found to buffer the negative effects of having a controlling instructor on students’ engagement and anxiety. These findings suggest that personal characteristics interact with the learning environment to allow students to function optimally in their language courses even when the teacher is controlling.

Application of a fuzzy control algorithm with improved learning speed to nuclear steam generator level control

International Nuclear Information System (INIS)

Park, Gee Yong; Seong, Poong Hyun

1994-01-01

In order to reduce the load of tuning works by trial-and-error for obtaining the best control performance of conventional fuzzy control algorithm, a fuzzy control algorithm with learning function is investigated in this work. This fuzzy control algorithm can make its rule base and tune the membership functions automatically by use of learning function which needs the data from the control actions of the plant operator or other controllers. Learning process in fuzzy control algorithm is to find the optimal values of parameters, which consist of the membership functions and the rule base, by gradient descent method. Learning speed of gradient descent is significantly improved in this work with the addition of modified momentum. This control algorithm is applied to the steam generator level control by computer simulations. The simulation results confirm the good performance of this control algorithm for level control and show that the fuzzy learning algorithm has the generalization capability for the relation of inputs and outputs and it also has the excellent capability of disturbance rejection

Measuring strategic control in artificial grammar learning.

Science.gov (United States)

Norman, Elisabeth; Price, Mark C; Jones, Emma

2011-12-01

In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning. Copyright © 2011 Elsevier Inc. All rights reserved.

Learning feedforward controller for a mobile robot vehicle

NARCIS (Netherlands)

Starrenburg, J.G.; Starrenburg, J.G.; van Luenen, W.T.C.; van Luenen, W.T.C.; Oelen, W.; Oelen, W.; van Amerongen, J.

1996-01-01

This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning

Statistical learning methods: Basics, control and performance

Energy Technology Data Exchange (ETDEWEB)

Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

2006-04-01

The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

Statistical learning methods: Basics, control and performance

International Nuclear Information System (INIS)

Zimmermann, J.

2006-01-01

The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

Approaches to Learning to Control Dynamic Uncertainty

Directory of Open Access Journals (Sweden)

Magda Osman

2015-10-01

Full Text Available In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains or exploit (maximizing their short term gains? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.

Concurrent Learning of Control in Multi agent Sequential Decision Tasks

Science.gov (United States)

2018-04-17

Concurrent Learning of Control in Multi-agent Sequential Decision Tasks The overall objective of this project was to develop multi-agent reinforcement... learning (MARL) approaches for intelligent agents to autonomously learn distributed control policies in decentral- ized partially observable... learning of policies in Dec-POMDPs, established performance bounds, evaluated these algorithms both theoretically and empirically, The views

Active controllers and the time duration to learn a task

Science.gov (United States)

Repperger, D. W.; Goodyear, C.

1986-01-01

An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Dong Yun Kim; Poong Hyun Seong; .

1997-01-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun

1997-02-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

Learning feedback and feedforward control in a mirror-reversed visual environment.

Science.gov (United States)

Kasuga, Shoko; Telgen, Sebastian; Ushiba, Junichi; Nozaki, Daichi; Diedrichsen, Jörn

2015-10-01

When we learn a novel task, the motor system needs to acquire both feedforward and feedback control. Currently, little is known about how the learning of these two mechanisms relate to each other. In the present study, we tested whether feedforward and feedback control need to be learned separately, or whether they are learned as common mechanism when a new control policy is acquired. Participants were trained to reach to two lateral and one central target in an environment with mirror (left-right)-reversed visual feedback. One group was allowed to make online movement corrections, whereas the other group only received visual information after the end of the movement. Learning of feedforward control was assessed by measuring the accuracy of the initial movement direction to lateral targets. Feedback control was measured in the responses to sudden visual perturbations of the cursor when reaching to the central target. Although feedforward control improved in both groups, it was significantly better when online corrections were not allowed. In contrast, feedback control only adaptively changed in participants who received online feedback and remained unchanged in the group without online corrections. Our findings suggest that when a new control policy is acquired, feedforward and feedback control are learned separately, and that there may be a trade-off in learning between feedback and feedforward controllers. Copyright © 2015 the American Physiological Society.

Online reinforcement learning control for aerospace systems

NARCIS (Netherlands)

Zhou, Y.

2018-01-01

Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical

Effect of Jian-Pi-Zhi-Dong Decoction on striatal glutamate and γ-aminobutyric acid levels detected using microdialysis in a rat model of Tourette syndrome

Directory of Open Access Journals (Sweden)

Zhang W

2016-05-01

Full Text Available Wen Zhang,1,* Li Wei,2,* Wenjing Yu,1 Xia Cui,1 Xiaofang Liu,2 Qian Wang,1 Sumei Wang2 1Department of Pediatrics, The Third Affiliated Hospital, 2Department of Pediatrics, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, People’s Republic of China *These authors contributed equally to this work Background: Jian-Pi-Zhi-Dong Decoction (JPZDD is a dedicated treatment of Tourette syndrome (TS. The balance of neurotransmitters in the cortico-striato-pallido-thalamo-cortical network is crucial to the occurrence of TS and related to its severity. This study evaluated the effect of JPZDD on glutamate (Glu and γ-aminobutyric acid (GABA and their receptors in a TS rat model.Materials and methods: Rats were divided into four groups (n=12 each. TS was induced in three of the groups by injecting them with 3,3'-iminodipropionitrile for 7 consecutive days. Two model groups were treated with tiapride (Tia or JPZDD, while the control and the remaining model group were gavaged with saline. Behavior was assessed by stereotypic score and autonomic activity. Striatal Glu and GABA contents were detected using microdialysis. Expressions of N-methyl-D-aspartate receptor 1 and GABAA receptor (GABAAR were observed using Western blot and real-time polymerase chain reaction.Results: Tia and JPZDD groups had decreased stereotypy compared with model rats; however, the JPZDD group showed a larger decrease in stereotypy than the Tia group at a 4-week time point. In a spontaneous activity test, the total distance of the JPZDD and Tia groups was significantly decreased compared with the model group. The Glu levels of the model group were higher than the control group and decreased with Tia or JPZDD treatment. The GABA level was higher in the model group than the control group. Expressions of GABAAR protein in the model group were higher than in the control group. Treatment with Tia or JPZDD reduced the expression of GABAAR protein. In the case of the m

Learn How to Control Asthma

Science.gov (United States)

... Guidelines Asthma & Community Health Learn How to Control Asthma Language: English (US) Español (Spanish) Arabic Chinese Français ... Is Asthma Treated? Select a Language What Is Asthma? Asthma is a disease that affects your lungs. ...

Cognitive Models for Learning to Control Dynamic Systems

National Research Council Canada - National Science Library

Eberhart, Russ; Hu, Xiaohui; Chen, Yaobin

2008-01-01

Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning...

Continuous residual reinforcement learning for traffic signal control optimization

NARCIS (Netherlands)

Aslani, Mohammad; Seipel, Stefan; Wiering, Marco

2018-01-01

Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on

Iterative learning control with sampled-data feedback for robot manipulators

Directory of Open Access Journals (Sweden)

Delchev Kamen

2014-09-01

Full Text Available This paper deals with the improvement of the stability of sampled-data (SD feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more, while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

Science.gov (United States)

Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

2008-08-01

In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

Science.gov (United States)

Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

2011-01-01

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

Game-Theoretic Learning in Distributed Control

KAUST Repository

Marden, Jason R.

2018-01-05

In distributed architecture control problems, there is a collection of interconnected decision-making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game-theoretic language, the incentives are defined through utility functions, and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.

Comparison of hyporheic flow and water quality in open and tree-covered banks downstream of Xin'an River dam, China

Science.gov (United States)

Liu, D.

2017-12-01

Plants, especially trees, in the riparian zone may have a significant impact on the flow rate, temperature and chemical properties of groundwater. A field study was conducted in the downstream bank of the Xin'an River dam, Zhejiang, China. In the field, two areas of about 20 meters apart were chosen, of which one was a open place and the other was covered with many orange trees. Comparison of hyporheic flow and water quality in the open and tree-covered banks were made by monitoring the water level, water temperature, water chemistry (March, 2015) along the cross sections perpendicular to the river. The analyses indicated that water level around the trees was relatively low in the day and high in the evening, thus changed the direction and magnitude of the natural groundwater flow velocity, totally strengthened the hyporheic exchange between the groundwater and river. The trees also changed the temperature distribution of the natural river bank, and induced the wider infiltration range of the low-temperature water. The temperature around the trees was relatively low in the day, yet it was high in the evening. Dissolved oxygen (DO) and electricity conductivity (EC) around the trees were significantly increased, yet the pH was almost unaffected.

Changing pulse-shape basis for molecular learning control

International Nuclear Information System (INIS)

Cardoza, David; Langhojer, Florian; Trallero-Herrero, Carlos; Weinacht, Thomas; Monti, Oliver L.A.

2004-01-01

We interpret the results of a molecular fragmentation learning control experiment. We show that in the case of a system where control can be related to the structure of the optimal pulse matching the vibrational dynamics of the molecule, a simple change of pulse-shape basis in which the learning algorithm performs the search can reduce the dimensionality of the search space to one or two degrees of freedom

A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

Directory of Open Access Journals (Sweden)

Lun Zhai

2014-01-01

Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

Science.gov (United States)

Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

2018-06-01

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

DEFF Research Database (Denmark)

Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

2013-01-01

Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

Autonomy supported, learner-controlled or system-controlled learning in hypermedia environments and the influence of academic self-regulation style

NARCIS (Netherlands)

Gorissen, Chantal; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

2012-01-01

This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style ( ASRS; i.e., a macro level of motivation) on learning. This

Autonomy supported, learner-controlled or system-controlled learning in hypermedia environments and the influence of academic self-regulation style

NARCIS (Netherlands)

Gorissen, Chantal J J; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

2015-01-01

This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This

Robust Control Methods for On-Line Statistical Learning

Directory of Open Access Journals (Sweden)

Capobianco Enrico

2001-01-01

Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.

Reinforcement learning for optimal control of low exergy buildings

International Nuclear Information System (INIS)

Yang, Lei; Nagy, Zoltan; Goffin, Philippe; Schlueter, Arno

2015-01-01

Highlights: • Implementation of reinforcement learning control for LowEx Building systems. • Learning allows adaptation to local environment without prior knowledge. • Presentation of reinforcement learning control for real-life applications. • Discussion of the applicability for real-life situations. - Abstract: Over a third of the anthropogenic greenhouse gas (GHG) emissions stem from cooling and heating buildings, due to their fossil fuel based operation. Low exergy building systems are a promising approach to reduce energy consumption as well as GHG emissions. They consists of renewable energy technologies, such as PV, PV/T and heat pumps. Since careful tuning of parameters is required, a manual setup may result in sub-optimal operation. A model predictive control approach is unnecessarily complex due to the required model identification. Therefore, in this work we present a reinforcement learning control (RLC) approach. The studied building consists of a PV/T array for solar heat and electricity generation, as well as geothermal heat pumps. We present RLC for the PV/T array, and the full building model. Two methods, Tabular Q-learning and Batch Q-learning with Memory Replay, are implemented with real building settings and actual weather conditions in a Matlab/Simulink framework. The performance is evaluated against standard rule-based control (RBC). We investigated different neural network structures and find that some outperformed RBC already during the learning phase. Overall, every RLC strategy for PV/T outperformed RBC by over 10% after the third year. Likewise, for the full building, RLC outperforms RBC in terms of meeting the heating demand, maintaining the optimal operation temperature and compensating more effectively for ground heat. This allows to reduce engineering costs associated with the setup of these systems, as well as decrease the return-of-invest period, both of which are necessary to create a sustainable, zero-emission building

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun; Seong, Poong Hyun

1996-01-01

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

Consensus-based distributed cooperative learning from closed-loop neural control systems.

Science.gov (United States)

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

Motor skill learning, retention, and control deficits in Parkinson's disease.

Directory of Open Access Journals (Sweden)

Lisa Katharina Pendt

Full Text Available Parkinson's disease, which affects the basal ganglia, is known to lead to various impairments of motor control. Since the basal ganglia have also been shown to be involved in learning processes, motor learning has frequently been investigated in this group of patients. However, results are still inconsistent, mainly due to skill levels and time scales of testing. To bridge across the time scale problem, the present study examined de novo skill learning over a long series of practice sessions that comprised early and late learning stages as well as retention. 19 non-demented, medicated, mild to moderate patients with Parkinson's disease and 19 healthy age and gender matched participants practiced a novel throwing task over five days in a virtual environment where timing of release was a critical element. Six patients and seven control participants came to an additional long-term retention testing after seven to nine months. Changes in task performance were analyzed by a method that differentiates between three components of motor learning prominent in different stages of learning: Tolerance, Noise and Covariation. In addition, kinematic analysis related the influence of skill levels as affected by the specific motor control deficits in Parkinson patients to the process of learning. As a result, patients showed similar learning in early and late stages compared to the control subjects. Differences occurred in short-term retention tests; patients' performance constantly decreased after breaks arising from poorer release timing. However, patients were able to overcome the initial timing problems within the course of each practice session and could further improve their throwing performance. Thus, results demonstrate the intact ability to learn a novel motor skill in non-demented, medicated patients with Parkinson's disease and indicate confounding effects of motor control deficits on retention performance.

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

Directory of Open Access Journals (Sweden)

Erdal Kayacan

2017-01-01

Full Text Available A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN is used in parallel with a conventional P (proportional controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs. The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

Science.gov (United States)

Yuan, Chengzhi; Licht, Stephen; He, Haibo

2017-09-26

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Measuring strategic control in implicit learning: how and why?

Science.gov (United States)

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

Learning control for batch thermal sterilization of canned foods.

Science.gov (United States)

Syafiie, S; Tadeo, F; Villafin, M; Alonso, A A

2011-01-01

A control technique based on Reinforcement Learning is proposed for the thermal sterilization of canned foods. The proposed controller has the objective of ensuring a given degree of sterilization during Heating (by providing a minimum temperature inside the cans during a given time) and then a smooth Cooling, avoiding sudden pressure variations. For this, three automatic control valves are manipulated by the controller: a valve that regulates the admission of steam during Heating, and a valve that regulate the admission of air, together with a bleeder valve, during Cooling. As dynamical models of this kind of processes are too complex and involve many uncertainties, controllers based on learning are proposed. Thus, based on the control objectives and the constraints on input and output variables, the proposed controllers learn the most adequate control actions by looking up a certain matrix that contains the state-action mapping, starting from a preselected state-action space. This state-action matrix is constantly updated based on the performance obtained with the applied control actions. Experimental results at laboratory scale show the advantages of the proposed technique for this kind of processes. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

An iterative learning controller for nonholonomic mobile robots

International Nuclear Information System (INIS)

Oriolo, G.; Panzieri, S.; Ulivi, G.

1998-01-01

The authors present an iterative learning controller that applies to nonholonomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained-form. The learning algorithm exploits the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method

Can we (control) Engineer the degree learning process?

Science.gov (United States)

White, A. S.; Censlive, M.; Neilsen, D.

2014-07-01

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

Can we (control) Engineer the degree learning process?

International Nuclear Information System (INIS)

White, A S; Censlive, M; Neilsen, D

2014-01-01

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process

Autonomy Supported, Learner-Controlled or System-Controlled Learning in Hypermedia Environments and the Influence of Academic Self-Regulation Style

Science.gov (United States)

Gorissen, Chantal J. J.; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

2015-01-01

This study focuses on learning in three different hypermedia environments that either support autonomous learning, learner-controlled learning or system-controlled learning and explores the mediating role of academic self-regulation style (ASRS; i.e. a macro level of motivation) on learning. This research was performed to gain more insight in the…

Systems control with generalized probabilistic fuzzy-reinforcement learning

NARCIS (Netherlands)

Hinojosa, J.; Nefti, S.; Kaymak, U.

2011-01-01

Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be

Efficient model learning methods for actor-critic control.

Science.gov (United States)

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Learning to push and learning to move: The adaptive control of contact forces

Directory of Open Access Journals (Sweden)

Maura eCasadio

2015-11-01

Full Text Available To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in compatible pairs connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e. when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and

Sensitivity-based self-learning fuzzy logic control for a servo system

NARCIS (Netherlands)

Balenovic, M.

1998-01-01

Describes an experimental verification of a self-learning fuzzy logic controller (SLFLC). The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been

Influence of polyethylene glycol coating on biodistribution and toxicity of nanoscale graphene oxide in mice after intravenous injection

Directory of Open Access Journals (Sweden)

Li B

2014-10-01

Full Text Available Bo Li,1,2 Xiao-Yong Zhang,1 Jian-Zhong Yang,1 Yu-Jie Zhang,1 Wen-Xin Li,1 Chun-Hai Fan,1 Qing Huang1 1Laboratory of Physical Biology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 2Department of Human Anatomy, College of Basic Medical Sciences, Jilin University, Changchun, People’s Republic of China Abstract: In this study, we assessed the in vivo behavior and toxicology of nanoscale graphene oxide (NGO in mice after intravenous injection. The influence of a polyethylene glycol (PEG coating on the distribution and toxicity of the NGO was also investigated. The results show that NGO is mainly retained in the liver, lung, and spleen. Retention in the lung is partially due to NGO aggregation. The PEG coating reduces the retention of NGO in the liver, lung, and spleen and promotes the clearance of NGO from these organs, but NGO and NGO-PEG are still present after 3 months. The PEG coating effectively reduces the early weight loss caused by NGO and alleviates NGO-induced acute tissue injuries, which can include damage to the liver, lung, and kidney, and chronic hepatic and lung fibrosis. Keywords: graphene oxide, biodistribution, toxicity, polyethylene glycol

Cuprous oxide nanoparticles selectively induce apoptosis of tumor cells

Directory of Open Access Journals (Sweden)

Wang Y

2012-05-01

Full Text Available Ye Wang,1,2,* Xiao-Yuan Zi,1,* Juan Su,1 Hong-Xia Zhang,1 Xin-Rong Zhang,3 Hai-Ying Zhu,1 Jian-Xiu Li,1 Meng Yin,3 Feng Yang,3 Yi-Ping Hu,11Department of Cell Biology, 2School of Clinical Medicine, 3Department of Pharmaceuticals, Second Military Medical University, Shanghai, People's Republic of China*Authors contributed equally.Abstract: In the rapid development of nanoscience and nanotechnology, many researchers have discovered that metal oxide nanoparticles have very useful pharmacological effects. Cuprous oxide nanoparticles (CONPs can selectively induce apoptosis and suppress the proliferation of tumor cells, showing great potential as a clinical cancer therapy. Treatment with CONPs caused a G1/G0 cell cycle arrest in tumor cells. Furthermore, CONPs enclosed in vesicles entered, or were taken up by mitochondria, which damaged their membranes, thereby inducing apoptosis. CONPs can also produce reactive oxygen species (ROS and initiate lipid peroxidation of the liposomal membrane, thereby regulating many signaling pathways and influencing the vital movements of cells. Our results demonstrate that CONPs have selective cytotoxicity towards tumor cells, and indicate that CONPs might be a potential nanomedicine for cancer therapy.Keywords: nanomedicine, selective cytotoxicity, apoptosis, cell cycle arrest, mitochondrion-targeted nanomaterials

Traffic light control by multiagent reinforcement learning systems

NARCIS (Netherlands)

Bakker, B.; Whiteson, S.; Kester, L.; Groen, F.C.A.; Babuška, R.; Groen, F.C.A.

2010-01-01

Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of

Traffic Light Control by Multiagent Reinforcement Learning Systems

NARCIS (Netherlands)

Bakker, B.; Whiteson, S.; Kester, L.J.H.M.; Groen, F.C.A.

2010-01-01

Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced traffic congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of

Complexity control in statistical learning

Indian Academy of Sciences (India)

Then we describe how the method of regularization is used to control complexity in learning. We discuss two examples of regularization, one in which the function space used is finite dimensional, and another in which it is a reproducing kernel Hilbert space. Our exposition follows the formulation of Cucker and Smale.

A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

Science.gov (United States)

Mendez, Juan A.; Gonzalez, Evelio J.

2010-01-01

As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

On equivalence classes in iterative learning control

NARCIS (Netherlands)

Verwoerd, M.H.A.; Meinsma, Gjerrit; de Vries, Theodorus J.A.

2003-01-01

This paper advocates a new approach to study the relation between causal iterative learning control (ILC) and conventional feedback control. Central to this approach is the introduction of the set of admissible pairs (of operators) defined with respect to a family of iterations. Considered are two

Learning-based position control of a closed-kinematic chain robot end-effector

Science.gov (United States)

Nguyen, Charles C.; Zhou, Zhen-Lei

1990-01-01

A trajectory control scheme whose design is based on learning theory, for a six-degree-of-freedom (DOF) robot end-effector built to study robotic assembly of NASA hardwares in space is presented. The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system is designed using the concept of linearization about a selected operating point, and the method of pole placement so that the closed-loop linearized system is stabilized. The learning control scheme consisting of PD-type learning controllers, provides additional inputs to improve the end-effector performance after each trial. Experimental studies performed on a 2 DOF end-effector built at CUA, for three tracking cases show that actual trajectories approach desired trajectories as the number of trials increases. The tracking errors are substantially reduced after only five trials.

Machine learning control taming nonlinear dynamics and turbulence

CERN Document Server

Duriez, Thomas; Noack, Bernd R

2017-01-01

This is the first book on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading r...

Reclassification of Serpens flexibilis Hespell 1977 as Pseudomonas flexibilis comb. nov., with Pseudomonas tuomuerensis Xin et al. 2009 as a later heterotypic synonym.

Science.gov (United States)

Shin, Su-Kyoung; Hwang, Chung Yeon; Cho, Yong-Joon; Yi, Hana

2015-12-01

Serpens flexibilis was proposed in 1977 and approved in 1980 without the 16S rRNA gene sequence information. The sequence of S. flexibilis became available in 2010, after the publication of Pseudomonas tuomuerensis in 2009. Our preliminary phylogenetic analyses indicated that these two strains share high sequence similarity and therefore showed strong potential to be united into a single species. To clarify the taxonomic status of the two species, a polyphasic taxonomy study was conducted including whole genome sequencing. The value of average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) between the genome sequences of S. flexibilis ATCC 29606(T) and P. tuomuerensis JCM 14085(T) were 98.1% and 89.0%, respectively. The phenotypic and chemotaxonomic properties including enzymatic activities, substrate utilization profiles, and fatty acids, supported that the two taxa have no pronounced difference and should thus constitute a single species. Therefore, we propose to transfer Serpens flexibilis Hespell 1977 to the genus Pseudomonas as Pseudomonas flexibilis comb. nov. (type strain=ATCC 29606(T)), with Pseudomonas tuomuerensis Xin et al. 2009 as a later heterotypic synonym of Pseudomonas flexibilis. Copyright © 2015 Elsevier GmbH. All rights reserved.

Multi Car Elevator Control by using Learning Automaton

Science.gov (United States)

Shiraishi, Kazuaki; Hamagami, Tomoki; Hirata, Hironori

We study an adaptive control technique for multi car elevators (MCEs) by adopting learning automatons (LAs.) The MCE is a high performance and a near-future elevator system with multi shafts and multi cars. A strong point of the system is that realizing a large carrying capacity in small shaft area. However, since the operation is too complicated, realizing an efficient MCE control is difficult for top-down approaches. For example, “bunching up together" is one of the typical phenomenon in a simple traffic environment like the MCE. Furthermore, an adapting to varying environment in configuration requirement is a serious issue in a real elevator service. In order to resolve these issues, having an autonomous behavior is required to the control system of each car in MCE system, so that the learning automaton, as the solutions for this requirement, is supposed to be appropriate for the simple traffic control. First, we assign a stochastic automaton (SA) to each car control system. Then, each SA varies its stochastic behavior distributions for adapting to environment in which its policy is evaluated with each passenger waiting times. That is LA which learns the environment autonomously. Using the LA based control technique, the MCE operation efficiency is evaluated through simulation experiments. Results show the technique enables reducing waiting times efficiently, and we confirm the system can adapt to the dynamic environment.

Amygdala subsystems and control of feeding behavior by learned cues.

Science.gov (United States)

Petrovich, Gorica D; Gallagher, Michela

2003-04-01

A combination of behavioral studies and a neural systems analysis approach has proven fruitful in defining the role of the amygdala complex and associated circuits in fear conditioning. The evidence presented in this chapter suggests that this approach is also informative in the study of other adaptive functions that involve the amygdala. In this chapter we present a novel model to study learning in an appetitive context. Furthermore, we demonstrate that long-recognized connections between the amygdala and the hypothalamus play a crucial role in allowing learning to modulate feeding behavior. In the first part we describe a behavioral model for motivational learning. In this model a cue that acquires motivational properties through pairings with food delivery when an animal is hungry can override satiety and promote eating in sated rats. Next, we present evidence that a specific amygdala subsystem (basolateral area) is responsible for allowing such learned cues to control eating (override satiety and promote eating in sated rats). We also show that basolateral amygdala mediates these actions via connectivity with the lateral hypothalamus. Lastly, we present evidence that the amygdalohypothalamic system is specific for the control of eating by learned motivational cues, as it does not mediate another function that depends on intact basolateral amygdala, namely, the ability of a conditioned cue to support new learning based on its acquired value. Knowledge about neural systems through which food-associated cues specifically control feeding behavior provides a defined model for the study of learning. In addition, this model may be informative for understanding mechanisms of maladaptive aspects of learned control of eating that contribute to eating disorders and more moderate forms of overeating.

Study on the Correlation between Gene Expression and Enzyme Activity of Seven Key Enzymes and Ginsenoside Content in Ginseng in Over Time in Ji'an, China.

Science.gov (United States)

Yin, Juxin; Zhang, Daihui; Zhuang, Jianjian; Huang, Yi; Mu, Ying; Lv, Shaowu

2017-12-11

Panax ginseng is a traditional medicine. Fresh ginseng is one of the most important industries related to ginseng development, and fresh ginseng of varying ages has different medicinal properties. Previous research has not systematically reported the correlation between changes in key enzyme activity with changes in ginsenoside content in fresh ginseng over time. In this study, for the first time, we use ginseng samples of varying ages in Ji'an and systematically reported the changes in the activity of seven key enzymes (HMGR, FPS, SS, SE, DS, CYP450, and GT). We investigated the content of ginsenoside and gene expression of these key enzymes. Ginsenoside content was measured using HPLC. HPLC, GC-MS, and LC-MS were combined to measure the enzyme activity of the key enzymes. Quantitative PCR was used in the investigation of gene expression. By analyzing the correlation between the enzyme activity and the transcription level of the key enzymes with ginsenoside content, we found that DS and GT enzyme activities are significantly correlated with the ginsenoside content in different ages of ginseng. Our findings might provide a new strategy to discriminate between ginseng of different years. Meanwhile, this research provides important information for the in-depth study of ginsenoside biosynthesis.

Disturbed spontaneous brain activity pattern in patients with primary angle-closure glaucoma using amplitude of low-frequency fluctuation: a fMRI study

Directory of Open Access Journals (Sweden)

Huang X

2015-07-01

Full Text Available Xin Huang,1,* Yu-Lin Zhong,1,* Xian-Jun Zeng,2 Fuqing Zhou,2 Xin-Hua Liu,1 Pei-Hong Hu,1 Chong-Gang Pei,1 Yi Shao,1 Xi-Jian Dai21Department of Ophthalmology, 2Department of Radiology, The First Affiliated Hospital of Nanchang University, Nangchang, Jiangxi, People’s Republic of China*These authors contributed equally to this workObjective: The aim of this study is to use amplitude of low-frequency fluctuation (ALFF as a method to explore the local features of spontaneous brain activity in patients with primary angle -closure glaucoma (PACG and ALFFs relationship with the behavioral performances.Methods: A total of twenty one patients with PACG (eight males and 13 females, and twenty one healthy subjects (nine males and twelve females closely matched in age, sex, and education, each underwent a resting-state functional magnetic resonance imaging scan. The ALFF method was used to assess the local features of spontaneous brain activity. The correlation analysis was used to explore the relationships between the observed mean ALFF signal values of the different areas in PACG patients and the thickness of the retinal nerve fiber layer (RNFL. Results: Compared with the healthy subjects, patients with PACG had significant lower ALFF areas in the left precentral gyrus, bilateral middle frontal gyrus, bilateral superior frontal gyrus, right precuneus, and right angular gyrus, and higher areas in the right precentral gyrus. In the PACG group, there were significant negative correlations between the mean ALFF signal value of the right middle frontal gyrus and the left mean RNFL thickness (r=-0.487, P=0.033, and between the mean ALFF signal value of the left middle frontal gyrus and the right mean RNFL thickness (r=-0.504, P=0.020. Conclusion: PACG mainly involved in the dysfunction in the frontal lobe, which may reflect the underlying pathologic mechanism of PACG.Keywords: angle-closure glaucoma, amplitude of low-frequency fluctuation, functional

Discrete Learning Control with Application to Hydraulic Actuators

DEFF Research Database (Denmark)

Andersen, Torben Ole; Pedersen, Henrik Clemmensen; Hansen, Michael R.

2015-01-01

In this paper the robustness of a class of learning control algorithms to state disturbances, output noise, and errors in initial conditions is studied. We present a simple learning algorithm and exhibit, via a concise proof, bounds on the asymptotic trajectory errors for the learned input...... and the corresponding state and output trajectories. Furthermore, these bounds are continuous functions of the bounds on the initial condition errors, state disturbance, and output noise, and the bounds are zero in the absence of these disturbances....

Understanding Self-Controlled Motor Learning Protocols through the Self-Determination Theory.

Science.gov (United States)

Sanli, Elizabeth A; Patterson, Jae T; Bray, Steven R; Lee, Timothy D

2012-01-01

The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

Understanding self-controlled motor learning protocols through the self determination theory

Directory of Open Access Journals (Sweden)

Elizabeth Ann Sanli

2013-01-01

Full Text Available The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT. Three micro theories within the macro theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory & Organismic Integration Theory are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

Land cover of oases and forest in XinJiang, China retrieved from ASTER data

Science.gov (United States)

Buhe, Aosier; Tsuchiya, K.; Kaneko, M.; Ohtaishi, N.; Halik, Mahmut

ASTER aboard NASA’s satellite Terra is a high-resolution multispectral radiometer of 14 bands. The spatial resolution is 15 m in VNIR, 30 m in SWIR and 90 m in TIR spectra, respectively. With the data observed with ASTER, the land cover classification is produced for the Tarim Diversifolious Poplar Protection Area along the Tarim River in the northern Tarim Basin (Taklamakan Desert) in XinJiang, China. The classification of the vegetation (plants) in the arid and semiarid regions using remote-sensing technology is very difficult. Because the cause has low vegetable cover density and the influence of reflection from background soil is large. ASTER data are effective in studying the spectrum characteristics of land cover in arid and semiarid regions. The sensor has several bands in the shortwave infrared wavelength region that is designed for exploration of earth resources and study of the arid and semiarid region natural environment. However, we are not clear combination of which band is the most effective in research of the arid region like the Taklamakan desert in the data of 14 bands of ASTER. The optimum index factor (OIF), based on total variance within bands and correlation coefficient between bands, is a statistical approach to rank all possible three-band combinations. In the process of analyzing the data, the pixel sizes of all the data are converted (layer stacking and re-sampling) into consistent same size of 15 m. The three-band composite with the largest OIF value will have most information (as measured by variance) with the least amount of duplication (as measured by correlation). We used the OIF technique to rank all three-band combinations of ASTER original 14-band data over Tarim River Poplar Protection Area. Our study indicates that RGB color overlay using atmospheric corrected ASTER original bands 2, 3 (VNIR), and 6 (SWIR) has the highest OIF. When NDVI is considered as one ASTER band, highest OIF will have by carrying out bands 3 (VNIR), 4

A neural fuzzy controller learning by fuzzy error propagation

Science.gov (United States)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Generalized projective synchronization of chaotic systems via adaptive learning control

International Nuclear Information System (INIS)

Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang

2010-01-01

In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

Science.gov (United States)

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Exploring Learner Autonomy: Language Learning Locus of Control in Multilinguals

Science.gov (United States)

Peek, Ron

2016-01-01

By using data from an online language learning beliefs survey (n?=?841), defining language learning experience in terms of participants' multilingualism, and using a domain-specific language learning locus of control (LLLOC) instrument, this article examines whether more experienced language learners can also be seen as more autonomous language…

Learning and Control Model of the Arm for Loading

Science.gov (United States)

Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

Biomimetic approach to tacit learning based on compound control.

Science.gov (United States)

Shimoda, Shingo; Kimura, Hidenori

2010-02-01

The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.

Measuring strategic control in implicit learning: how and why?

OpenAIRE

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledg...

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Fixed Point Learning Based Intelligent Traffic Control System

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Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

Impaired learning of punishments in Parkinson's disease with and without impulse control disorder.

Science.gov (United States)

Leplow, Bernd; Sepke, Maria; Schönfeld, Robby; Pohl, Johannes; Oelsner, Henriette; Latzko, Lea; Ebersbach, Georg

2017-02-01

To document specific learning mechanisms in patients with Parkinson's disease (PD) with and without impulse control disorder (ICD). Thirty-two PD patients receiving dopamine replacement therapy (DRT) were investigated. Sixteen were diagnosed with ICD (ICD + ) and 16 PD patients matched for levodopa equivalence dosage, and DRT duration and severity of disease did not show impulsive behavior (non-ICD). Short-term learning of inhibitory control was assessed by an experimental procedure which was intended to mimic everyday life. Correct inhibition especially, had to be learned without reward (passive avoidance), and the failure to inhibit a response was punished (punishment learning). Results were compared to 16 healthy controls (HC) matched for age and sex. In ICD + patients within-session learning of non-rewarded inhibition was at chance levels. Whereas healthy controls rapidly developed behavioral inhibition, non-ICD patients were also significantly impaired compared to HC, but gradually developed some degree of control. Both patient groups showed significantly decreased learning if the failure to withhold a response was punished. PD patients receiving DRT show impaired ability to acquire both punishment learning and passive avoidance learning, irrespective of whether or not ICD was developed. In ICD + PD patients, behavioral inhibition is nearly absent. Results demonstrate that by means of subtle learning paradigms it is possible to identify PD-DRT patients who show subtle alterations of punishment learning. This may be a behavioral measure for the identification of PD patients who are prone to develop ICD if DRT is continued.

Learning-based controller for biotechnology processing, and method of using

Science.gov (United States)

Johnson, John A.; Stoner, Daphne L.; Larsen, Eric D.; Miller, Karen S.; Tolle, Charles R.

2004-09-14

The present invention relates to process control where some of the controllable parameters are difficult or impossible to characterize. The present invention relates to process control in biotechnology of such systems, but not limited to. Additionally, the present invention relates to process control in biotechnology minerals processing. In the inventive method, an application of the present invention manipulates a minerals bioprocess to find local exterma (maxima or minima) for selected output variables/process goals by using a learning-based controller for bioprocess oxidation of minerals during hydrometallurgical processing. The learning-based controller operates with or without human supervision and works to find processor optima without previously defined optima due to the non-characterized nature of the process being manipulated.

Variations in Volatile Oil Yield and Composition of "Xin-yi" (Magnolia biondii Pamp. Flower Buds) at Different Growth Stages.

Science.gov (United States)

Hu, Mingli; Bai, Mei; Ye, Wei; Wang, Yaling; Wu, Hong

2018-06-01

Dried flower buds of Magnolia biondii Pamp. are the main ingredient in "Xin-yi" in China, and the volatile oils of M. biondii flower buds are the principal medicinal component. Gas chromatographymass spectrometry (GC-MS) and microscopic techniques were employed to detect the volatile yields of M. biondii flowers at various growth stages. The volatile oil yields of M. biondii flowers differed significantly at different growth stages and were closely related to flower dry weight, oil cell density and degree of oil accumulation. In February 2016, flower buds had the highest dry weight, the maximum percentage of oil cells at the oil saturation stage and the highest density of oil cells, which coincided with the highest oil yield. In March 2016, flower buds had a lower dry weight, a higher percentage of oil cells at the oil-degrading stage and the lowest oil cell density, resulting in decreased oil yields. The total amounts of the major medicinal components in the M. biondii flower also showed regular changes at different growth stages. In January and February of 2016, M. biondii flowers had a higher dry weight, volatile oil yield and total content of medicinal ingredients, which was the best time for harvesting high-quality medicinal components. Our study reveals that volatile oil content and chemical composition are closely related to the growth stage of M. biondii flower buds. The results provide a scientific morphology and composition index for evaluating the medicinal value and harvesting of high-quality M. biondii medicinal herbs.

The control of tonic pain by active relief learning

Science.gov (United States)

Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W

2018-01-01

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty (‘associability’) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. PMID:29482716

The control of tonic pain by active relief learning.

Science.gov (United States)

Zhang, Suyi; Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W; Seymour, Ben

2018-02-27

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. © 2018, Zhang et al.

Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

Science.gov (United States)

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

Automatic learning rate adjustment for self-supervising autonomous robot control

Science.gov (United States)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

E-Learning System for Learning Virtual Circuit Making with a Microcontroller and Programming to Control a Robot

Science.gov (United States)

Takemura, Atsushi

2015-01-01

This paper proposes a novel e-Learning system for learning electronic circuit making and programming a microcontroller to control a robot. The proposed e-Learning system comprises a virtual-circuit-making function for the construction of circuits with a versatile, Arduino microcontroller and an educational system that can simulate behaviors of…

Application of parsimonious learning feedforward control to mechatronic systems

NARCIS (Netherlands)

de Vries, Theodorus J.A.; Velthuis, W.J.R.; Idema, L.J.

2001-01-01

For motion control, learning feedforward controllers (LFFCs) should be applied when accurate process modelling is difficult. When controlling such processes with LFFCs in the form of multidimensional B-spline networks, large network sizes and a poor generalising ability may result, known as the

Tongxinluo (Tong xin luo or Tong-xin-luo) capsule for unstable angina pectoris.

Science.gov (United States)

Wu, Taixiang; Harrison, R A; Chen, Xiaoyan; Ni, Juan; Zhou, Likun; Qiao, Jieqi; Wang, Qin; Wei, Jiafu; Xin, Duan; Zheng, Jie

2006-10-18

Tongxinluo capsule is a medicine consisting of traditional Chinese herbs and insects used for cardiovascular diseases in China and some other Asian countries. To date the evidence of its effect has not previously been subject to systematic review, making it difficult to derive robust conclusions about its actual benefits, and indeed, possible harms. To assess systematically the effects of tongxinluo capsule in people with unstable angina pectoris. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) on The Cochrane Library, Issue 4 2004, MEDLINE, EMBASE, Chinese Biomedical Database, China National Knowledge Infrastructure, Japana Centra Revuo Medicina (all 1995 to 2005). We also handsearched the relevant Chinese journals, checked with manufacturers and registers of ongoing studies. Randomised trials comparing either tongxinluo capsule only or standard treatment plus tongxinluo capsule with standard treatment or other anti-angina pectoris drugs, placebo or no intervention. Two authors identified relevant studies for the review independently and went on to abstract data, and assess trial quality. Authors of included studies were contacted to obtain further information as required. 18 short term follow-up trials involving 1413 people were included. The studies did not provide strong support of a benefit of tongxinluo for reducing the combined outcome of acute myocardial infarction, angioplasty (PTCA) coronary artery bypass graft (CABG) and sudden death or all-cause mortality (RR 0.42, 95% CI 0.07 to 2.59, P=0.35; RR 0.33, 95% CI 0.01to 7.78, P=0.49, respectively). Tongxinluo reduced the frequency of acute angina attacks (WMD -1.20, 95%CI -1.38 to -1.02, P<0.00001 and RR -2.36, 95%CI -2.53 to -2.18, P<0.00001, respectively), improved ECG (RR 1.31, 95% CI 1.08 to 1.57, P=0.005) and angina symptoms (RR 1.21, 95% CI 1.06 to 1.40; P=0.007). Tongxinluo in combination with routine angina therapy appears to reduce the risk of subsequent AMI, PTCA or CABG

INTELLIGENT FRACTIONAL ORDER ITERATIVE LEARNING CONTROL USING FEEDBACK LINEARIZATION FOR A SINGLE-LINK ROBOT

Directory of Open Access Journals (Sweden)

Iman Ghasemi

2017-05-01

Full Text Available In this paper, iterative learning control (ILC is combined with an optimal fractional order derivative (BBO-Da-type ILC and optimal fractional and proportional-derivative (BBO-PDa-type ILC. In the update law of Arimoto's derivative iterative learning control, a first order derivative of tracking error signal is used. In the proposed method, fractional order derivative of the error signal is stated in term of 'sa' where  to update iterative learning control law. Two types of fractional order iterative learning control namely PDa-type ILC and Da-type ILC are gained for different value of a. In order to improve the performance of closed-loop control system, coefficients of both  and  learning law i.e. proportional , derivative  and  are optimized using Biogeography-Based optimization algorithm (BBO. Outcome of the simulation results are compared with those of the conventional fractional order iterative learning control to verify effectiveness of BBO-Da-type ILC and BBO-PDa-type ILC

Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

Directory of Open Access Journals (Sweden)

Takashi Watanabe

2010-01-01

Full Text Available Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES system using powered orthotic devices. In this paper, Feedback Error Learning (FEL controller for FES (FEL-FES controller was examined using an inverse statics model (ISM with an inverse dynamics model (IDM to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

Indicators for successful learning in air traffic control training

NARCIS (Netherlands)

Van Meeuwen, Ludo; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen; De Bock, Jeano; Kirschner, Paul A.

2011-01-01

Van Meeuwen, L. W., Brand-Gruwel, S., Van Merriënboer, J. J. G., De Bock, J. J. P. R., & Kirschner, P. A. (2010, August). Indicators for successful learning in air traffic control training. Paper presented at the 5th EARLI SIG 14 Learning and Professional Development Conference. Munich, Germany.

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

Science.gov (United States)

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

Traditional Chinese medicine for stable angina pectoris via TCM pattern differentiation and TCM mechanism: study protocol of a randomized controlled trial.

Science.gov (United States)

Zhang, Zhe; Zhang, Fan; Wang, Yang; Du, Yi; Zhang, Huiyong; Kong, Dezhao; Liu, Yue; Yang, Guanlin

2014-10-30

Stable angina pectoris is experienced as trans-sternal or retro-sternal pressure or pain that may radiate to the left arm, neck or back. Although available evidence relating to its effectiveness and mechanism are weak, traditional Chinese medicine is used as an alternative therapy for stable angina pectoris. We report a protocol of a randomized controlled trial using traditional Chinese medicine to investigate the effectiveness, mechanism and safety for patients with stable angina pectoris. This is a north-east Chinese, multi-center, multi-blinded, placebo-controlled and superiority randomized trail. A total of 240 patients with stable angina pectoris will be randomly assigned to three groups: two treatment groups and a control group. The treatment groups will receive Chinese herbal medicine consisting of Yi-Qi-Jian-Pi and Qu-Tan-Hua-Zhuo granule and Yi-Qi-Jian-Pi and Qu-Tan-Hua-Yu granule, respectively, and conventional medicine. The control group will receive placebo medicine in addition to conventional medicine. All 3 groups will undergo a 12-week treatment and 2-week follow-up. Four visits in sum will be scheduled for each subject: 1 visit each in week 0, week 4, week 12 and week 14. The primary outcomes include: the frequency of angina pectoris attack; the dosage of nitroglycerin; body limited dimension of Seattle Angina Questionnaire. The secondary outcomes include: except for the body limited dimension of SAQ, traditional Chinese medicine pattern questionnaire and so on. Therapeutic mechanism outcomes, safety outcomes and endpoint outcomes will be also assessed. The primary aim of this trial is to develop a standard protocol to utilize high-quality EBM evidence for assessing the effectiveness and safety of SAP via TCM pattern differentiation as well as exploring the efficacy mechanism and regulation with the molecular biology and systems biology. ChiCTR-TRC-13003608, registered 18 June 2013.

Procedural Memory: Computer Learning in Control Subjects and in Parkinson’s Disease Patients

Directory of Open Access Journals (Sweden)

C. Thomas-Antérion

1996-01-01

Full Text Available We used perceptual motor tasks involving the learning of mouse control by looking at a Macintosh computer screen. We studied 90 control subjects aged between sixteen and seventy-five years. There was a significant time difference between the scales of age but improvement was the same for all subjects. We also studied 24 patients with Parkinson's disease (PD. We observed an influence of age and also of educational levels. The PD patients had difficulties of learning in all tests but they did not show differences in time when compared to the control group in the first learning session (Student's t-test. They learned two or four and a half times less well than the control group. In the first test, they had some difficulty in initiating the procedure and learned eight times less well than the control group. Performances seemed to be heterogeneous: patients with only tremor (seven and patients without treatment (five performed better than others but learned less. Success in procedural tasks for the PD group seemed to depend on the capacity to initiate the response and not on the development of an accurate strategy. Many questions still remain unanswered, and we have to study different kinds of implicit memory tasks to differentiate performance in control and basal ganglia groups.

Short-term memory, executive control, and children's route learning

OpenAIRE

Purser, H. R.; Farran, E. K.; Courbois, Y.; Lemahieu, A.; Mellier, D.; Sockeel, P.; Blades, M.

2012-01-01

The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and visuospatial long-term memory; the route-learning task was conducted using a maze in a virtual environment. In contrast to previous research, correlation...

A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

2017-01-01

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space...... and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models...

Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control.

Science.gov (United States)

Kawato, Mitsuo; Kuroda, Shinya; Schweighofer, Nicolas

2011-10-01

The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning. Copyright © 2011 Elsevier Ltd. All rights reserved.

Stable schizophrenia patients learn equally well as age-matched controls and better than elderly controls in two sensorimotor Rotary Pursuit tasks

Directory of Open Access Journals (Sweden)

Livia J. De Picker

2014-11-01

Full Text Available Objective: To compare sensorimotor performance and learning in stable schizophrenia patients, healthy age- and sex-matched controls and elderly controls on two variations of the Rotary Pursuit: Circle Pursuit (true motor learning and Figure Pursuit (motor and sequence learning.Method: In the Circle Pursuit a target circle, rotating with increasing speed along a predictable circular path on the computer screen, must be followed by a cursor controlled by a pen on a writing tablet. In the eight-trial Figure Pursuit, subjects learn to draw a complex figure by pursuing the target circle that moves along an invisible trajectory between and around several goals. Tasks were administered thrice (day 1, day 2, day 7 to 30 patients with stable schizophrenia (S, 30 healthy age- and sex-matched controls (C and 30 elderly participants (>65y; E and recorded with a digitizing tablet and pressure-sensitive pen. The outcome measure accuracy (% of time that cursor is within the target was used to assess performance.Results: We observed significant group differences in accuracy, both in Circle and Figure Pursuit tasks (Elearning effects were found in each group. Learning curves were similar in Circle Pursuit but differed between groups in Figure Pursuit. When corrected for group differences in starting level, the learning gains over the three sessions of schizophrenia patients and age-matched controls were equal and both were larger than those of the elderly controls. Conclusion: Despite the reduced sensorimotor performance that was found in the schizophrenia patients their sensorimotor learning seems to be preserved. The relevance of this finding for the evaluation of procedural learning in schizophrenia is discussed. The better performance and learning rate of the patients compared to the elderly controls was unexpected and deserves further study.

Organization of students’ knowledge control in the process of distance learning

Directory of Open Access Journals (Sweden)

Miklashevich N. V.

2016-07-01

Full Text Available the article observes the main problems of organizing and carrying out the educational diagnosis in distance learning. Studying different approaches to monitoring showed that such control types as routine control and self-control are more efficient and effective. There is a difficulty of carrying out the control in distance learning: the need for accurate identification of the learner's personality. Despite existing technologies and recent developments in this area, the problem of preventing the test results from falsification is not fully resolved. According to the authors, the basic type of routine control when educating distantly remains the student obligatory attendance.

Design of intelligent comfort control system with human learning and minimum power control strategies

International Nuclear Information System (INIS)

Liang, J.; Du, R.

2008-01-01

This paper presents the design of an intelligent comfort control system by combining the human learning and minimum power control strategies for the heating, ventilating and air conditioning (HVAC) system. In the system, the predicted mean vote (PMV) is adopted as the control objective to improve indoor comfort level by considering six comfort related variables, whilst a direct neural network controller is designed to overcome the nonlinear feature of the PMV calculation for better performance. To achieve the highest comfort level for the specific user, a human learning strategy is designed to tune the user's comfort zone, and then, a VAV and minimum power control strategy is proposed to minimize the energy consumption further. In order to validate the system design, a series of computer simulations are performed based on a derived HVAC and thermal space model. The simulation results confirm the design of the intelligent comfort control system. In comparison to the conventional temperature controller, this system can provide a higher comfort level and better system performance, so it has great potential for HVAC applications in the future

Stable schizophrenia patients learn equally well as age-matched controls and better than elderly controls in two sensorimotor rotary pursuit tasks

NARCIS (Netherlands)

Picker, L.J. De; Cornelis, C.; Hulstijn, W.; Dumont, G.J.H.; Fransen, E.; Timmers, M.; Janssens, L.; Morrens, M.; Sabbe, B.G.C.

2014-01-01

Objective: To compare sensorimotor performance and learning in stable schizophrenia patients, healthy age- and sex-matched controls and elderly controls on two variations of the rotary pursuit: circle pursuit (true motor learning) and figure pursuit (motor and sequence learning). Method: In the

Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

2016-01-01

Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

Optimal critic learning for robot control in time-varying environments.

Science.gov (United States)

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Theories and control models and motor learning: clinical applications in neuro-rehabilitation.

Science.gov (United States)

Cano-de-la-Cuerda, R; Molero-Sánchez, A; Carratalá-Tejada, M; Alguacil-Diego, I M; Molina-Rueda, F; Miangolarra-Page, J C; Torricelli, D

2015-01-01

In recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neuro-rehabilitation. The major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neuro-rehabilitation. At present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neuro-rehabilitation. Copyright © 2011 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

Measuring strategic control in implicit learning: How and why?

Directory of Open Access Journals (Sweden)

Elisabeth eNorman

2015-09-01

Full Text Available Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in SRT learning (Destrebecqz & Cleeremans, 2001; Goschke, 1998 and 2-grammar classification tasks in AGL (Dienes, Altmann, Kwan, & Goode, 1995; Norman, Price, & Jones, 2011. Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge is consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodologial and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

Short-Term Memory, Executive Control, and Children's Route Learning

Science.gov (United States)

Purser, Harry R. M.; Farran, Emily K.; Courbois, Yannick; Lemahieu, Axelle; Mellier, Daniel; Sockeel, Pascal; Blades, Mark

2012-01-01

The aim of this study was to investigate route-learning ability in 67 children aged 5 to 11 years and to relate route-learning performance to the components of Baddeley's model of working memory. Children carried out tasks that included measures of verbal and visuospatial short-term memory and executive control and also measures of verbal and…

Density control in ITER: an iterative learning control and robust control approach

Science.gov (United States)

Ravensbergen, T.; de Vries, P. C.; Felici, F.; Blanken, T. C.; Nouailletas, R.; Zabeo, L.

2018-01-01

Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

Machine Learning for Flapping Wing Flight Control

NARCIS (Netherlands)

Goedhart, Menno; van Kampen, E.; Armanini, S.F.; de Visser, C.C.; Chu, Q.

2018-01-01

Flight control of Flapping Wing Micro Air Vehicles is challenging, because of their complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a

Effects of Locus of Control and Learner-Control on Web-Based Language Learning

Science.gov (United States)

Chang, Mei-Mei; Ho, Chiung-Mei

2009-01-01

The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

Directory of Open Access Journals (Sweden)

Fangzheng Xue

2014-05-01

Full Text Available With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.

A Plant Control Technology Using Reinforcement Learning Method with Automatic Reward Adjustment

Science.gov (United States)

Eguchi, Toru; Sekiai, Takaaki; Yamada, Akihiro; Shimizu, Satoru; Fukai, Masayuki

A control technology using Reinforcement Learning (RL) and Radial Basis Function (RBF) Network has been developed to reduce environmental load substances exhausted from power and industrial plants. This technology consists of the statistic model using RBF Network, which estimates characteristics of plants with respect to environmental load substances, and RL agent, which learns the control logic for the plants using the statistic model. In this technology, it is necessary to design an appropriate reward function given to the agent immediately according to operation conditions and control goals to control plants flexibly. Therefore, we propose an automatic reward adjusting method of RL for plant control. This method adjusts the reward function automatically using information of the statistic model obtained in its learning process. In the simulations, it is confirmed that the proposed method can adjust the reward function adaptively for several test functions, and executes robust control toward the thermal power plant considering the change of operation conditions and control goals.

SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

Directory of Open Access Journals (Sweden)

Hossam Gabbar

2016-03-01

Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

Extracting quantum dynamics from genetic learning algorithms through principal control analysis

International Nuclear Information System (INIS)

White, J L; Pearson, B J; Bucksbaum, P H

2004-01-01

Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)

Associative visual learning by tethered bees in a controlled visual environment.

Science.gov (United States)

Buatois, Alexis; Pichot, Cécile; Schultheiss, Patrick; Sandoz, Jean-Christophe; Lazzari, Claudio R; Chittka, Lars; Avarguès-Weber, Aurore; Giurfa, Martin

2017-10-10

Free-flying honeybees exhibit remarkable cognitive capacities but the neural underpinnings of these capacities cannot be studied in flying insects. Conversely, immobilized bees are accessible to neurobiological investigation but display poor visual learning. To overcome this limitation, we aimed at establishing a controlled visual environment in which tethered bees walking on a spherical treadmill learn to discriminate visual stimuli video projected in front of them. Freely flying bees trained to walk into a miniature Y-maze displaying these stimuli in a dark environment learned the visual discrimination efficiently when one of them (CS+) was paired with sucrose and the other with quinine solution (CS-). Adapting this discrimination to the treadmill paradigm with a tethered, walking bee was successful as bees exhibited robust discrimination and preferred the CS+ to the CS- after training. As learning was better in the maze, movement freedom, active vision and behavioral context might be important for visual learning. The nature of the punishment associated with the CS- also affects learning as quinine and distilled water enhanced the proportion of learners. Thus, visual learning is amenable to a controlled environment in which tethered bees learn visual stimuli, a result that is important for future neurobiological studies in virtual reality.

Self-Control of Haptic Assistance for Motor Learning: Influences of Frequency and Opinion of Utility

Science.gov (United States)

Williams, Camille K.; Tseung, Victrine; Carnahan, Heather

2017-01-01

Studies of self-controlled practice have shown benefits when learners controlled feedback schedule, use of assistive devices and task difficulty, with benefits attributed to information processing and motivational advantages of self-control. Although haptic assistance serves as feedback, aids task performance and modifies task difficulty, researchers have yet to explore whether self-control over haptic assistance could be beneficial for learning. We explored whether self-control of haptic assistance would be beneficial for learning a tracing task. Self-controlled participants selected practice blocks on which they would receive haptic assistance, while participants in a yoked group received haptic assistance on blocks determined by a matched self-controlled participant. We inferred learning from performance on retention tests without haptic assistance. From qualitative analysis of open-ended questions related to rationales for/experiences of the haptic assistance that was chosen/provided, themes emerged regarding participants’ views of the utility of haptic assistance for performance and learning. Results showed that learning was directly impacted by the frequency of haptic assistance for self-controlled participants only and view of haptic assistance. Furthermore, self-controlled participants’ views were significantly associated with their requested haptic assistance frequency. We discuss these findings as further support for the beneficial role of self-controlled practice for motor learning. PMID:29255438

Remote-online case-based learning: A comparison of remote-online and face-to-face, case-based learning - a randomized controlled trial.

Science.gov (United States)

Nicklen, Peter; Keating, Jenny L; Paynter, Sophie; Storr, Michael; Maloney, Stephen

2016-01-01

Case-based learning (CBL) is an educational approach where students work in small, collaborative groups to solve problems. Computer assisted learning (CAL) is the implementation of computer technology in education. The purpose of this study was to compare the effects of a remote-online CBL (RO-CBL) with traditional face-to-face CBL on learning the outcomes of undergraduate physiotherapy students. Participants were randomized to either the control (face-to-face CBL) or to the CAL intervention (RO-CBL). The entire 3rd year physiotherapy cohort (n = 41) at Monash University, Victoria, Australia, were invited to participate in the randomized controlled trial. Outcomes included a postintervention multiple-choice test evaluating the knowledge gained from the CBL, a self-assessment of learning based on examinable learning objectives and student satisfaction with the CBL. In addition, a focus group was conducted investigating perceptions and responses to the online format. Thirty-eight students (control n = 19, intervention n = 19) participated in two CBL sessions and completed the outcome assessments. CBL median scores for the postintervention multiple-choice test were comparable (Wilcoxon rank sum P = 0.61) (median/10 [range] intervention group: 9 [8-10] control group: 10 [7-10]). Of the 15 examinable learning objectives, eight were significantly in favor of the control group, suggesting a greater perceived depth of learning. Eighty-four percent of students (16/19) disagreed with the statement "I enjoyed the method of CBL delivery." Key themes identified from the focus group included risks associated with the implementation of, challenges of communicating in, and flexibility offered, by web-based programs. RO-CBL appears to provide students with a comparable learning experience to traditional CBL. Procedural and infrastructure factors need to be addressed in future studies to counter student dissatisfaction and decreased perceived depth of learning.

Optimal control in microgrid using multi-agent reinforcement learning.

Science.gov (United States)

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

The Effectiveness of E-Learning Systems: A Review of the Empirical Literature on Learner Control

Science.gov (United States)

Sorgenfrei, Christian; Smolnik, Stefan

2016-01-01

E-learning systems are considerably changing education and organizational training. With the advancement of online-based learning systems, learner control over the instructional process has emerged as a decisive factor in technology-based forms of learning. However, conceptual work on the role of learner control in e-learning has not advanced…

Learned Helplessness: A Theory for the Age of Personal Control.

Science.gov (United States)

Peterson, Christopher; And Others

Experiences with uncontrollable events may lead to the expectation that future events will elude control, resulting in disruptions in motivation, emotion, and learning. This text explores this phenomenon, termed learned helplessness, tracking it from its discovery to its entrenchment in the psychological canon. The volume summarizes and integrates…

Human-level control through deep reinforcement learning

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning.

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

A Modified Chinese Herbal Decoction (Kai-Xin-San Promotes NGF-Induced Neuronal Differentiation in PC12 Cells via Up-Regulating Trk A Signaling

Directory of Open Access Journals (Sweden)

Lu Yan

2017-12-01

Full Text Available Kai-Xin-San (KXS, a Chinese herbal decoction, has been applied to medical care of depression for thousands of years. It is composed of two functional paired-herbs: Ginseng Radix et Rhizoma (GR-Polygalae Radix (PR and Acori Tatarinowii Rhizoma (ATR-Poria (PO. The compatibility of the paired-herbs has been frequently changed to meet the criteria of syndrome differentiation and treatment variation. Currently, a modified KXS (namely KXS2012 was prepared by optimizing the combinations of GR-PR and ATR-PO: the new herbal formula was shown to be very effective in animal studies. However, the cellular mechanism of KXS2012 against depression has not been fully investigated. Here, the study on KXS2012-induced neuronal differentiation in cultured PC12 cells was analyzed. In PC12 cultures, single application of KXS2012 showed no effect on the neuronal differentiation, but which showed robust effects in potentiating nerve growth factor (NGF-induced neurite outgrowth and neurofilament expression. The potentiating effect of KXS2012 was mediated through NGF receptor, tropomyosin receptor kinase (Trk A: because the receptor expression and activity was markedly up-regulated in the presence of KXS2012, and the potentiating effect was blocked by k252a, an inhibitor of Trk A. Our current results in cell cultures fully support the therapeutic efficacy of KXS2012 against depression.

Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

Science.gov (United States)

Doll, Bradley B; Jacobs, W Jake; Sanfey, Alan G; Frank, Michael J

2009-11-24

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

A statistical learning strategy for closed-loop control of fluid flows

Science.gov (United States)

Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

2016-12-01

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

Science.gov (United States)

Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

2014-08-01

This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

Locus of control and learning strategies as predictors of academic ...

African Journals Online (AJOL)

The aim of the research was to determine the relationships which exist between academic success, learning strategies and locus of control. In order to achieve this aim a small-scale quantitative study, utilising two inventories, was done. The first measuring instrument is the Learning and Study Strategies Inventory, which is ...

Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

OpenAIRE

Watanabe, Takashi; Sugi, Yoshihiro

2010-01-01

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests ...

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Science.gov (United States)

Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

2016-01-01

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

A new subspace based approach to iterative learning control

NARCIS (Netherlands)

Nijsse, G.; Verhaegen, M.; Doelman, N.J.

2001-01-01

This paper1 presents an iterative learning control (ILC) procedure based on an inverse model of the plant under control. Our first contribution is that we formulate the inversion procedure as a Kalman smoothing problem: based on a compact state space model of a possibly non-minimum phase system,

Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

OpenAIRE

Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

2015-01-01

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

A Standardized Chinese Herbal Decoction, Kai-Xin-San, Restores Decreased Levels of Neurotransmitters and Neurotrophic Factors in the Brain of Chronic Stress-Induced Depressive Rats

Science.gov (United States)

Zhu, Kevin Yue; Mao, Qing-Qiu; Ip, Siu-Po; Choi, Roy Chi-Yan; Dong, Tina Ting-Xia; Lau, David Tai-Wai; Tsim, Karl Wah-Keung

2012-01-01

Kai-xin-san (KXS), a Chinese herbal decoction being prescribed by Sun Simiao in Beiji Qianjin Yaofang about 1400 years ago, contains Ginseng Radix et Rhizoma, Polygalae Radix, Acori tatarinowii Rhizoma, and Poria. KXS has been used to treat stress-related psychiatric disease with the symptoms of depression and forgetfulness in ancient China until today. However, the mechanism of its antidepression action is still unknown. Here, the chronic mild-stress-(CMS-) induced depressive rats were applied in exploring the action mechanisms of KXS treatment. Daily intragastric administration of KXS for four weeks significantly alleviated the CMS-induced depressive symptoms displayed by enhanced sucrose consumption. In addition, the expressions of those molecular bio-markers relating to depression in rat brains were altered by the treatment of KXS. These KXS-regulated brain biomarkers included: (i) the levels of dopamine, norepinephrine, and serotonin (ii) the transcript levels of proteins relating to neurotransmitter metabolism; (iii) the transcript levels of neurotrophic factors and their receptors. The results suggested that the anti-depressant-like action of KXS might be mediated by an increase of neurotransmitters and expression of neurotrophic factors and its corresponding receptors in the brain. Thus, KXS could serve as alternative medicine, or health food supplement, for patients suffering from depression. PMID:22973399

A Standardized Chinese Herbal Decoction, Kai-Xin-San, Restores Decreased Levels of Neurotransmitters and Neurotrophic Factors in the Brain of Chronic Stress-Induced Depressive Rats

Directory of Open Access Journals (Sweden)

Kevin Yue Zhu

2012-01-01

Full Text Available Kai-xin-san (KXS, a Chinese herbal decoction being prescribed by Sun Simiao in Beiji Qianjin Yaofang about 1400 years ago, contains Ginseng Radix et Rhizoma, Polygalae Radix, Acori tatarinowii Rhizoma, and Poria. KXS has been used to treat stress-related psychiatric disease with the symptoms of depression and forgetfulness in ancient China until today. However, the mechanism of its antidepression action is still unknown. Here, the chronic mild-stress-(CMS- induced depressive rats were applied in exploring the action mechanisms of KXS treatment. Daily intragastric administration of KXS for four weeks significantly alleviated the CMS-induced depressive symptoms displayed by enhanced sucrose consumption. In addition, the expressions of those molecular bio-markers relating to depression in rat brains were altered by the treatment of KXS. These KXS-regulated brain biomarkers included: (i the levels of dopamine, norepinephrine, and serotonin (ii the transcript levels of proteins relating to neurotransmitter metabolism; (iii the transcript levels of neurotrophic factors and their receptors. The results suggested that the anti-depressant-like action of KXS might be mediated by an increase of neurotransmitters and expression of neurotrophic factors and its corresponding receptors in the brain. Thus, KXS could serve as alternative medicine, or health food supplement, for patients suffering from depression.

Identification of the chemical constituents of Chinese medicine Yi-Xin-Shu capsule by molecular feature orientated precursor ion selection and tandem mass spectrometry structure elucidation.

Science.gov (United States)

Wang, Hong-ping; Chen, Chang; Liu, Yan; Yang, Hong-Jun; Wu, Hong-Wei; Xiao, Hong-Bin

2015-11-01

The incomplete identification of the chemical components of traditional Chinese medicinal formula has been one of the bottlenecks in the modernization of traditional Chinese medicine. Tandem mass spectrometry has been widely used for the identification of chemical substances. Current automatic tandem mass spectrometry acquisition, where precursor ions were selected according to their signal intensity, encounters a drawback in chemical substances identification when samples contain many overlapping signals. Compounds in minor or trace amounts could not be identified because most tandem mass spectrometry information was lost. Herein, a molecular feature orientated precursor ion selection and tandem mass spectrometry structure elucidation method for complex Chinese medicine chemical constituent analysis was developed. The precursor ions were selected according to their two-dimensional characteristics of retention times and mass-to-charge ratio ranges from herbal compounds, so that all precursor ions from herbal compounds were included and more minor chemical constituents in Chinese medicine were identified. Compared to the conventional automatic tandem mass spectrometry setups, the approach is novel and can overcome the drawback for chemical substances identification. As an example, 276 compounds from the Chinese Medicine of Yi-Xin-Shu capsule were identified. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

Science.gov (United States)

Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

2006-01-01

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

Which Management Control System principles and aspects are relevant when deploying a learning machine?

OpenAIRE

Martin, Johansson; Mikael, Göthager

2017-01-01

How shall a business adapt its management control systems when learning machines enter the arena? Will the control system continue to focus on humans aspects and continue to consider a learning machine to be an automation tool as any other historically programmed computer? Learning machines introduces productivity capabilities that achieve very high levels of efficiency and quality. A learning machine can sort through large amounts of data and make conclusions difficult by a human mind. Howev...

A control center design revisited: learning from users’ appropriation

DEFF Research Database (Denmark)

Souza da Conceição, Carolina; Cordeiro, Cláudia

2014-01-01

This paper aims to present the lessons learned during a control center design project by revisiting another control center from the same company designed two and a half years before by the same project team. In light of the experience with the first project and its analysis, the designers and res...

San-Huang-Xie-Xin-Tang Prevents Rat Hearts from Ischemia/Reperfusion-Induced Apoptosis through eNOS and MAPK Pathways

Directory of Open Access Journals (Sweden)

Shu-Fen Liou

2011-01-01

Full Text Available San-Huang-Xie-Xin-Tang (SHXT is a traditional Chinese medication consisting of three herbs, namely Coptidis rhizome, Scutellariae radix and Rhei rhizome. This study aimed to examine the cardioprotective effects of SHXT in a rat model of acute myocardial apoptosis induced by ischemia/reperfusion (I/R. Vehicle (intravenous saline or SHXT (intravenous or oral was administered prior to I/R (occlusion of left coronary artery for 45 min followed by reperfusion for 2 h. In the vehicle group, myocardial I/R caused myocardial infarction with increased plasma cardiac enzymes, severe arrhythmia and mortality. Myocardial apoptosis was induced by I/R as evidenced by DNA ladder and Bcl-2/Bax ratio. In the SHXT group, we found that SHXT significantly reduced plasma levels of cardiac enzymes, arrhythmia scores (from 5 ± 1 to 2 ± 1, P<.01 and mortality rate (from 53 to 0%, P<.01. In addition, pretreatment with intravenous SHXT reduced the infarct size dose-dependently when compared with the vehicle group (10 mg kg−1: 14.0 ± 0.2 versus 44.5 ± 5.0%, and 30 mg kg−1: 6.2 ± 1.2% versus 44.5 ± 5.0%, both P<.01. Similarly, oral administration of SHXT reduced the infarct size dose-dependently. Furthermore, SHXT markedly decreased the apoptosis induced by I/R with increased Bcl-2/Bax ratio. Finally, we found that SHXT counteracted the I/R-induced downstream signaling, resulting in increased myocardial eNOS expression and plasma nitrite, and decreased activation of ERK1/2, p38 and JNK. These data suggest that SHXT has cardioprotective effects against I/R-induced apoptosis, and that these effects are mediated, at least in part, by eNOS and MAPK pathways.

Tunnel Ventilation Control Using Reinforcement Learning Methodology

Science.gov (United States)

Chu, Baeksuk; Kim, Dongnam; Hong, Daehie; Park, Jooyoung; Chung, Jin Taek; Kim, Tae-Hyung

The main purpose of tunnel ventilation system is to maintain CO pollutant concentration and VI (visibility index) under an adequate level to provide drivers with comfortable and safe driving environment. Moreover, it is necessary to minimize power consumption used to operate ventilation system. To achieve the objectives, the control algorithm used in this research is reinforcement learning (RL) method. RL is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment. The goal of RL is to maximize a reward which is an evaluative feedback from the environment. In the process of constructing the reward of the tunnel ventilation system, two objectives listed above are included, that is, maintaining an adequate level of pollutants and minimizing power consumption. RL algorithm based on actor-critic architecture and gradient-following algorithm is adopted to the tunnel ventilation system. The simulations results performed with real data collected from existing tunnel ventilation system and real experimental verification are provided in this paper. It is confirmed that with the suggested controller, the pollutant level inside the tunnel was well maintained under allowable limit and the performance of energy consumption was improved compared to conventional control scheme.

ISO learning approximates a solution to the inverse-controller problem in an unsupervised behavioral paradigm.

Science.gov (United States)

Porr, Bernd; von Ferber, Christian; Wörgötter, Florentin

2003-04-01

In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control.

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

International Nuclear Information System (INIS)

Wei Qing-Lai; Song Rui-Zhuo; Xiao Wen-Dong; Sun Qiu-Ye

2015-01-01

This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. (paper)

Digital control for nuclear reactors - lessons learned

International Nuclear Information System (INIS)

Bernard, J.A.; Aviles, B.N.; Lanning, D.D.

1992-01-01

Lessons learned during the course of the now decade-old MIT program on the digital control of nuclear reactors are enumerated. Relative to controller structure, these include the importance of a separate safety system, the need for signal validation, the role of supervisory algorithms, the significance of command validation, and the relevance of automated reasoning. Relative to controller implementation, these include the value of nodal methods to the creation of real-time reactor physics and thermal hydraulic models, the advantages to be gained from the use of real-time system models, and the importance of a multi-tiered structure to the simultaneous achievement of supervisory, global, and local control. Block diagrams are presented of proposed controllers and selected experimental and simulation-study results are shown. In addition, a history is given of the MIT program on reactor digital control

Video Demo: Deep Reinforcement Learning for Coordination in Traffic Light Control

NARCIS (Netherlands)

van der Pol, E.; Oliehoek, F.A.; Bosse, T.; Bredeweg, B.

2016-01-01

This video demonstration contrasts two approaches to coordination in traffic light control using reinforcement learning: earlier work, based on a deconstruction of the state space into a linear combination of vehicle states, and our own approach based on the Deep Q-learning algorithm.

Can Working Memory and Inhibitory Control Predict Second Language Learning in the Classroom?

Directory of Open Access Journals (Sweden)

Jared A. Linck

2015-10-01

Full Text Available The role of executive functioning in second language (L2 aptitude remains unclear. Whereas some studies report a relationship between working memory (WM and L2 learning, others have argued against this association. Similarly, being bilingual appears to benefit inhibitory control, and individual differences in inhibitory control are related to online L2 processing. The current longitudinal study examines whether these two components of executive functioning predict learning gains in an L2 classroom context using a pretest/posttest design. We assessed 25 university students in language courses, who completed measures of WM and inhibitory control. They also completed a proficiency measure at the beginning and end of a semester and reported their grade point average (GPA. WM was positively related to L2 proficiency and learning, but inhibitory control was not. These results support the notion that WM is an important component of L2 aptitude, particularly for predicting the early stages of L2 classroom learning.

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

Science.gov (United States)

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Ryotaro Kamimura

2014-01-01

Full Text Available We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

Directory of Open Access Journals (Sweden)

Guofeng Tong

2014-04-01

Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

Locus of control and online learning

Directory of Open Access Journals (Sweden)

Suretha Esterhuysen

2004-10-01

Full Text Available The integration of online learning in university courses is considered to be both inevitable and necessary. Thus there is an increasing need to raise awareness among educators and course designers about the critical issues impacting on online learning. The aim of this study, therefore, was to assess the differences between two groups of first-year Business Sciences learners (online and conventional learners in terms of biographic and demographic characteristics and locus of control. The study population consisted of 586 first-year learners of whom 185 completed the Locus of Control Inventory (LCI. The results show that the two groups of learners do not differ statistically significantly from each other with respect to locus of control. The findings and their implications are also discussed. Opsomming Die integrasie van aanlyn-leer in universiteitskursusse word beskou as sowel onafwendbaar as noodsaaklik. Daar is dus ’n toenemende behoefte om bewustheid onder opvoedkundiges en kursusontwerpers te kweek oor die kritiese aspekte wat ’n impak op aanlyn-leer het (Morgan, 1996. Daarom was die doel van hierdie ondersoek om die verskille tussen twee groepe eerstejaarleerders in Bestuurs- en Ekonomiese Wetenskap (aanlyn en konvensionele leerders te bepaal ten opsigte van biografiese en demografiese eienskappe en lokus van beheer. Die populasie het bestaan uit 586 eerstejaarleerders waarvan 185 die Lokus van Beheer Vraelys voltooi het. Die resultate toon dat die twee groepe leerders nie statisties beduidend van mekaar verskil het met betrekking tot lokus van beheer nie. Die bevindinge en implikasies word ook bespreek.

Online learning versus blended learning of clinical supervisee skills with pre-registration nursing students: A randomised controlled trial.

Science.gov (United States)

McCutcheon, Karen; O'Halloran, Peter; Lohan, Maria

2018-06-01

The World Health Organisation amongst others recognises the need for the introduction of clinical supervision education in health professional education as a central strategy for improving patient safety and patient care. Online and blended learning methods are growing exponentially in use in higher education and the systematic evaluation of these methods will aid understanding of how best to teach clinical supervision. The purpose of this study was to test whether undergraduate nursing students who received clinical supervisee skills training via a blended learning approach would score higher in terms of motivation and attitudes towards clinical supervision, knowledge of clinical supervision and satisfaction of learning method, when compared to those students who received an online only teaching approach. A post-test-only randomised controlled trial. Participants were a total of 122 pre-registration nurses enrolled at one United Kingdom university, randomly assigned to the online learning control group (n = 60) or the blended learning intervention group (n = 62). The blended learning intervention group participated in a face-to-face tutorial and the online clinical supervisee skills training app. The online learning control group participated in an online discussion forum and the same online clinical supervisee skills training app. The outcome measures were motivation and attitudes using the modified Manchester Clinical Supervision Scale, knowledge using a 10 point Multiple Choice Questionnaire and satisfaction using a university training evaluation tool. Statistical analysis was performed using independent t-tests to compare the differences between the means of the control group and the intervention group. Thematic analysis was used to analyse responses to open-ended questions. All three of our study hypotheses were confirmed. Participants who received clinical supervisee skills training via a blended learning approach scored higher in terms of motivation

Non-Hebbian learning implementation in light-controlled resistive memory devices.

Science.gov (United States)

Ungureanu, Mariana; Stoliar, Pablo; Llopis, Roger; Casanova, Fèlix; Hueso, Luis E

2012-01-01

Non-Hebbian learning is often encountered in different bio-organisms. In these processes, the strength of a synapse connecting two neurons is controlled not only by the signals exchanged between the neurons, but also by an additional factor external to the synaptic structure. Here we show the implementation of non-Hebbian learning in a single solid-state resistive memory device. The output of our device is controlled not only by the applied voltages, but also by the illumination conditions under which it operates. We demonstrate that our metal/oxide/semiconductor device learns more efficiently at higher applied voltages but also when light, an external parameter, is present during the information writing steps. Conversely, memory erasing is more efficiently at higher applied voltages and in the dark. Translating neuronal activity into simple solid-state devices could provide a deeper understanding of complex brain processes and give insight into non-binary computing possibilities.

Developing Learning Tool of Control System Engineering Using Matrix Laboratory Software Oriented on Industrial Needs

Science.gov (United States)

Isnur Haryudo, Subuh; Imam Agung, Achmad; Firmansyah, Rifqi

2018-04-01

The purpose of this research is to develop learning media of control technique using Matrix Laboratory software with industry requirement approach. Learning media serves as a tool for creating a better and effective teaching and learning situation because it can accelerate the learning process in order to enhance the quality of learning. Control Techniques using Matrix Laboratory software can enlarge the interest and attention of students, with real experience and can grow independent attitude. This research design refers to the use of research and development (R & D) methods that have been modified by multi-disciplinary team-based researchers. This research used Computer based learning method consisting of computer and Matrix Laboratory software which was integrated with props. Matrix Laboratory has the ability to visualize the theory and analysis of the Control System which is an integration of computing, visualization and programming which is easy to use. The result of this instructional media development is to use mathematical equations using Matrix Laboratory software on control system application with DC motor plant and PID (Proportional-Integral-Derivative). Considering that manufacturing in the field of Distributed Control systems (DCSs), Programmable Controllers (PLCs), and Microcontrollers (MCUs) use PID systems in production processes are widely used in industry.

Iterative Learning Control design for uncertain and time-windowed systems

NARCIS (Netherlands)

Wijdeven, van de J.J.M.

2008-01-01

Iterative Learning Control (ILC) is a control strategy capable of dramatically increasing the performance of systems that perform batch repetitive tasks. This performance improvement is achieved by iteratively updating the command signal, using measured error data from previous trials, i.e., by

Enhancing the bioactivity of Poly(lactic-co-glycolic acid scaffold with a nano-hydroxyapatite coating for the treatment of segmental bone defect in a rabbit model

Directory of Open Access Journals (Sweden)

Wang DX

2013-05-01

Full Text Available De-Xin Wang,1,* Yao He,2,* Long Bi1,* Ze-Hua Qu,2 Ji-Wei Zou,1 Zhen Pan,2 Jun-Jun Fan,1 Liang Chen,2 Xin Dong,1 Xiang-Nan Liu,2 Guo-Xian Pei,1 Jian-Dong Ding,21Department of Orthopaedics, Xijing Hospital, The Fourth Military Medical University, Xi'an, People's Republic of China; 2State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai, People's Republic of China*These authors contributed equally to this workPurpose: Poly(lactic-co-glycolic acid (PLGA is excellent as a scaffolding matrix due to feasibility of processing and tunable biodegradability, yet the virgin scaffolds lack osteoconduction and osteoinduction. In this study, nano-hydroxyapatite (nHA was coated on the interior surfaces of PLGA scaffolds in order to facilitate in vivo bone defect restoration using biomimetic ceramics while keeping the polyester skeleton of the scaffolds.Methods: PLGA porous scaffolds were prepared and surface modification was carried out by incubation in modified simulated body fluids. The nHA coated PLGA scaffolds were compared to the virgin PLGA scaffolds both in vitro and in vivo. Viability and proliferation rate of bone marrow stromal cells of rabbits were examined. The constructs of scaffolds and autogenous bone marrow stromal cells were implanted into the segmental bone defect in the rabbit model, and the bone regeneration effects were observed.Results: In contrast to the relative smooth pore surface of the virgin PLGA scaffold, a biomimetic hierarchical nanostructure was found on the surface of the interior pores of the nHA coated PLGA scaffolds by scanning electron microscopy. Both the viability and proliferation rate of the cells seeded in nHA coated PLGA scaffolds were higher than those in PLGA scaffolds. For bone defect repairing, the radius defects had, after 12 weeks implantation of nHA coated PLGA scaffolds, completely recuperated with significantly better bone formation than in

The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

Directory of Open Access Journals (Sweden)

Lun-Hui Xu

2013-01-01

Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

THE DYNAMIC MODEL FOR CONTROL OF STUDENT’S LEARNING INDIVIDUAL TRAJECTORY

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A. A. Mitsel

2015-01-01

Full Text Available In connection with the transition of the educational system to a competence-oriented approach, the problem of learning outcomes assessment and creating an individual learning trajectory of a student has become relevant. Its solution requires the application of modern information technologies. The third generation of Federal state educational standards of higher professional education (FSES HPE defines the requirements for the results of Mastering the basic educational programs (BEP. According to FSES HPE up to 50% of subjects have a variable character, i.e. depend on the choice of a student. It significantly influences on the results of developing various competencies. The problem of forming student’s learning trajectory is analyzed in general and the choice of an individual direction was studied in details. Various methods, models and algorithms of the student’s individual learning trajectory formation were described. The analysis of the model of educational process organization in terms of individual approach makes it possible to develop a decision support system (DSS. DSS is a set of interrelated programs and data used for analysis of situation, development of alternative solutions and selection of the most acceptable alternative. DSSs are often used when building individual learning path, because this task can be considered as a discrete multi-criteria problem, creating a significant burden on the decision maker. A new method of controlling the learning trajectory has been developed. The article discusses problem statement and solution of determining student’s optimal individual educational trajectory as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects. A new model of management learning trajectory is based on dynamic models for tracking the reference trajectory. The task can be converted to an equivalent model of linear programming, for which a reliable solution

Migration, Displacement, and Movements in the Global Space: Ming-Yuen S. Ma’s Multi-Media Project Xin Lu: A Travelogue in Four Parts

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Xiaojing Zhou

2012-06-01

Full Text Available In her recent work, Sau-ling Cynthia Wong draws critical attention to the implications of the formation of an Asian American “diaporic community” in cyberspace, where race still operates as an organizing principle of power relations. Although cyberspace is not confined by national borders, Wong examines how subversion of and intervention in race- and sex-based hierarchies in cyberspace can articulate Asian American identities in relation to diasporas and the nation-state. This essay explores the politics of artistic invention in diasporas as embedded in the disruption, dislocation, and fragmentation in Ming-Yuen S. Ma’s multi-media project, Xin Lu: A Travelogue in Four Parts—a series of four experimental videos about Chinese diasporas. It argues that by moving outside the nation-space into the experiential and virtual “global space” of diasporas, Ma’s work addresses Wong’s concerns and enacts a viable “virtual mediation” that situates Chinese diasporas in the historical contexts of British colonialism and American racial exploitation and exclusion. This movement also entails confronting other forms of oppression, including sexism and heterosexism in both the East and West. While giving voice and visibility to the struggles of racial and sexual minorities across national borders, Ma demonstrates the possibilities of a historicized critical approach to diasporas, one which underlies Wong’s insistence in critiquing gendered and racialized power structures both within and outside the nation-state.

A model reference and sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

NARCIS (Netherlands)

Kovacic, Z.; Bogdan, S.; Balenovic, M.

1999-01-01

In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

Science.gov (United States)

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

A new 2-d approach to iterative , learning control system

International Nuclear Information System (INIS)

Ashraf, S.; Muhammad, E.; Tasleem, M.

2004-01-01

The well known two-dimensional system theory is used to analyze and develop a class of learning control system. In this paper we first explore and test a method given by ZHENG and JAMSHIDI. In that paper all the input samples are treated at once. In comparison our paper presents a scheme in which one sample at a time is treated. The 2- D state-space model of proposed learning control scheme is given. An important consequence of the proposed scheme is that given the right choice of gain matrix and sampling time the system's output can be made to converge to any degree of accuracy. (author)

Automation and Control Learning Environment with Mixed Reality Remote Experiments Architecture

Directory of Open Access Journals (Sweden)

Frederico M. Schaf

2007-05-01

Full Text Available This work aims to the use of remotely web-based experiments to improve the learning process of automation and control systems theory courses. An architecture combining virtual learning environments, remote experiments, students guide and experiments analysis is proposed based on a wide state of art study. The validation of the architecture uses state of art technologies and new simple developed programs to implement the case studies presented. All implementations presented use an internet accessible virtual learning environment providing educational resources, guides and learning material to create a distance learning course associated with the remote mixed reality experiment. This work is part of the RExNet consortium, supported by the European Alfa project.

Up-Regulation of CYP2C19 Expression by BuChang NaoXinTong via PXR Activation in HepG2 Cells.

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Hong Sun

Full Text Available Cytochrome P450 2C19 (CYP2C19 is an important drug-metabolizing enzyme (DME, which is responsible for the biotransformation of several kinds of drugs such as proton pump inhibitors, platelet aggregation inhibitors and antidepressants. Previous studies showed that Buchang NaoXinTong capsules (NXT increased the CYP2C19 metabolic activity in vitro and enhanced the antiplatelet effect of clopidogrel in vivo. However, the underlying molecular mechanism remained unclear. In the present study, we examined whether Pregnane X receptor (PXR plays a role in NXT-mediated regulation of CYP2C19 expression.We applied luciferase assays, real-time quantitative PCR (qPCR, Western blotting and cell-based analysis of metabolic activity experiments to investigate the NXT regulatory effects on the CYP2C19 promoter activity, the mRNA/ protein expression and the metabolic activity.Our results demonstrated that NXT significantly increased the CYP2C19 promoter activity when co-transfected with PXR in HepG2 cells. Mutations in PXR responsive element abolished the NXT inductive effects on the CYP2C19 promoter transcription. Additionally, NXT incubation (150 and 250μg/mL also markedly up-regulated endogenous CYP2C19 mRNA and protein levels in PXR-transfected HepG2 cells. Correspondingly, NXT leaded to a significant enhancement of the CYP2C19 catalytic activity in PXR-transfected HepG2 cells.In summary, this is the first study to suggest that NXT could induce CYP2C19 expression via PXR activation.

Reconfigurable control of a power plant deaerator using learning automata

International Nuclear Information System (INIS)

Garcia, H.E.; Ray, A.; Edwards, R.M.

1991-01-01

A deaerating feedwater heater, equipped with a water level controller and a pressure controller, has been chosen to investigate the feasibility of a reconfigurable control scheme for power plants by incorporating the concept of learning automata. In this paper simulation results based on a model of the Experimental Breeder Reactor (EBR-II) at the Argonne National Laboratory site in Idaho are presented to demonstrate the efficacy of the reconfigurable control scheme

Self-teaching neural network learns difficult reactor control problem

International Nuclear Information System (INIS)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

A User-Centered Educational Modeling Language Improving the Controllability of Learning Design Quality

Science.gov (United States)

Zendi, Asma; Bouhadada, Tahar; Bousbia, Nabila

2016-01-01

Semiformal EMLs are developed to facilitate the adoption of educational modeling languages (EMLs) and to address practitioners' learning design concerns, such as reusability and readability. In this article, SDLD (Structure Dialogue Learning Design) is presented, which is a semiformal EML that aims to improve controllability of learning design…

Splendidly blended: a machine learning set up for CDU control

Science.gov (United States)

Utzny, Clemens

2017-06-01

As the concepts of machine learning and artificial intelligence continue to grow in importance in the context of internet related applications it is still in its infancy when it comes to process control within the semiconductor industry. Especially the branch of mask manufacturing presents a challenge to the concepts of machine learning since the business process intrinsically induces pronounced product variability on the background of small plate numbers. In this paper we present the architectural set up of a machine learning algorithm which successfully deals with the demands and pitfalls of mask manufacturing. A detailed motivation of this basic set up followed by an analysis of its statistical properties is given. The machine learning set up for mask manufacturing involves two learning steps: an initial step which identifies and classifies the basic global CD patterns of a process. These results form the basis for the extraction of an optimized training set via balanced sampling. A second learning step uses this training set to obtain the local as well as global CD relationships induced by the manufacturing process. Using two production motivated examples we show how this approach is flexible and powerful enough to deal with the exacting demands of mask manufacturing. In one example we show how dedicated covariates can be used in conjunction with increased spatial resolution of the CD map model in order to deal with pathological CD effects at the mask boundary. The other example shows how the model set up enables strategies for dealing tool specific CD signature differences. In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget. Overall, this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing.

Self-Control of Task Difficulty During Early Practice Promotes Motor Skill Learning.

Science.gov (United States)

Andrieux, Mathieu; Boutin, Arnaud; Thon, Bernard

2016-01-01

This study was designed to determine whether the effect of self-control of task difficulty on motor learning is a function of the period of self-control administration. In a complex anticipation-coincidence task that required participants to intercept 3 targets with a virtual racquet, the task difficulty was either self-controlled or imposed to the participants in the two phases of the acquisition session. First, the results confirmed the beneficial effects of self-control over fully prescribed conditions. Second, the authors also demonstrated that a partial self-control of task difficulty better promotes learning than does a complete self-controlled procedure. Overall, the results revealed that these benefits are increased when this choice is allowed during early practice. The findings are discussed in terms of theoretical and applied perspectives.

Controlling Uncertainty Decision Making and Learning in Complex Worlds

CERN Document Server

Osman, Magda

2010-01-01

Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us.: Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering; Presents a new framework that is designed to integrate a variety of disparate fields of research; Represents the first book of its kind to provide a general overview of work related to understanding control

Learned helplessness and learned prevalence: exploring the causal relations among perceived controllability, reward prevalence, and exploration.

Science.gov (United States)

Teodorescu, Kinneret; Erev, Ido

2014-10-01

Exposure to uncontrollable outcomes has been found to trigger learned helplessness, a state in which the agent, because of lack of exploration, fails to take advantage of regained control. Although the implications of this phenomenon have been widely studied, its underlying cause remains undetermined. One can learn not to explore because the environment is uncontrollable, because the average reinforcement for exploring is low, or because rewards for exploring are rare. In the current research, we tested a simple experimental paradigm that contrasts the predictions of these three contributors and offers a unified psychological mechanism that underlies the observed phenomena. Our results demonstrate that learned helplessness is not correlated with either the perceived controllability of one's environment or the average reward, which suggests that reward prevalence is a better predictor of exploratory behavior than the other two factors. A simple computational model in which exploration decisions were based on small samples of past experiences captured the empirical phenomena while also providing a cognitive basis for feelings of uncontrollability. © The Author(s) 2014.

Motor control and learning with lower-limb myoelectric control in amputees.

Science.gov (United States)

Alcaide-Aguirre, Ramses E; Morgenroth, David C; Ferris, Daniel P

2013-01-01

Advances in robotic technology have recently enabled the development of powered lower-limb prosthetic limbs. A major hurdle in developing commercially successful powered prostheses is the control interface. Myoelectric signals are one way for prosthetic users to provide feedforward volitional control of prosthesis mechanics. The goal of this study was to assess motor learning in people with lower-limb amputation using proportional myoelectric control from residual-limb muscles. We examined individuals with transtibial amputation and nondisabled controls performing tracking tasks of a virtual object. We assessed how quickly the individuals with amputation improved their performance and whether years since amputation correlated with performance. At the beginning of training, subjects with amputation performed much worse than control subjects. By the end of a short training period, tracking error did not significantly differ between subjects with amputation and nondisabled subjects. Initial but not final performance correlated significantly with time since amputation. This study demonstrates that although subjects with amputation may initially have poor volitional control of their residual lower-limb muscles, training can substantially improve their volitional control. These findings are encouraging for the future use of proportional myoelectric control of powered lower-limb prostheses.

Lessons Learned from the Node 1 Temperature and Humidity Control Subsystem Design

Science.gov (United States)

Williams, David E.

2010-01-01

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Temperature and Humidity Control (THC) subsystem and it will document some of the lessons that have been learned to date for this subsystem and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs. 1

Hilar GABAergic interneuron activity controls spatial learning and memory retrieval.

Directory of Open Access Journals (Sweden)

Yaisa Andrews-Zwilling

Full Text Available Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD, the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear.We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0--a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity.Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD.

Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

Directory of Open Access Journals (Sweden)

Hongbin Wang

2016-01-01

Full Text Available We propose an iterative learning control algorithm (ILC that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.

Visual memory and learning in extremely low-birth-weight/extremely preterm adolescents compared with controls: a geographic study.

Science.gov (United States)

Molloy, Carly S; Wilson-Ching, Michelle; Doyle, Lex W; Anderson, Vicki A; Anderson, Peter J

2014-04-01

Contemporary data on visual memory and learning in survivors born extremely preterm (EP; Visual learning and memory data were available for 221 (74.2%) EP/ELBW subjects and 159 (60.7%) controls. EP/ELBW adolescents exhibited significantly poorer performance across visual memory and learning variables compared with controls. Visual learning and delayed visual memory were particularly problematic and remained so after controlling for visual-motor integration and visual perception and excluding adolescents with neurosensory disability, and/or IQ visual memory and learning outcomes compared with controls, which cannot be entirely explained by poor visual perceptual or visual constructional skills or intellectual impairment.

Magnetic-field control of quantum critical points of valence transition.

Science.gov (United States)

Watanabe, Shinji; Tsuruta, Atsushi; Miyake, Kazumasa; Flouquet, Jacques

2008-06-13

We study the mechanism of how critical end points of first-order valence transitions are controlled by a magnetic field. We show that the critical temperature is suppressed to be a quantum critical point (QCP) by a magnetic field, and unexpectedly, the QCP exhibits nonmonotonic field dependence in the ground-state phase diagram, giving rise to the emergence of metamagnetism even in the intermediate valence-crossover regime. The driving force of the field-induced QCP is clarified to be cooperative phenomena of the Zeeman and Kondo effects, which create a distinct energy scale from the Kondo temperature. This mechanism explains the peculiar magnetic response in CeIrIn(5) and the metamagnetic transition in YbXCu(4) for X=In as well as the sharp contrast between X=Ag and Cd.

Reference: 612 [Arabidopsis Phenome Database[Archive

Lifescience Database Archive (English)

Full Text Available 612 http://metadb.riken.jp/db/SciNetS_ria224i/cria224u4ria224u17513484i Feng Haizh...1531-45 17513484 2007 Jul Plant physiology Chen Qingguo|Feng Haizhong|Feng Jian|Yang Xiaohui|Zhang Jian|Zuo Jianru

Optimization and control of a continuous stirred tank fermenter using learning system

Energy Technology Data Exchange (ETDEWEB)

Thibault, J [Dept. of Chemical Engineering, Laval Univ., Quebec City, PQ (Canada); Najim, K [CNRS, URA 192, GRECO SARTA, Ecole Nationale Superieure d' Ingenieurs de Genie Chimique, 31 - Toulouse (France)

1993-05-01

A variable structure learning automaton is used as an optimization and control of a continuous stirred tank fermenter. The alogrithm requires no modelling of the process. The use of appropriate learning rules enables to locate the optimum dilution rate in order to maximize an objective cost function. It is shown that a hierarchical structure of automata can adapt to environmental changes and can also modify efficiently the domain of variation of the control variable in order to encompass the optimum value. (orig.)

Development of fuzzy algorithm with learning function for nuclear steam generator level control

International Nuclear Information System (INIS)

Park, Gee Yong; Seong, Poong Hyun

1993-01-01

A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

2017-01-01

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Learning Machine. The LWPR algorithm optimizes...... the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical...

Child predictors of learning to control variables via instruction or self-discovery

NARCIS (Netherlands)

Wagensveld, B.; Segers, P.C.J.; Kleemans, M.A.J.; Verhoeven, L.T.W.

2015-01-01

We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group.

An H(∞) control approach to robust learning of feedforward neural networks.

Science.gov (United States)

Jing, Xingjian

2011-09-01

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Practical iterative learning control with frequency domain design and sampled data implementation

CERN Document Server

Wang, Danwei; Zhang, Bin

2014-01-01

This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much h...

Locus of Control in Offenders and Alleged Offenders with Learning Disabilities

Science.gov (United States)

Goodman, Wendy; Leggett, Janice; Garrett, Tanya

2007-01-01

Locus of control can be a useful measure of treatment outcome in offenders from the general population. However, there is little information regarding locus of control and offenders with learning disabilities. Existing measures of locus of control use complex language and abstract ideas that may not be accessible to individuals in this group. A…

Reinforcement learning techniques for controlling resources in power networks

Science.gov (United States)

Kowli, Anupama Sunil

As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

The design of instructional tools affects secondary school students' learning of cardiopulmonary resuscitation (CPR) in reciprocal peer learning: a randomized controlled trial.

Science.gov (United States)

Iserbyt, Peter; Byra, Mark

2013-11-01

Research investigating design effects of instructional tools for learning Basic Life Support (BLS) is almost non-existent. To demonstrate the design of instructional tools matter. The effect of spatial contiguity, a design principle stating that people learn more deeply when words and corresponding pictures are placed close (i.e., integrated) rather than far from each other on a page was investigated on task cards for learning Cardiopulmonary Resuscitation (CPR) during reciprocal peer learning. A randomized controlled trial. A total of 111 students (mean age: 13 years) constituting six intact classes learned BLS through reciprocal learning with task cards. Task cards combine a picture of the skill with written instructions about how to perform it. In each class, students were randomly assigned to the experimental group or the control. In the control, written instructions were placed under the picture on the task cards. In the experimental group, written instructions were placed close to the corresponding part of the picture on the task cards reflecting application of the spatial contiguity principle. One-way analysis of variance found significantly better performances in the experimental group for ventilation volumes (P=.03, ηp2=.10) and flow rates (P=.02, ηp2=.10). For chest compression depth, compression frequency, compressions with correct hand placement, and duty cycles no significant differences were found. This study shows that the design of instructional tools (i.e., task cards) affects student learning. Research-based design of learning tools can enhance BLS and CPR education. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

How Much Do You Trust Me? Learning a Case-Based Model of Inverse Trust

Science.gov (United States)

2014-10-01

metric does not take into account factors of the robot’s behavior that increase trust. The inverse trust metric we use is based on the number of times the...sets contain identical behav- iors. To account for this, the similarity function looks at the overlap between the two sets and ignores behaviors that...155–156 5. Jian, J.Y., Bisantz, A.M., Drury , C.G.: Foundations for an empirically determined scale of trust in automated systems. International

Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

Science.gov (United States)

Chen, Ching-Huei; Chang, Shu-Wei

2015-01-01

The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

Hilar GABAergic Interneuron Activity Controls Spatial Learning and Memory Retrieval

Science.gov (United States)

Andrews-Zwilling, Yaisa; Gillespie, Anna K.; Kravitz, Alexxai V.; Nelson, Alexandra B.; Devidze, Nino; Lo, Iris; Yoon, Seo Yeon; Bien-Ly, Nga; Ring, Karen; Zwilling, Daniel; Potter, Gregory B.; Rubenstein, John L. R.; Kreitzer, Anatol C.; Huang, Yadong

2012-01-01

Background Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD), the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear. Methodology and Principal Findings We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0)—a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity. Conclusions and Significance Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD. PMID:22792368

The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values.

Science.gov (United States)

Msetfi, Rachel M; Kornbrot, Diana E; Matute, Helena; Murphy, Robin A

2015-01-01

Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not) values relevant to control-individualist and collectivist values. Three online experimental contingency learning studies (N 1 = 127, N 2 = 324, N 3 = 272) were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance.

Memory and cognitive control circuits in mathematical cognition and learning

Science.gov (United States)

Menon, V.

2018-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal–frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal–frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. PMID:27339012

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

Science.gov (United States)

Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

2011-01-01

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

Science.gov (United States)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

Development of Computer-Aided Learning Programs on Nuclear Nonproliferation and Control

International Nuclear Information System (INIS)

Kim, Hyun Chul

2011-01-01

The fulfillment of international norms for nuclear nonproliferation is indispensable to the promotion of nuclear energy. The education and training for personnel and mangers related to the nuclear material are one of crucial factors to avoid unintended non-compliance to international norms. Korea Institute of Nuclear Nonproliferation and Control (KINAC) has been providing education and training on nuclear control as its legal duty. One of the legally mandatory educations is 'nuclear control education' performed since 2006 for the observation of the international norms on nuclear nonproliferation and the spread of the nuclear control culture. The other is 'physical protection education' performed since 2010 for maintaining the national physical protection regime effectively and the spread of the nuclear security culture. The 2010 Nuclear Security Summit was held in Washington, DC to enhance international cooperation to prevent nuclear terrorism. During the Summit, the South Korea was chosen to host the second Nuclear Summit in 2012. South Korean President announced that South Korea would share its expertise and support the Summit's mission by setting up an international education and training center on nuclear security in 2014. KINAC is making a full effort to set up the center successfully. An important function of the center is education and training in the subjects of nuclear nonproliferation, nuclear safeguards, nuclear security, and nuclear export/import control. With increasing importance of education and training education on nuclear nonproliferation and control, KINAC has been developing computer-aided learning programs on nuclear nonproliferation and control to overcome the weaknesses in classroom educations. This paper shows two learning programs. One is an e-learning system on the nuclear nonproliferation and control and the other is a virtual reality program for training nuclear material accountancy inspection of light water reactor power plants

Development of Computer-Aided Learning Programs on Nuclear Nonproliferation and Control

Energy Technology Data Exchange (ETDEWEB)

Kim, Hyun Chul [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

2011-10-15

The fulfillment of international norms for nuclear nonproliferation is indispensable to the promotion of nuclear energy. The education and training for personnel and mangers related to the nuclear material are one of crucial factors to avoid unintended non-compliance to international norms. Korea Institute of Nuclear Nonproliferation and Control (KINAC) has been providing education and training on nuclear control as its legal duty. One of the legally mandatory educations is 'nuclear control education' performed since 2006 for the observation of the international norms on nuclear nonproliferation and the spread of the nuclear control culture. The other is 'physical protection education' performed since 2010 for maintaining the national physical protection regime effectively and the spread of the nuclear security culture. The 2010 Nuclear Security Summit was held in Washington, DC to enhance international cooperation to prevent nuclear terrorism. During the Summit, the South Korea was chosen to host the second Nuclear Summit in 2012. South Korean President announced that South Korea would share its expertise and support the Summit's mission by setting up an international education and training center on nuclear security in 2014. KINAC is making a full effort to set up the center successfully. An important function of the center is education and training in the subjects of nuclear nonproliferation, nuclear safeguards, nuclear security, and nuclear export/import control. With increasing importance of education and training education on nuclear nonproliferation and control, KINAC has been developing computer-aided learning programs on nuclear nonproliferation and control to overcome the weaknesses in classroom educations. This paper shows two learning programs. One is an e-learning system on the nuclear nonproliferation and control and the other is a virtual reality program for training nuclear material accountancy inspection of light water

Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

NARCIS (Netherlands)

Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

2017-01-01

Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

Electrophysiological effects of Chinese medicine Shen song Yang xin (SSYX) on Chinese miniature swine heart and isolated guinea pig ventricular myocytes.

Science.gov (United States)

Feng, Li; Gong, Jing; Jin, Zhen-yi; Li, Ning; Sun, Li-ping; Wu, Yi-ling; Pu, Jie-lin

2009-07-05

Shen song Yang xin (SSYX) is a compound of Chinese medicine with the effect of increasing heart rate (HR). This study aimed to evaluate its electrophysiological properties at heart and cellular levels. The Chinese miniature swines were randomly assigned to two groups, administered with SSYX or placebo for 4 weeks (n = 8 per group). Cardiac electrophysiological study (EPS) was performed before and after drug administration. The guinea pig ventricular myocytes were enzymatically isolated and whole cell voltage-clamp technique was used to evaluate the effect of SSYX on cardiac action potential (AP). SSYX treatment accelerated the HR from (141.8 +/- 36.0) beats per minute to (163.0 +/- 38.0) beats per minute (P = 0.013) without changing the other parameters in surface electrocardiogram. After blockage of the autonomic nervous system with metoprolol and atropin, SSYX had no effect on intrinsic HR (IHR), but decreased corrected sinus node recovery time (CSNRT) and sinus atrium conducting time (SACT). Intra cardiac EPS showed that SSYX significantly decreased the A-H and A-V intervals as well as shortened the atrial (A), atrioventricular node (AVN) and ventricular (V) effective refractory period (ERP). In isolated guinea pig ventricular myocytes, the most obvious effect of SSYX on action potential was a shortening of the action potential duration (APD) without change in shape of action potential. The shortening rates of APD(30), APD(50) and APD(90) were 19.5%, 17.8% and 15.3%, respectively. The resting potential (Em) and the interval between the end of APD(30) and APD(90) did not significantly change. The present study demonstrates that SSYX increases the HR and enhances the conducting capacity of the heart in the condition of the intact autonomic nervous system. SSYX homogenously decreases the ERP of the heart and shortens the APD of the myocytes, suggesting its antiarrhythmic effect without proarrhythmia.

Self-controlled learning: The importance of protecting perceptions of competence

Directory of Open Access Journals (Sweden)

Suzete eChiviacowsky

2012-11-01

Full Text Available Recent studies examining the role of self-controlled feedback have shown that learners ask for feedback after what they believe was a good rather than poor trial. Also, trials on which participants request feedback are often more accurate than those without feedback. The present study examined whether manipulating participants’ perception of good performance would have differential effects on learning. All participants practiced a coincident-anticipation timing task with a self-controlled feedback schedule during practice. Specifically, they were able to ask for feedback after 3 trials in each of 3 10-trial practice blocks. While one group (Self-30 was told that an error of 30 ms or less would be considered good performance, another group (Self-4 was informed that an error of 4 ms or less would be considered a good trial. A third, self-control group (Self did not receive any information about what constituted good performance. The results showed that participants of all groups asked for feedback primarily after relatively good trials. At the end of practice, both the Self-30 and Self groups demonstrated greater perceived competence and self-efficacy than the Self-4 group. The Self-30 and Self groups also performed with greater accuracy and less variability on retention and transfer tests (non-dominant hand one day later. The present findings indicated that the typical learning benefits of self-controlled practice can be thwarted by depriving learners of the opportunity of experiencing competence through good performance. They add to the accumulating evidence of motivational influences on motor learning.

Mental health first aid training by e-learning: a randomized controlled trial.

Science.gov (United States)

Jorm, Anthony F; Kitchener, Betty A; Fischer, Julie-Anne; Cvetkovski, Stefan

2010-12-01

Mental Health First Aid training is a course for the public that teaches how to give initial help to a person developing a mental health problem or in a mental health crisis. The present study evaluated the effects of Mental Health First Aid training delivered by e-learning on knowledge about mental disorders, stigmatizing attitudes and helping behaviour. A randomized controlled trial was carried out with 262 members of the Australian public. Participants were randomly assigned to complete an e-learning CD, read a Mental Health First Aid manual or be in a waiting list control group. The effects of the interventions were evaluated using online questionnaires pre- and post-training and at 6-months follow up. The questionnaires covered mental health knowledge, stigmatizing attitudes, confidence in providing help to others, actions taken to implement mental health first aid and participant mental health. Both e-learning and the printed manual increased aspects of knowledge, reduced stigma and increased confidence compared to waiting list. E-learning also improved first aid actions taken more than waiting list, and was superior to the printed manual in reducing stigma and disability due to mental ill health. Mental Health First Aid information received by either e-learning or printed manual had positive effects, but e-learning was better at reducing stigma.

Learning of Temporal and Spatial Movement Aspects: A Comparison of Four Types of Haptic Control and Concurrent Visual Feedback.

Science.gov (United States)

Rauter, Georg; Sigrist, Roland; Riener, Robert; Wolf, Peter

2015-01-01

In literature, the effectiveness of haptics for motor learning is controversially discussed. Haptics is believed to be effective for motor learning in general; however, different types of haptic control enhance different movement aspects. Thus, in dependence on the movement aspects of interest, one type of haptic control may be effective whereas another one is not. Therefore, in the current work, it was investigated if and how different types of haptic controllers affect learning of spatial and temporal movement aspects. In particular, haptic controllers that enforce active participation of the participants were expected to improve spatial aspects. Only haptic controllers that provide feedback about the task's velocity profile were expected to improve temporal aspects. In a study on learning a complex trunk-arm rowing task, the effect of training with four different types of haptic control was investigated: position control, path control, adaptive path control, and reactive path control. A fifth group (control) trained with visual concurrent augmented feedback. As hypothesized, the position controller was most effective for learning of temporal movement aspects, while the path controller was most effective in teaching spatial movement aspects of the rowing task. Visual feedback was also effective for learning temporal and spatial movement aspects.

A Learning Framework for Control-Oriented Modeling of Buildings

Energy Technology Data Exchange (ETDEWEB)

Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.; Vishnu, Abhinav; Vrabie, Draguna L.

2018-01-18

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

Internet-Based Assessment of Oncology Health Care Professional Learning Style and Optimization of Materials for Web-Based Learning: Controlled Trial With Concealed Allocation.

Science.gov (United States)

Micheel, Christine M; Anderson, Ingrid A; Lee, Patricia; Chen, Sheau-Chiann; Justiss, Katy; Giuse, Nunzia B; Ye, Fei; Kusnoor, Sheila V; Levy, Mia A

2017-07-25

Precision medicine has resulted in increasing complexity in the treatment of cancer. Web-based educational materials can help address the needs of oncology health care professionals seeking to understand up-to-date treatment strategies. This study aimed to assess learning styles of oncology health care professionals and to determine whether learning style-tailored educational materials lead to enhanced learning. In all, 21,465 oncology health care professionals were invited by email to participate in the fully automated, parallel group study. Enrollment and follow-up occurred between July 13 and September 7, 2015. Self-enrolled participants took a learning style survey and were assigned to the intervention or control arm using concealed alternating allocation. Participants in the intervention group viewed educational materials consistent with their preferences for learning (reading, listening, and/or watching); participants in the control group viewed educational materials typical of the My Cancer Genome website. Educational materials covered the topic of treatment of metastatic estrogen receptor-positive (ER+) breast cancer using cyclin-dependent kinases 4/6 (CDK4/6) inhibitors. Participant knowledge was assessed immediately before (pretest), immediately after (posttest), and 2 weeks after (follow-up test) review of the educational materials. Study statisticians were blinded to group assignment. A total of 751 participants enrolled in the study. Of these, 367 (48.9%) were allocated to the intervention arm and 384 (51.1%) were allocated to the control arm. Of those allocated to the intervention arm, 256 (69.8%) completed all assessments. Of those allocated to the control arm, 296 (77.1%) completed all assessments. An additional 12 participants were deemed ineligible and one withdrew. Of the 552 participants, 438 (79.3%) self-identified as multimodal learners. The intervention arm showed greater improvement in posttest score compared to the control group (0.4 points

Friend or Foe? Flipped Classroom for Undergraduate Electrocardiogram Learning: a Randomized Controlled Study.

Science.gov (United States)

Rui, Zeng; Lian-Rui, Xiang; Rong-Zheng, Yue; Jing, Zeng; Xue-Hong, Wan; Chuan, Zuo

2017-03-07

Interpreting an electrocardiogram (ECG) is not only one of the most important parts of clinical diagnostics but also one of the most difficult topics to teach and learn. In order to enable medical students to master ECG interpretation skills in a limited teaching period, the flipped teaching method has been recommended by previous research to improve teaching effect on undergraduate ECG learning. A randomized controlled trial for ECG learning was conducted, involving 181 junior-year medical undergraduates using a flipped classroom as an experimental intervention, compared with Lecture-Based Learning (LBL) as a control group. All participants took an examination one week after the intervention by analysing 20 ECGs from actual clinical cases and submitting their ECG reports. A self-administered questionnaire was also used to evaluate the students' attitudes, total learning time, and conditions under each teaching method. The students in the experimental group scored significantly higher than the control group (8.72 ± 1.01 vs 8.03 ± 1.01, t = 4.549, P = 0.000) on ECG interpretation. The vast majority of the students in the flipped classroom group held positive attitudes toward the flipped classroom method and also supported LBL. There was no significant difference (4.07 ± 0.96 vs 4.16 ± 0.89, Z = - 0.948, P = 0.343) between the groups. Prior to class, the students in the flipped class group devoted significantly more time than those in the control group (42.33 ± 22.19 vs 30.55 ± 10.15, t = 4.586, P = 0.000), whereas after class, the time spent by the two groups were not significantly different (56.50 ± 46.80 vs 54.62 ± 31.77, t = 0.317, P = 0.752). Flipped classroom teaching can improve medical students' interest in learning and their self-learning abilities. It is an effective teaching model that needs to be further studied and promoted.

The relationship between mood state and perceived control in contingency learning: Effects of individualist and collectivist values

Directory of Open Access Journals (Sweden)

Rachel M. Msetfi

2015-09-01

Full Text Available Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not values relevant to control - individualist and collectivist values. Three online experimental contingency learning studies (N1 = 127, N2 = 324, N3 = 272 were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance.

Iterative learning control for electrical stimulation and stroke rehabilitation

CERN Document Server

Freeman, Chris T; Burridge, Jane H; Hughes, Ann-Marie; Meadmore, Katie L

2015-01-01

Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke. Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ mus...

Controlled Experiment Replication in Evaluation of E-Learning System's Educational Influence

Science.gov (United States)

Grubisic, Ani; Stankov, Slavomir; Rosic, Marko; Zitko, Branko

2009-01-01

We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning system's advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal…

Trust-Guided Behavior Adaptation Using Case-Based Reasoning

Science.gov (United States)

2015-08-01

Human Factors and Ergonomics Society, 53(5):517–527, 2011. [Jian et al., 2000] Jiun-Yin Jian, Ann M. Bisantz, and Colin G. Drury . Foundations for an...2014] Michelle S. Carlson, Munjal Desai, Jill L. Drury , Hyangshim Kwak, and Holly A. Yanco. Identifying factors that influence trust in automated cars

The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values

Science.gov (United States)

Msetfi, Rachel M.; Kornbrot, Diana E.; Matute, Helena; Murphy, Robin A.

2015-01-01

Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relationship between perceived control and symptoms of mood disorders. We tested these hypotheses with European university samples who were categorized as endorsing (or not) values relevant to control—individualist and collectivist values. Three online experimental contingency learning studies (N1 = 127, N2 = 324, N3 = 272) were carried out. Evidence suggested that individualist values influenced basic learning processes via an effect on learning about the context in which events took place. Participants who endorsed individualist values made control judgments that were more in line with an elemental associative learning model, whilst those who were ambivalent about individualist values made judgments that were more consistent with a configural process. High levels of perceived control and individualist values were directly associated with increased euphoric symptoms of bipolar disorder, and such values completely mediated the relation between perceived control and symptoms. The effect of low perceived control on depression was moderated by collectivist values. Anxiety created by dissonance between values and task may be a catalyst for developing mood symptoms. Conclusions are that values play a significant intermediary role in the relation between perceived control and symptoms of mood disturbance. PMID:26483707

Self-controlled learning benefits: exploring contributions of self-efficacy and intrinsic motivation via path analysis.

Science.gov (United States)

Ste-Marie, Diane M; Carter, Michael J; Law, Barbi; Vertes, Kelly; Smith, Victoria

2016-09-01

Research has shown learning advantages for self-controlled practice contexts relative to yoked (i.e., experimenter-imposed) contexts; yet, explanations for this phenomenon remain relatively untested. We examined, via path analysis, whether self-efficacy and intrinsic motivation are important constructs for explaining self-controlled learning benefits. The path model was created using theory-based and empirically supported relationships to examine causal links between these psychological constructs and physical performance. We hypothesised that self-efficacy and intrinsic motivation would have greater predictive power for learning under self-controlled compared to yoked conditions. Participants learned double-mini trampoline progressions, and measures of physical performance, self-efficacy and intrinsic motivation were collected over two practice days and a delayed retention day. The self-controlled group (M = 2.04, SD = .98) completed significantly more skill progressions in retention than their yoked counterparts (M = 1.3, SD = .65). The path model displayed adequate fit, and similar significant path coefficients were found for both groups wherein each variable was predominantly predicted by its preceding time point (e.g., self-efficacy time 1 predicts self-efficacy time 2). Interestingly, the model was not moderated by group; thus, failing to support the hypothesis that self-efficacy and intrinsic motivation have greater predictive power for learning under self-controlled relative to yoked conditions.

Learning and strain among newcomers: a three-wave study on the effects of job demands and job control.

Science.gov (United States)

Taris, Toon W; Feij, Jan A

2004-11-01

The present 3-wave longitudinal study was an examination of job-related learning and strain as a function of job demand and job control. The participants were 311 newcomers to their jobs. On the basis of R. A. Karasek and T. Theorell's (1990) demand-control model, the authors predicted that high demand and high job control would lead to high levels of learning; low demand and low job control should lead to low levels of learning; high demand and low job control should lead to high levels of strain; and low demand and high job control should lead to low levels of strain. The relation between strain and learning was also examined. The authors tested the hypotheses using ANCOVA and structural equation modeling. The results revealed that high levels of strain have an adverse effect on learning; the reverse effect was not confirmed. It appears that Karasek and Theorell's model is very relevant when examining work socialization processes.

Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

Directory of Open Access Journals (Sweden)

Dazi Li

2015-01-01

Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

Real time reinforcement learning control of dynamic systems applied to an inverted pendulum

NARCIS (Netherlands)

van Luenen, W.T.C.; van Luenen, W.T.C.; Stender, J.; Addis, T.

1990-01-01

Describes work started in order to investigate the use of neural networks for application in adaptive or learning control systems. Neural networks have learning capabilities and they can be used to realize non-linear mappings. These are attractive features which could make them useful building

Iterative learning control an optimization paradigm

CERN Document Server

Owens, David H

2016-01-01

This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...

Online learning control using adaptive critic designs with sparse kernel machines.

Science.gov (United States)

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

The significance of clinical experience on learning outcome from resuscitation training-a randomised controlled study

DEFF Research Database (Denmark)

Jensen, Morten Lind; Lippert, Freddy; Hesselfeldt, Rasmus

2008-01-01

CONTEXT: The impact of clinical experience on learning outcome from a resuscitation course has not been systematically investigated. AIM: To determine whether half a year of clinical experience before participation in an Advanced Life Support (ALS) course increases the immediate learning outcome...... and retention of learning. MATERIALS AND METHODS: This was a prospective single blinded randomised controlled study of the learning outcome from a standard ALS course on a volunteer sample of the entire cohort of newly graduated doctors from Copenhagen University. The outcome measurement was ALS...... immediately following graduation. RESULTS: Invitation to participate was accepted by 154/240 (64%) graduates and 117/154 (76%) completed the study. There was no difference between the intervention and control groups with regard to the immediate learning outcome. The intervention group had significantly higher...

Controlling changes - lessons learned from waste management facilities

International Nuclear Information System (INIS)

Johnson, B.M.; Koplow, A.S.; Stoll, F.E.; Waetje, W.D.

1995-01-01

This paper discusses lessons learned about change control at the Waste Reduction Operations Complex (WROC) and Waste Experimental Reduction Facility (WERF) of the Idaho National Engineering Laboratory (INEL). WROC and WERF have developed and implemented change control and an as-built drawing process and have identified structures, systems, and components (SSCS) for configuration management. The operations have also formed an Independent Review Committee to minimize costs and resources associated with changing documents. WROC and WERF perform waste management activities at the INEL. WROC activities include storage, treatment, and disposal of hazardous and mixed waste. WERF provides volume reduction of solid low-level waste through compaction, incineration, and sizing operations. WROC and WERF's efforts aim to improve change control processes that have worked inefficiently in the past

Iterative learning control for multi-agent systems coordination

CERN Document Server

Yang, Shiping; Li, Xuefang; Shen, Dong

2016-01-01

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.

Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

Science.gov (United States)

van Doorn, Yvonne; van Ruysseveldt, Joris; van Dam, Karen; Mistiaen, Wilhelm; Nikolova, Irina

2016-10-01

This study investigated whether Nigerian nurses' emotional exhaustion and active learning were predicted by job demands, control and social support. Limited research has been conducted concerning nurses' work stress in developing countries, such as Nigeria. Accordingly, it is not clear whether work interventions for improving nurses' well-being in these countries can be based on work stress models that are developed in Western countries, such as the job demand control support model, as well as on empirical findings of job demand control support research. Nurses from Nurses Across the Borders Nigeria were invited to complete an online questionnaire containing validated scales; 210 questionnaires were fully completed and analysed. Multiple regression analysis was used to test the hypotheses. Emotional exhaustion was higher for nurses who experienced high demands and low supervisor support. Active learning occurred when nurses worked under conditions of high control and high supervisor support. The findings suggest that the job demand control support model is applicable in a Nigerian nursing situation; the model indicates which occupational stressors contribute to poor well-being in Nigerian nurses and which work characteristics may boost nurses' active learning. Job (re)design interventions can enhance nurses' well-being and learning by guarding nurses' job demands, and stimulating job control and supervisor support. © 2016 John Wiley & Sons Ltd.

Self-control over combined video feedback and modeling facilitates motor learning.

Science.gov (United States)

Post, Phillip G; Aiken, Christopher A; Laughlin, David D; Fairbrother, Jeffrey T

2016-06-01

Allowing learners to control the video presentation of knowledge of performance (KP) or an expert model during practice has been shown to facilitate motor learning (Aiken, Fairbrother, & Post, 2012; Wulf, Raupach, & Pfeiffer, 2005). Split-screen replay features now allow for the simultaneous presentation of these modes of instructional support. It is uncertain, however, if such a combination incorporated into a self-control protocol would yield similar benefits seen in earlier self-control studies. Therefore, the purpose of the present study was to examine the effects of self-controlled split-screen replay on the learning of a golf chip shot. Participants completed 60 practice trials, three administrations of the Intrinsic Motivation Inventory, and a questionnaire on day one. Retention and transfer tests and a final motivation inventory were completed on day two. Results revealed significantly higher form and accuracy scores for the self-control group during transfer. The self-control group also had significantly higher scores on the perceived competence subscale, reported requesting feedback mostly after perceived poor trials, and recalled a greater number of critical task features compared to the yoked group. The findings for the performance measures were consistent with previous self-control research. Copyright © 2016 Elsevier B.V. All rights reserved.

Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

Directory of Open Access Journals (Sweden)

Houli Duan

2010-01-01

Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

Biisofraxidin on Apoptosis of Human Gastric Cancer BGC-823 Cells

African Journals Online (AJOL)

Wu et al. Trop J Pharm Res, October 2015; 14(10): 1803. Tropical Journal of Pharmaceutical Research ... Jian-Tao Wu1,2*, She-Min Lv1, Chun-Hui Lu1, Jun Gong1 and Jian-Bo An3 ..... Zhu AK, Zhou H, Xia JZ, Jin HC, Wang K, Yan J, Zuo JB,.

A Learning Model for Enhancing the Student's Control in Educational Process Using Web 2.0 Personal Learning Environments

Science.gov (United States)

Rahimi, Ebrahim; van den Berg, Jan; Veen, Wim

2015-01-01

In recent educational literature, it has been observed that improving student's control has the potential of increasing his or her feeling of ownership, personal agency and activeness as means to maximize his or her educational achievement. While the main conceived goal for personal learning environments (PLEs) is to increase student's control by…

Memory and cognitive control circuits in mathematical cognition and learning.

Science.gov (United States)

Menon, V

2016-01-01

Numerical cognition relies on interactions within and between multiple functional brain systems, including those subserving quantity processing, working memory, declarative memory, and cognitive control. This chapter describes recent advances in our understanding of memory and control circuits in mathematical cognition and learning. The working memory system involves multiple parietal-frontal circuits which create short-term representations that allow manipulation of discrete quantities over several seconds. In contrast, hippocampal-frontal circuits underlying the declarative memory system play an important role in formation of associative memories and binding of new and old information, leading to the formation of long-term memories that allow generalization beyond individual problem attributes. The flow of information across these systems is regulated by flexible cognitive control systems which facilitate the integration and manipulation of quantity and mnemonic information. The implications of recent research for formulating a more comprehensive systems neuroscience view of the neural basis of mathematical learning and knowledge acquisition in both children and adults are discussed. © 2016 Elsevier B.V. All rights reserved.

Longitudinal investigation on learned helplessness tested under negative and positive reinforcement involving stimulus control.

Science.gov (United States)

Oliveira, Emileane C; Hunziker, Maria Helena

2014-07-01

In this study, we investigated whether (a) animals demonstrating the learned helplessness effect during an escape contingency also show learning deficits under positive reinforcement contingencies involving stimulus control and (b) the exposure to positive reinforcement contingencies eliminates the learned helplessness effect under an escape contingency. Rats were initially exposed to controllable (C), uncontrollable (U) or no (N) shocks. After 24h, they were exposed to 60 escapable shocks delivered in a shuttlebox. In the following phase, we selected from each group the four subjects that presented the most typical group pattern: no escape learning (learned helplessness effect) in Group U and escape learning in Groups C and N. All subjects were then exposed to two phases, the (1) positive reinforcement for lever pressing under a multiple FR/Extinction schedule and (2) a re-test under negative reinforcement (escape). A fourth group (n=4) was exposed only to the positive reinforcement sessions. All subjects showed discrimination learning under multiple schedule. In the escape re-test, the learned helplessness effect was maintained for three of the animals in Group U. These results suggest that the learned helplessness effect did not extend to discriminative behavior that is positively reinforced and that the learned helplessness effect did not revert for most subjects after exposure to positive reinforcement. We discuss some theoretical implications as related to learned helplessness as an effect restricted to aversive contingencies and to the absence of reversion after positive reinforcement. This article is part of a Special Issue entitled: insert SI title. Copyright © 2014. Published by Elsevier B.V.

Oncology E-Learning for Undergraduate. A Prospective Randomized Controlled Trial.

Science.gov (United States)

da Costa Vieira, René Aloisio; Lopes, Ana Helena; Sarri, Almir José; Benedetti, Zuleica Caulada; de Oliveira, Cleyton Zanardo

2017-06-01

The e-learning education is a promising method, but there are few prospective randomized publications in oncology. The purpose of this study was to assess the level of retention of information in oncology from undergraduate students of physiotherapy. A prospective, controlled, randomized, crossover study, 72 undergraduate students of physiotherapy, from the second to fourth years, were randomized to perform a course of physiotherapy in oncology (PHO) using traditional classroom or e-learning. Students were offered the same content of the subject. The teacher in the traditional classroom model and the e-learning students used the Articulate® software. The course tackled the main issues related to PHO, and it was divided into six modules, 18 lessons, evaluated by 126 questions. A diagnosis evaluation was performed previous to the course and after every module. The sample consisted of 67 students, allocated in groups A (n = 35) and B (n = 32), and the distribution was homogeneous between the groups. Evaluating the correct answers, we observed a limited score in the pre-test (average grade 44.6 %), which has significant (p e-learning, a fact that encourages the use of e-learning in oncology. REBECU1111-1142-1963.

Learning effects of dynamic postural control by auditory biofeedback versus visual biofeedback training.

Science.gov (United States)

Hasegawa, Naoya; Takeda, Kenta; Sakuma, Moe; Mani, Hiroki; Maejima, Hiroshi; Asaka, Tadayoshi

2017-10-01

Augmented sensory biofeedback (BF) for postural control is widely used to improve postural stability. However, the effective sensory information in BF systems of motor learning for postural control is still unknown. The purpose of this study was to investigate the learning effects of visual versus auditory BF training in dynamic postural control. Eighteen healthy young adults were randomly divided into two groups (visual BF and auditory BF). In test sessions, participants were asked to bring the real-time center of pressure (COP) in line with a hidden target by body sway in the sagittal plane. The target moved in seven cycles of sine curves at 0.23Hz in the vertical direction on a monitor. In training sessions, the visual and auditory BF groups were required to change the magnitude of a visual circle and a sound, respectively, according to the distance between the COP and target in order to reach the target. The perceptual magnitudes of visual and auditory BF were equalized according to Stevens' power law. At the retention test, the auditory but not visual BF group demonstrated decreased postural performance errors in both the spatial and temporal parameters under the no-feedback condition. These findings suggest that visual BF increases the dependence on visual information to control postural performance, while auditory BF may enhance the integration of the proprioceptive sensory system, which contributes to motor learning without BF. These results suggest that auditory BF training improves motor learning of dynamic postural control. Copyright © 2017 Elsevier B.V. All rights reserved.

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.

Science.gov (United States)

Oyama, Shintaro; Shimoda, Shingo; Alnajjar, Fady S K; Iwatsuki, Katsuyuki; Hoshiyama, Minoru; Tanaka, Hirotaka; Hirata, Hitoshi

2016-01-01

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

Science.gov (United States)

Wang, Wei-Cheng; Tai, Cheng-Chi

2017-07-01

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

A randomised controlled trial of blended learning to improve the newborn examination skills of medical students.

Science.gov (United States)

Stewart, Alice; Inglis, Garry; Jardine, Luke; Koorts, Pieter; Davies, Mark William

2013-03-01

To evaluate the hypotheses that a blended learning approach would improve the newborn examination skills of medical students and yield a higher level of satisfaction with learning newborn examination. Undergraduate medical students at a tertiary teaching hospital were individually randomised to receive either a standard neonatology teaching programme (control group), or additional online access to the PENSKE Baby Check Learning Module (blended learning group). The primary outcome was performance of newborn examination on standardised assessment by blinded investigators. The secondary outcomes were performance of all 'essential' items of the examination, and participant satisfaction. The recruitment rate was 88% (71/81). The blended learning group achieved a significantly higher mean score than the control group (p=0.02) for newborn examination. There was no difference for performance of essential items, or satisfaction with learning newborn examination. The blended learning group rated the module highly for effective use of learning time and ability to meet specific learning needs. A blended learning approach resulted in a higher level of performance of newborn examination on standardised assessment. This is consistent with published literature on blended learning and has implications for all neonatal clinicians including junior doctors, midwifes and nurse practitioners.

The effects of control versus autonomy in hypermedia learning environments

NARCIS (Netherlands)

Gorissen, Chantal; Kester, Liesbeth; Brand-Gruwel, Saskia; Martens, Rob

2010-01-01

Gorissen, C. J. J., Kester, L., Brand-Gruwel, S., & Martens, R. L. (2010, 13-16 May). The effects of control versus autonomy in hypermedia learning environments. Poster presented at the 4th International Self-Determination Theory Conference, Ghent, Belgium: Kennisnet.

Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

NARCIS (Netherlands)

Doll, B.B.; Jacobs, W.J.; Sanfey, A.G.; Frank, M.J.

2009-01-01

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S (Ed) 1989. Rule-governed behavior:

Bu-Shen-Ning-Xin Decoction ameliorated the osteoporotic phenotype of ovariectomized mice without affecting the serum estrogen concentration or uterus

Directory of Open Access Journals (Sweden)

Wang L

2015-08-01

Full Text Available Ling Wang,1,2,* Xue-Min Qiu,1,2,* Yu-Yan Gui,1,2 Ying-Ping Xu,1,2 Hans-Jürgen Gober,3 Da-Jin Li1 1Laboratory for Reproductive Immunology, Hospital and Institute of Obstetrics and Gynecology, IBS, Fudan University Shanghai Medical College, 2Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai, People’s Republic of China; 3Department of Pharmacy, Wagner Jauregg Hospital and Children’s Hospital, Linz, Austria *These authors contributed equally to this work Introduction: Bu-Shen-Ning-Xin Decoction (BSNXD, a traditional Chinese medicinal composition, has been used as a remedy for postmenopausal osteoporosis, but its effects on bone metabolism and the uterus have not been reported.Purpose: We aimed to determine the respective effects of BSNXD on the bones and the uterus of ovariectomized (OVX mice to evaluate the efficacy and safety of this herbal formula.Materials and methods: Postmenopausal osteoporosis animal models that were generated by ovariectomy were treated with BSNXD. Dual-energy X-ray absorptiometry was performed to analyze the bone mineral density, and histomorphometric analysis was performed to measure the parameters related to bone metabolism. Calcein labeling was performed to detect bone formation. The uteruses from the mice were weighed, and the histomorphometry was analyzed. Drug-derived serum was prepared to assess the 17-β-estradiol concentration via enzyme immunoassay.Results: BSNXD administration ameliorated the osteoporotic phenotype of OVX mice, as evidenced by an increase in the bone mineral density and bone volume; these effects could not be abolished by the administration of the aromatase inhibitor letrozole. Moreover, BSNXD had no effect on the serum estrogen concentration or uterus.Conclusion: These results suggest that BSNXD has ameliorating effects on bone loss due to estrogen deprivation without affecting the peripheral blood estrogen concentration or the uterus in OVX mice. Keywords

The Role of Executive Control of Attention and Selective Encoding for Preschoolers' Learning

Science.gov (United States)

Roderer, Thomas; Krebs, Saskia; Schmid, Corinne; Roebers, Claudia M.

2012-01-01

Selectivity in encoding, aspects of attentional control and their contribution to learning performance were explored in a sample of preschoolers. While the children are performing a learning task, their encoding of relevant and attention towards irrelevant information was recorded through an eye-tracking device. Recognition of target items was…

Reaching control of a full-torso, modelled musculoskeletal robot using muscle synergies emergent under reinforcement learning

International Nuclear Information System (INIS)

Diamond, A; Holland, O E

2014-01-01

‘Anthropomimetic’ robots mimic both human morphology and internal structure—skeleton, muscles, compliance and high redundancy—thus presenting a formidable challenge to conventional control. Here we derive a novel controller for this class of robot which learns effective reaching actions through the sustained activation of weighted muscle synergies, an approach which draws upon compelling, recent evidence from animal and human studies, but is almost unexplored to date in the musculoskeletal robot literature. Since the effective synergy patterns for a given robot will be unknown, we derive a reinforcement-learning approach intended to allow their emergence, in particular those patterns aiding linearization of control. Using an extensive physics-based model of the anthropomimetic ECCERobot, we find that effective reaching actions can be learned comprising only two sequential motor co-activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-activations can be largely reconstructed using weighted combinations of only 13 common fragments. Testing these ‘candidate’ synergies as drivable units, the same controller now learns the reaching task both faster and better. (paper)

Self-learning fuzzy controllers based on temporal back propagation

Science.gov (United States)

Jang, Jyh-Shing R.

1992-01-01

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

Pramana – Journal of Physics | Indian Academy of Sciences

Indian Academy of Sciences (India)

Article ID 33 Research Article. Dynamic analysis, circuit implementation and passive control of a novel four-dimensional chaotic system with multiscroll attractor and multiple coexisting attractors · BANG-CHENG LAI JIAN-JUN HE · More Details Abstract Fulltext PDF. In this paper, we construct a novel 4D autonomous chaotic ...

An Investigation into the Academic Success of Prospective Teachers in Terms of Learning Strategies, Learning Styles and the Locus of Control

Science.gov (United States)

Akça, Figen

2013-01-01

The present research aims to investigate the relationship between the learning strategies, learning styles, the locus of control and the academic success of prospective teachers. The study group consists of 198 university students in various departments at the Uludag University Faculty of Education. Research data were collected with the Locus of…

Knowledge-based errors in anesthesia: a paired, controlled trial of learning and retention.

Science.gov (United States)

Goldhaber-Fiebert, Sara N; Goldhaber-Fiebert, Jeremy D; Rosow, Carl E

2009-01-01

Optimizing patient safety by improving the training of physicians is a major challenge of medical education. In this pilot study, we hypothesized that a brief lecture, targeted to rare but potentially dangerous situations, could improve anesthesia practitioners' knowledge levels with significant retention of learning at six months. In this paired controlled trial, anesthesia residents and attending physicians at Massachusetts General Hospital took the same 14-question multiple choice examination three times: at baseline, immediately after a brief lecture, and six months later. The lecture covered material on seven "intervention" questions; the remaining seven were "control" questions. The authors measured immediate knowledge acquisition, defined as the change in percentage of correct answers on intervention questions between baseline and post-lecture, and measured learning retention as the difference between baseline and six months. Both measurements were corrected for change in performance on control questions. Fifty of the 89 subjects completed all three examinations. The post-lecture increase in percentage of questions answered correctly, adjusted for control, was 22.2% [95% confidence interval (CI) 16.0-28.4%; P learning at six months. Exposing residents or other practitioners to this type of inexpensive teaching intervention may help them to avoid preventable uncommon errors that are rooted in unfamiliarity with the situation or the equipment. The methods used for this study may also be applied to compare the effect of various other teaching modalities while, at the same time, preserving participant anonymity and making adjustments for ongoing learning.

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

Science.gov (United States)

Niewoehner, Kevin R.; Carter, John (Technical Monitor)

2001-01-01

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Digital control systems training on a distance learning platform

Directory of Open Access Journals (Sweden)

Jan PIECHA

2009-01-01

Full Text Available The paper deals with new training technologies development based on approach to distance learning website, implemented in the laboratory of a Traffic Engineering study branch at Faculty of Transport. The discussed computing interface allows students complete knowledge of traffic controllers’ architecture and machine language programming fundamentals. These training facilities are available at home; at their remote terminal. The training resources consist of electronic / computer based training; guidebooks and software units. The laboratory provides the students with an interface entering into simulation packages and programming interfaces, supporting the web training facilities. The courseware complexity selection is one of the most difficult factors in intelligent training unit’s development. The dynamically configured application provides the user with his individually set structure of the training resources. The trainee controls the application structure and complexity, from the time he started. For simplifying the training process and studying activities, several unifications were provided. The introduced ideas need various standardisations, simplifying the e-learning units’ development and application control processes [8], [9]. Further training facilities development concerns virtual laboratory environment organisation in laboratories of Transport Faculty.

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Directory of Open Access Journals (Sweden)

Bin Xu

2017-01-01

Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

International Nuclear Information System (INIS)

Dehkordi, Behzad Mirzaeian; Parsapoor, Amir; Moallem, Mehdi; Lucas, Caro

2011-01-01

In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

Energy Technology Data Exchange (ETDEWEB)

Dehkordi, Behzad Mirzaeian, E-mail: mirzaeian@eng.ui.ac.i [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Parsapoor, Amir, E-mail: amirparsapoor@yahoo.co [Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezar-Jerib St., Postal code 8174673441, Isfahan (Iran, Islamic Republic of); Moallem, Mehdi, E-mail: moallem@cc.iut.ac.i [Department of Electrical Engineering, Isfahan University of Technology, Isfahan (Iran, Islamic Republic of); Lucas, Caro, E-mail: lucas@ut.ac.i [Centre of Excellence for Control and Intelligent Processing, Electrical and Computer Engineering Faculty, College of Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

2011-01-15

In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.

Learning to Learn Online: Using Locus of Control to Help Students Become Successful Online Learners

Science.gov (United States)

Lowes, Susan; Lin, Peiyi

2015-01-01

In this study, approximately 600 online high school students were asked to take Rotter's locus of control questionnaire and then reflect on the results, with the goal of helping them think about their ability to regulate their learning in this new environment. In addition, it was hoped that the results could provide a diagnostic for teachers who…

A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Lund, Henrik Hautop

2017-01-01

Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds...... the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input...

The relationship between mood state and perceived control in contingency learning: effects of individualist and collectivist values

OpenAIRE

Msetfi, Rachel M.; Kornbrot, Diana E.; Matute, Helena; Murphy, Robin A.

2015-01-01

Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relation...

The relationship between mood state and perceived control in contingency learning: Effects of individualist and collectivist values

OpenAIRE

Rachel M. Msetfi; Rachel M. Msetfi; Diana eKornbrot; Helena eMatute; Robin A. Murphy

2015-01-01

Perceived control in contingency learning is linked to psychological wellbeing with low levels of perceived control thought to be a cause or consequence of depression and high levels of control considered to be the hallmark of mental healthiness. However, it is not clear whether this is a universal phenomenon or whether the value that people ascribe to control influences these relationships. Here we hypothesize that values affect learning about control contingencies and influence the relation...

Self-Learning Power Control in Wireless Sensor Networks.

Science.gov (United States)

Chincoli, Michele; Liotta, Antonio

2018-01-27

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

The active learning hypothesis of the job-demand-control model: an experimental examination.

Science.gov (United States)

Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

2014-01-01

The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

Experiential learning in control systems laboratories and engineering project management

Science.gov (United States)

Reck, Rebecca Marie

Experiential learning is a process by which a student creates knowledge through the insights gained from an experience. Kolb's model of experiential learning is a cycle of four modes: (1) concrete experience, (2) reflective observation, (3) abstract conceptualization, and (4) active experimentation. His model is used in each of the three studies presented in this dissertation. Laboratories are a popular way to apply the experiential learning modes in STEM courses. Laboratory kits allow students to take home laboratory equipment to complete experiments on their own time. Although students like laboratory kits, no previous studies compared student learning outcomes on assignments using laboratory kits with existing laboratory equipment. In this study, we examined the similarities and differences between the experiences of students who used a portable laboratory kit and students who used the traditional equipment. During the 2014- 2015 academic year, we conducted a quasi-experiment to compare students' achievement of learning outcomes and their experiences in the instructional laboratory for an introductory control systems course. Half of the laboratory sections in each semester used the existing equipment, while the other sections used a new kit. We collected both quantitative data and qualitative data. We did not identify any major differences in the student experience based on the equipment they used. Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this study, I identified a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories. During the summer of

Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

Science.gov (United States)

Bosworth, John

2006-01-01

A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

Science.gov (United States)

Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

2014-09-01

Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

Science.gov (United States)

Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

2018-05-01

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules.

Science.gov (United States)

Carter, Michael J; Ste-Marie, Diane M

2017-03-01

The learning advantages of self-controlled knowledge-of-results (KR) schedules compared to yoked schedules have been linked to the optimization of the informational value of the KR received for the enhancement of one's error-detection capabilities. This suggests that information-processing activities that occur after motor execution, but prior to receiving KR (i.e., the KR-delay interval) may underlie self-controlled KR learning advantages. The present experiment investigated whether self-controlled KR learning benefits would be eliminated if an interpolated activity was performed during the KR-delay interval. Participants practiced a waveform matching task that required two rapid elbow extension-flexion reversals in one of four groups using a factorial combination of choice (self-controlled, yoked) and KR-delay interval (empty, interpolated). The waveform had specific spatial and temporal constraints, and an overall movement time goal. The results indicated that the self-controlled + empty group had superior retention and transfer scores compared to all other groups. Moreover, the self-controlled + interpolated and yoked + interpolated groups did not differ significantly in retention and transfer; thus, the interpolated activity eliminated the typically found learning benefits of self-controlled KR. No significant differences were found between the two yoked groups. We suggest the interpolated activity interfered with information-processing activities specific to self-controlled KR conditions that occur during the KR-delay interval and that these activities are vital for reaping the associated learning benefits. These findings add to the growing evidence that challenge the motivational account of self-controlled KR learning advantages and instead highlights informational factors associated with the KR-delay interval as an important variable for motor learning under self-controlled KR schedules.

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African Journals Online (AJOL)

Dan Jiang 1*, Jian-Hong Zhou 2*, Jue Zhou 3 , Ying-Er Gu 2, Dong-Xia Yang 4, Qing .... Women were informed about the short- and long-term benefits of HRT. .... Current employment status .... randomized, double-blind, controlled trial of a Chinese herbal formula (Er-Xian decoction) for menopausal symptoms in Hong Kong.

Evolutionary Acquisition of the Global Command and Control System: Lessons Learned

National Research Council Canada - National Science Library

Wallis, Johnathan

1998-01-01

This paper summarizes a "lessons learned" study that reviews DoD's approach to managing the GCCS program on behalf on the Assistant Secretary of Defense for Command, Control, Communications, and Intelligence (ASD/C3I...

Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance.

Science.gov (United States)

Xu, Bin; Sun, Fuchun

2018-02-01

This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

A Neuro-Control Design Based on Fuzzy Reinforcement Learning

DEFF Research Database (Denmark)

Katebi, S.D.; Blanke, M.

This paper describes a neuro-control fuzzy critic design procedure based on reinforcement learning. An important component of the proposed intelligent control configuration is the fuzzy credit assignment unit which acts as a critic, and through fuzzy implications provides adjustment mechanisms....... The fuzzy credit assignment unit comprises a fuzzy system with the appropriate fuzzification, knowledge base and defuzzification components. When an external reinforcement signal (a failure signal) is received, sequences of control actions are evaluated and modified by the action applier unit. The desirable...... ones instruct the neuro-control unit to adjust its weights and are simultaneously stored in the memory unit during the training phase. In response to the internal reinforcement signal (set point threshold deviation), the stored information is retrieved by the action applier unit and utilized for re...

Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

Directory of Open Access Journals (Sweden)

Jian Ding

2014-01-01

Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

Degraded expression of learned feedforward control in movements released by startle.

Science.gov (United States)

Wright, Zachary A; Carlsen, Anthony N; MacKinnon, Colum D; Patton, James L

2015-08-01

Recent work has shown that preplanned motor programs can be rapidly released via fast conducting pathways using a startling acoustic stimulus. Our question was whether the startle-elicited response might also release a recently learned internal model, which draws on experience to predict and compensate for expected perturbations in a feedforward manner. Our initial investigation using adaptation to robotically produced forces showed some evidence of this, but the results were potentially confounded by co-contraction caused by startle. In this study, we eliminated this confound by asking subjects to make reaching movements in the presence of a visual distortion. Results show that a startle stimulus (1) decreased performance of the recently learned task and (2) reduced after-effect magnitude. Since the recall of learned control was reduced, but not eliminated during startle trials, we suggest that multiple neural centers (cortical and subcortical) are involved in such learning and adaptation. These findings have implications for motor training in areas such as piloting, teleoperation, sports, and rehabilitation.

Thalamic control of human attention driven by memory and learning.

Science.gov (United States)

de Bourbon-Teles, José; Bentley, Paul; Koshino, Saori; Shah, Kushal; Dutta, Agneish; Malhotra, Paresh; Egner, Tobias; Husain, Masud; Soto, David

2014-05-05

The role of the thalamus in high-level cognition-attention, working memory (WM), rule-based learning, and decision making-remains poorly understood, especially in comparison to that of cortical frontoparietal networks [1-3]. Studies of visual thalamus have revealed important roles for pulvinar and lateral geniculate nucleus in visuospatial perception and attention [4-10] and for mediodorsal thalamus in oculomotor control [11]. Ventrolateral thalamus contains subdivisions devoted to action control as part of a circuit involving the basal ganglia [12, 13] and motor, premotor, and prefrontal cortices [14], whereas anterior thalamus forms a memory network in connection with the hippocampus [15]. This connectivity profile suggests that ventrolateral and anterior thalamus may represent a nexus between mnemonic and control functions, such as action or attentional selection. Here, we characterize the role of thalamus in the interplay between memory and visual attention. We show that ventrolateral lesions impair the influence of WM representations on attentional deployment. A subsequent fMRI study in healthy volunteers demonstrates involvement of ventrolateral and, notably, anterior thalamus in biasing attention through WM contents. To further characterize the memory types used by the thalamus to bias attention, we performed a second fMRI study that involved learning of stimulus-stimulus associations and their retrieval from long-term memory to optimize attention in search. Responses in ventrolateral and anterior thalamic nuclei tracked learning of the predictiveness of these abstract associations and their use in directing attention. These findings demonstrate a key role for human thalamus in higher-level cognition, notably, in mnemonic biasing of attention. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

A learning flight control system for the F8-DFBW aircraft. [Digital Fly-By-Wire

Science.gov (United States)

Montgomery, R. C.; Mekel, R.; Nachmias, S.

1978-01-01

This report contains a complete description of a learning control system designed for the F8-DFBW aircraft. The system is parameter-adaptive with the additional feature that it 'learns' the variation of the control system gains needed over the flight envelope. It, thus, generates and modifies its gain schedule when suitable data are available. The report emphasizes the novel learning features of the system: the forms of representation of the flight envelope and the process by which identified parameters are used to modify the gain schedule. It contains data taken during piloted real-time 6 degree-of-freedom simulations that were used to develop and evaluate the system.

How does a specific learning and memory system in the mammalian brain gain control of behavior?

Science.gov (United States)

McDonald, Robert J; Hong, Nancy S

2013-11-01

This review addresses a fundamental, yet poorly understood set of issues in systems neuroscience. The issues revolve around conceptualizations of the organization of learning and memory in the mammalian brain. One intriguing, and somewhat popular, conceptualization is the idea that there are multiple learning and memory systems in the mammalian brain and they interact in different ways to influence and/or control behavior. This approach has generated interesting empirical and theoretical work supporting this view. One issue that needs to be addressed is how these systems influence or gain control of voluntary behavior. To address this issue, we clearly specify what we mean by a learning and memory system. We then review two types of processes that might influence which memory system gains control of behavior. One set of processes are external factors that can affect which system controls behavior in a given situation including task parameters like the kind of information available to the subject, types of training experience, and amount of training. The second set of processes are brain mechanisms that might influence what memory system controls behavior in a given situation including executive functions mediated by the prefrontal cortex; switching mechanisms mediated by ascending neurotransmitter systems, the unique role of the hippocampus during learning. The issue of trait differences in control of different learning and memory systems will also be considered in which trait differences in learning and memory function are thought to potentially emerge from differences in level of prefrontal influence, differences in plasticity processes, differences in ascending neurotransmitter control, differential access to effector systems like motivational and motor systems. Finally, we present scenarios in which different mechanisms might interact. This review was conceived to become a jumping off point for new work directed at understanding these issues. The outcome of

The Effects of Variations in Lesson Control and Practice on Learning from Interactive Video.

Science.gov (United States)

Hannafin, Michael J.; Colamaio, MaryAnne E.

1987-01-01

Discussion of the effects of variations in lesson control and practice on the learning of facts, procedures, and problem-solving skills during interactive video instruction focuses on a study of graduates and advanced level undergraduates learning cardiopulmonary resuscitation (CPR). Embedded questioning methods and posttests used are described.…

Language experience differentiates prefrontal and subcortical activation of the cognitive control network in novel word learning.

Science.gov (United States)

Bradley, Kailyn A L; King, Kelly E; Hernandez, Arturo E

2013-02-15

The purpose of this study was to examine the cognitive control mechanisms in adult English speaking monolinguals compared to early sequential Spanish-English bilinguals during the initial stages of novel word learning. Functional magnetic resonance imaging during a lexico-semantic task after only 2h of exposure to novel German vocabulary flashcards showed that monolinguals activated a broader set of cortical control regions associated with higher-level cognitive processes, including the supplementary motor area (SMA), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC), as well as the caudate, implicated in cognitive control of language. However, bilinguals recruited a more localized subcortical network that included the putamen, associated more with motor control of language. These results suggest that experience managing multiple languages may differentiate the learning strategy and subsequent neural mechanisms of cognitive control used by bilinguals compared to monolinguals in the early stages of novel word learning. Copyright © 2012 Elsevier Inc. All rights reserved.

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

Science.gov (United States)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

Effects of arousal on cognitive control: empirical tests of the conflict-modulated Hebbian-learning hypothesis.

Science.gov (United States)

Brown, Stephen B R E; van Steenbergen, Henk; Kedar, Tomer; Nieuwenhuis, Sander

2014-01-01

An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part) simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect) decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

Effects of arousal on cognitive control: Empirical tests of the conflict-modulated Hebbian-learning hypothesis

Directory of Open Access Journals (Sweden)

Stephen B.R.E. Brown

2014-01-01

Full Text Available An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education: A Randomized Controlled Trial.

Science.gov (United States)

Lee, Nam-Ju; Chae, Sun-Mi; Kim, Haejin; Lee, Ji-Hye; Min, Hyojin Jennifer; Park, Da-Eun

2016-01-01

Mobile devices are a regular part of daily life among the younger generations. Thus, now is the time to apply mobile device use to nursing education. The purpose of this study was to identify the effects of a mobile-based video clip on learning motivation, competence, and class satisfaction in nursing students using a randomized controlled trial with a pretest and posttest design. A total of 71 nursing students participated in this study: 36 in the intervention group and 35 in the control group. A video clip of how to perform a urinary catheterization was developed, and the intervention group was able to download it to their own mobile devices for unlimited viewing throughout 1 week. All of the students participated in a practice laboratory to learn urinary catheterization and were blindly tested for their performance skills after participation in the laboratory. The intervention group showed significantly higher levels of learning motivation and class satisfaction than did the control. Of the fundamental nursing competencies, the intervention group was more confident in practicing catheterization than their counterparts. Our findings suggest that video clips using mobile devices are useful tools that educate student nurses on relevant clinical skills and improve learning outcomes.

Computer simulation of nuclear reactor control by means of heuristic learning controller

International Nuclear Information System (INIS)

Bubak, M.; Moscinski, J.

1976-01-01

A trial of application of two techniques of Artificial Intelligence: heuristic Programming and Learning Machines Theory for nuclear reactor control is presented. Considering complexity of the mathematical models describing satisfactorily the nuclear reactors, value changes of these models parameters in course of operation, knowledge of some parameters value with too small exactness, there appear diffucluties in the classical approach application for these objects control systems design. The classical approach consists in definition of the permissible control actions set on the base of the set performance index and the object mathematical model. The Artificial Intelligence methods enable construction of the control system, which gets during work an information being a priori inaccessible and uses it for its action change for the control to be the optimum one. Applying these methods we have elaborated the reactor power control system. As the performance index there has been taken the integral of the error square. For the control system there are only accessible: the set power trajectory, the reactor power and the control rod position. The set power trajectory has been divided into time intervals called heuristic intervals. At the beginning of every heuristic interval, on the base of the obtained experience, the control system chooses from the control (heuristic) set the optimum control. The heuristic set it is the set of relations between the control rod rate and the state variables, the set and the obtained power, similar to simplifications applied by nuclear reactors operators. The results obtained for the different control rod rates and different reactor (simulated on the digital computer) show the proper work of the system. (author)

Machine Learning Control For Highly Reconfigurable High-Order Systems

Science.gov (United States)

2015-01-02

calibration and applications,” Mechatronics and Embedded Systems and Applications (MESA), 2010 IEEE/ASME International Conference on, IEEE, 2010, pp. 38–43...AFRL-OSR-VA-TR-2015-0012 MACHINE LEARNING CONTROL FOR HIGHLY RECONFIGURABLE HIGH-ORDER SYSTEMS John Valasek TEXAS ENGINEERING EXPERIMENT STATION...DIMENSIONAL RECONFIGURABLE SYSTEMS FA9550-11-1-0302 Period of Performance 1 July 2011 – 29 September 2014 John Valasek Aerospace Engineering

Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.

Science.gov (United States)

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

Transformations to diagonal bases in closed-loop quantum learning control experiments

International Nuclear Information System (INIS)

Cardoza, David; Trallero-Herrero, Carlos; Langhojer, Florian; Rabitz, Herschel; Weinacht, Thomas

2005-01-01

This paper discusses transformations between bases used in closed-loop learning control experiments. The goal is to transform to a basis in which the number of control parameters is minimized and in which the parameters act independently. We demonstrate a simple procedure for testing whether a unitary linear transformation (i.e., a rotation amongst the control variables) is sufficient to reduce the search problem to a set of globally independent variables. This concept is demonstrated with closed-loop molecular fragmentation experiments utilizing shaped, ultrafast laser pulses

[Voluntary postural control learning with a use of visual bio-feedback in patients with spinocerebellar degenerations].

Science.gov (United States)

Ustinova, K I; Ioffe, M E; Chernikova, L A; Kulikov, M A; Illarioshkin, S N; Markova, E D

2004-01-01

The study aimed at evaluation of possibility and features of voluntary postural control learning using biofeedback from a force platform in patients with spinocerebellar ataxias. Thirty-seven patients with different forms of spinocerebellar degenerations and 13 age-matched healthy subjects were trained to shift the center of pressure (CP) during several stabilographic computer games which tested an ability to learn 2 different types of voluntary postural control: general strategy and precise coordination of CP shifting. Despite the disturbances of static posture and ability for voluntary control of CP position, patients with spinocerebellar degenerations can learn to control a vertical posture using biofeedback on stabilogram. In contrast to healthy subjects, improvement of coordination in the training process does not exert a significant influence on the static posture characteristics, in particular on lateral CP oscillations. The results obtained suggest involvement of the cerebellum in both types of postural control that distinguishes them from pathology caused by motor cortex and nigro-striatal system involved only in one type of postural control.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

Science.gov (United States)

Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda, Jr.; Tani, Go

2014-01-01

Purpose: The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Method: Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys…

Assessing the Effectiveness of Case-Based Collaborative Learning via Randomized Controlled Trial.

Science.gov (United States)

Krupat, Edward; Richards, Jeremy B; Sullivan, Amy M; Fleenor, Thomas J; Schwartzstein, Richard M

2016-05-01

Case-based collaborative learning (CBCL) is a novel small-group approach that borrows from team-based learning principles and incorporates elements of problem-based learning (PBL) and case-based learning. CBCL includes a preclass readiness assurance process and case-based in-class activities in which students respond to focused, open-ended questions individually, discuss their answers in groups of 4, and then reach consensus in larger groups of 16. This study introduces CBCL and assesses its effectiveness in one course at Harvard Medical School. In a 2013 randomized controlled trial, 64 medical and dental student volunteers were assigned randomly to one of four 8-person PBL tutorial groups (control; n = 32) or one of two 16-person CBCL tutorial groups (experimental condition; n = 32) as part of a required first-year physiology course. Outcomes for the PBL and CBCL groups were compared using final exam scores, student responses to a postcourse survey, and behavioral coding of portions of video-recorded class sessions. Overall, the course final exam scores for CBCL and PBL students were not significantly different. However, CBCL students whose mean exam performance in prior courses was below the participant median scored significantly higher than their PBL counterparts on the physiology course final exam. The most common adjectives students used to describe CBCL were "engaging," "fun," and "thought-provoking." Coding of observed behaviors indicated that individual affect was significantly higher in the CBCL groups than in the PBL groups. CBCL is a viable, engaging, active learning method. It may particularly benefit students with lower academic performance.

Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

Directory of Open Access Journals (Sweden)

Xiaochun Lu

2016-05-01

Full Text Available This paper presents an iterative learning control (ILC strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN controller also demonstrates the effectiveness of the ILC strategy.

Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning.

Science.gov (United States)

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach.

Synergetic motor control paradigm for optimizing energy efficiency of multijoint reaching via tacit learning

Science.gov (United States)

Hayashibe, Mitsuhiro; Shimoda, Shingo

2014-01-01

A human motor system can improve its behavior toward optimal movement. The skeletal system has more degrees of freedom than the task dimensions, which incurs an ill-posed problem. The multijoint system involves complex interaction torques between joints. To produce optimal motion in terms of energy consumption, the so-called cost function based optimization has been commonly used in previous works.Even if it is a fact that an optimal motor pattern is employed phenomenologically, there is no evidence that shows the existence of a physiological process that is similar to such a mathematical optimization in our central nervous system.In this study, we aim to find a more primitive computational mechanism with a modular configuration to realize adaptability and optimality without prior knowledge of system dynamics.We propose a novel motor control paradigm based on tacit learning with task space feedback. The motor command accumulation during repetitive environmental interactions, play a major role in the learning process. It is applied to a vertical cyclic reaching which involves complex interaction torques.We evaluated whether the proposed paradigm can learn how to optimize solutions with a 3-joint, planar biomechanical model. The results demonstrate that the proposed method was valid for acquiring motor synergy and resulted in energy efficient solutions for different load conditions. The case in feedback control is largely affected by the interaction torques. In contrast, the trajectory is corrected over time with tacit learning toward optimal solutions.Energy efficient solutions were obtained by the emergence of motor synergy. During learning, the contribution from feedforward controller is augmented and the one from the feedback controller is significantly minimized down to 12% for no load at hand, 16% for a 0.5 kg load condition.The proposed paradigm could provide an optimization process in redundant system with dynamic-model-free and cost-function-free approach

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Biomechanical procedure to assess sleep restriction on motor control and learning.

Science.gov (United States)

Umemura, G S; Noriega, C L; Soares, D F; Forner-Cordero, A

2017-07-01

The analysis of sleep quality during long periods and its impact on motor control and learning performance are crucial aspects for human health. The aim of this study is to analyze effects of chronic sleep restriction on motor performance. It is intended to establish motor control indicators in sleep quality analysis. A wearable actigraphy that records accelerometry, ambient light, and body temperature was used to monitor the sleep habits of 12 healthy subjects for two weeks before performing motor control and learning tests. The day of the motor test, the subjects filled two questionnaires about the quality of sleep (Pittsburgh Sleep Quality Index - PSQI) and sleepiness (Epworth Sleepiness Scale - ESS). Afterwards they performed a coincident timing task that consisted of hitting a virtual target falling on the screen with the hand. An elbow flexion in the horizontal plane had to be performed on the correct time to reach the real target on a table at the same time as the virtual target on the screen. The subjects performed three sets of acquisition and transfer blocks of the coincident timing task. The subjects were clustered in two groups based on the PSQI and ESS scores. Actigraphy and motor control parameters (L5, correct responses, time variance) were compared between groups and experimental sets. The group with better sleep parameters did show a constant performance across blocks of task acquisition while the bad sleeper group improved from the first to the second acquisition block. Despite of this improvement, their performance is not better than the one of the good sleepers group. Although the number of subjects is low and it should be increased, these results indicate that the subjects with better sleep converged rapidly to a high level of performance, while the worse sleepers needed more trials to learn the task and their performance was not superior to the other group.

Online and Compositional Learning of Controllers with Application to Floor Heating

DEFF Research Database (Denmark)

Larsen, Kim Guldstrand; Mikučionis, Marius; Muniz, Marco

2016-01-01

possible input temperatures and an arbitrary time horizon, we propose an on-line synthesis methodology, where we periodically compute the controller only for the near future based on the current sensor readings. This computation is itself done by employing machine learning in order to avoid enumeration...... of continuous variables (e.g. temperature readings in the different rooms) and even after digitization, the state-space remains huge and cannot be fully explored. We suggest a general and scalable methodology for controller synthesis for such systems. Instead of off-line synthesis of a controller for all...

Fuzzy control in robot-soccer, evolutionary learning in the first layer of control

Directory of Open Access Journals (Sweden)

Peter J Thomas

2003-02-01

Full Text Available In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forward and reverse facing robot to ball and include its physical dimensions.

Using Feedback Error Learning for Control of Electro Hydraulic Servo System by Laguerre

Directory of Open Access Journals (Sweden)

Amir Reza Zare Bidaki

2014-01-01

Full Text Available In this paper, a new Laguerre controller is proposed to control the electro hydraulic servo system. The proposed controller uses feedback error learning method and leads to significantly improve performance in terms of settling time and amplitude of control signal rather than other controllers. All derived results are validated by simulation of nonlinear mathematical model of the system. The simulation results show the advantages of the proposed method for improved control in terms of both settling time and amplitude of control signal.

Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

Science.gov (United States)

1985-02-01

TERMS (Continue on retuerse if necessary and identify by block num ber) FIELD YGROUP SUB. GR. Adaptive control, aritificial intelligence , synthetic aetr1...application of Artificial Intelligence methods to Synthetic Aperture Radars (SARs) is investigated. It was shown that the neuron-like Adaptive Learning...wavelength Al SE!RI M RADAR DIVISION REFERENCES 1. Barto, A.G. and R.S. Sutton, Goal Seeking Components for Adaptive Intelligence : An Initial Assessment

Robust Learning Control Design for Quantum Unitary Transformations.

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Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Lessons learned on the Ground Test Accelerator control system

International Nuclear Information System (INIS)

Kozubal, A.J.; Weiss, R.E.

1994-01-01

When we initiated the control system design for the Ground Test Accelerator (GTA), we envisioned a system that would be flexible enough to handle the changing requirements of an experimental project. This control system would use a developers' toolkit to reduce the cost and time to develop applications for GTA, and through the use of open standards, the system would accommodate unforeseen requirements as they arose. Furthermore, we would attempt to demonstrate on GTA a level of automation far beyond that achieved by existing accelerator control systems. How well did we achieve these goals? What were the stumbling blocks to deploying the control system, and what assumptions did we make about requirements that turned out to be incorrect? In this paper we look at the process of developing a control system that evolved into what is now the ''Experimental Physics and Industrial Control System'' (EPICS). Also, we assess the impact of this system on the GTA project, as well as the impact of GTA on EPICS. The lessons learned on GTA will be valuable for future projects

System Optical Quality Users Guide. Part 2.

Science.gov (United States)

1980-03-01

UPDATES 1. *ID FIXZRN This update redefines the coeffiencets to be input to the Zernike subroutine. This new convention is more physically meaningful...XROT = XIN x COSROT + YIN x SINROT YROT = -XIN x SINROT + YIN x COSROT Application of Zernike polynomials to and SOQ point located at (XIN, YIN) would...MA) MAI1N d !P1 CumT I NUE MAIN 903 opf I TEI bt %) U%7AU5S #AMtES MAIN eI14 ’ 3d F4JeMAr~auoo (AUSSIAN AMP.LLTUOE UJlbIdN~d ON tEAS dtEN FOMb) wIIM

Action Control, L2 Motivational Self System, and Motivated Learning Behavior in a Foreign Language Learning Context

Science.gov (United States)

Khany, Reza; Amiri, Majid

2018-01-01

Theoretical developments in second or foreign language motivation research have led to a better understanding of the convoluted nature of motivation in the process of language acquisition. Among these theories, action control theory has recently shown a good deal of explanatory power in second language learning contexts and in the presence of…

Goal selection versus process control while learning to use a brain-computer interface

Science.gov (United States)

Royer, Audrey S.; Rose, Minn L.; He, Bin

2011-06-01

A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

Articulatory Control in Childhood Apraxia of Speech in a Novel Word-Learning Task

Science.gov (United States)

Case, Julie; Grigos, Maria I.

2016-01-01

Purpose: Articulatory control and speech production accuracy were examined in children with childhood apraxia of speech (CAS) and typically developing (TD) controls within a novel word-learning task to better understand the influence of planning and programming deficits in the production of unfamiliar words. Method: Participants included 16…

Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study.

Science.gov (United States)

Albrecht, Urs-Vito; Folta-Schoofs, Kristian; Behrends, Marianne; von Jan, Ute

2013-08-20

By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the "Profile of Mood States" questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For m

Habit learning and the genetics of the dopamine D3 receptor: evidence from patients with schizophrenia and healthy controls.

Science.gov (United States)

Kéri, Szabolcs; Juhász, Anna; Rimanóczy, Agnes; Szekeres, György; Kelemen, Oguz; Cimmer, Csongor; Szendi, István; Benedek, György; Janka, Zoltán

2005-06-01

In this study, the authors investigated the relationship between the Ser9Gly (SG) polymorphism of the dopamine D3 receptor (DRD3) and striatal habit learning in healthy controls and patients with schizophrenia. Participants were given the weather prediction task, during which probabilistic cue-response associations were learned for tarot cards and weather outcomes (rain or sunshine). In both healthy controls and patients with schizophrenia, participants with Ser9Ser (SS) genotype did not learn during the early phase of the task (1-50 trials), whereas participants with SG genotype did so. During the late phase of the task (51-100 trials), both participants with SS and SG genotype exhibited significant learning. Learning rate was normal in patients with schizophrenia. These results suggest that the DRD3 variant containing glycine is associated with more efficient striatal habit learning in healthy controls and patients with schizophrenia. (c) 2005 APA, all rights reserved.

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

Science.gov (United States)

Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

2018-01-01

Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo

Maladaptive Schemas and Affective Control in Students with Learning Disability: Benefits of Mindfulness-Based Cognitive Therapy

OpenAIRE

Nasrollah Vaisi; Mohammad Rostami; Zohreh Zangooei; Mohammad-Ali Khaksar-Beldachi

2015-01-01

Objectives: This study intended to examine the effectiveness of mindfulness-based cognitive therapy on moderating maladaptive schemas and affective control in students suffering from learning disabilities. Methods: This experimental research was conducted using pretest-posttest and a control group. The population included all the female students who  were studying in the Koohdasht's middle schools (academic year: 2012-2013). The sample included 40 female students suffering from learn...

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

Energy Technology Data Exchange (ETDEWEB)

Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering

2017-10-04

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO₂, NO_x, VOC, PM₁₀) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

A Controller Design with ANFIS Architecture Attendant Learning Ability for SSSC-Based Damping Controller Applied in Single Machine Infinite Bus System

Directory of Open Access Journals (Sweden)

A. Khoshsaadat

2014-09-01

Full Text Available Static Synchronous Series Compensator (SSSC is a series compensating Flexible AC Transmission System (FACTS controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC and PI controller.

A neural learning classifier system with self-adaptive constructivism for mobile robot control.

Science.gov (United States)

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

A Software Control Framework for Learning Coordinated, Multi-Robot Strategies in Open Environments

National Research Council Canada - National Science Library

Grupen, Roderic

2003-01-01

.... The UMass effort marries high-level process descriptions, discrete event analysis and model checking, learning and stochastic exploration, and a control theoretic substrate to accomplish these goals...

An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

Science.gov (United States)

Sun, Shu-Ting; Li, Xiao-Dong; Zhong, Ren-Xin

2017-10-01

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial.

Science.gov (United States)

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students' learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

Science.gov (United States)

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials. PMID:24229729

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

Directory of Open Access Journals (Sweden)

Bjarne Skjødt Worm

2013-11-01

Full Text Available Background and aims : The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods : One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+. All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results : All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups improved statistically significant compared to students at level 1 (p>0.05. There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05. Conclusions : This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

The Mediating Role of Maladaptive Perfectionism in the Association between Psychological Control and Learned Helplessness

Science.gov (United States)

Filippello, Pina; Larcan, Rosalba; Sorrenti, Luana; Buzzai, Caterina; Orecchio, Susanna; Costa, Sebastiano

2017-01-01

Despite the extensive research on parental psychological control, no study has explored the relation between parental and teacher psychological control, maladaptive perfectionism and learned helplessness (LH). The purpose of this study was to investigate (1) whether perceived teacher psychological control predicts positively LH, (2) whether…

Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

Science.gov (United States)

Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

2017-06-01

Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

Pramana – Journal of Physics | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Pramana – Journal of Physics; Volume 73; Issue 6. Magnons interaction of spinor Bose–Einstein condensates in an optical lattice. Yong-Qing Liu. Volume 73 Issue 6 ... Author Affiliations. Yong-Qing Liu1. Department of Physics and Electronics, XinZhou Teachers University, XinZhou, 034000, China ...

Manifold traversing as a model for learning control of autonomous robots

Science.gov (United States)

Szakaly, Zoltan F.; Schenker, Paul S.

1992-01-01

This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.

Wordless intervention for epilepsy in learning disabilities (WIELD): study protocol for a randomized controlled feasibility trial.

Science.gov (United States)

Durand, Marie-Anne; Gates, Bob; Parkes, Georgina; Zia, Asif; Friedli, Karin; Barton, Garry; Ring, Howard; Oostendorp, Linda; Wellsted, David

2014-11-20

Epilepsy is the most common neurological problem that affects people with learning disabilities. The high seizure frequency, resistance to treatments, associated skills deficit and co-morbidities make the management of epilepsy particularly challenging for people with learning disabilities. The Books Beyond Words booklet for epilepsy uses images to help people with learning disabilities manage their condition and improve quality of life. Our aim is to conduct a randomized controlled feasibility trial exploring key methodological, design and acceptability issues, in order to subsequently undertake a large-scale randomized controlled trial of the Books Beyond Words booklet for epilepsy. We will use a two-arm, single-centre randomized controlled feasibility design, over a 20-month period, across five epilepsy clinics in Hertfordshire, United Kingdom. We will recruit 40 eligible adults with learning disabilities and a confirmed diagnosis of epilepsy and will randomize them to use either the Books Beyond Words booklet plus usual care (intervention group) or to receive routine information and services (control group). We will collect quantitative data about the number of eligible participants, number of recruited participants, demographic data, discontinuation rates, variability of the primary outcome measure (quality of life: Epilepsy and Learning Disabilities Quality of Life scale), seizure severity, seizure control, intervention's patterns of use, use of other epilepsy-related information, resource use and the EQ-5D-5L health questionnaire. We will also gather qualitative data about the feasibility and acceptability of the study procedures and the Books Beyond Words booklet. Ethical approval for this study was granted on 28 April 2014, by the Wales Research Ethics Committee 5. Recruitment began on 1 July 2014. The outcomes of this feasibility study will be used to inform the design and methodology of a definitive study, adequately powered to determine the impact of

Failure of operant control of vocal learning in budgerigars

Directory of Open Access Journals (Sweden)

Yoshimasa Seki

2018-02-01

Full Text Available Budgerigars were trained by operant conditioning to produce contact calls immediately after hearing a stimulus contact call. In Experiments 1 and 2, playback stimuli were chosen from two different contact call classes from the bird’s repertoire. Once this task was learned, the birds were then tested with other probe stimulus calls from its repertoire, which differed from the original calls drawn from the two classes. Birds failed to mimic the probe stimuli but instead produced one of the two call classes as in the training sessions, showing that birds learned that each stimulus call served as a discriminative stimulus but not as a vocal template for imitation. In Experiment 3, birds were then trained with stimulus calls falling along a 24-step acoustic gradient which varied between the two sounds representing the two contact call categories. As before, birds obtained a reward when the bird’s vocalization matched that of the stimulus above a criterion level. Since the first step and the last step in the gradient were the birds’ original contact calls,　these two patterns were easily matched. Intermediate contact calls in the gradient were much harder for the birds to match. After extensive training, one bird learned to produce contact calls that had only a modest similarity to the intermediate contact calls along the gradient. In spite of remarkable vocal plasticity under natural conditions, operant conditioning methods with budgerigars, even after extensive training and rigorous control of vocal discriminative stimuli, failed to show vocal learning.

The information system of learning quality control in higher education institutions: achievements and problems of European universities

Directory of Open Access Journals (Sweden)

Orekhova Elena

2016-01-01

Full Text Available The article deals with the main trends in the development of the system of learning quality control connected with the European integration of higher education and the democratization of education. The authors analyze the state of information systems of learning quality control existing in European higher education and identify their strong and weak points. The authors show that in the learning process universities actively use innovative analytic methods as well as modern means of collecting, storing and transferring information that ensure the successful management of such a complex object as the university of the 21st century.

Evaluation of an online interactive Diabetes Needs Assessment Tool (DNAT versus online self-directed learning: a randomised controlled trial

Directory of Open Access Journals (Sweden)

Kellner Thomas

2011-06-01

Full Text Available Abstract Background Methods for the dissemination, understanding and implementation of clinical guidelines need to be examined for their effectiveness to help doctors integrate guidelines into practice. The objective of this randomised controlled trial was to evaluate the effectiveness of an interactive online Diabetes Needs Assessment Tool (DNAT (which constructs an e-learning curriculum based on individually identified knowledge gaps, compared with self-directed e-learning of diabetes guidelines. Methods Health professionals were randomised to a 4-month learning period and either given access to diabetes learning modules alone (control group or DNAT plus learning modules (intervention group. Participants completed knowledge tests before and after learning (primary outcome, and surveys to assess the acceptability of the learning and changes to clinical practice (secondary outcomes. Results Sixty four percent (677/1054 of participants completed both knowledge tests. The proportion of nurses (5.4% was too small for meaningful analysis so they were excluded. For the 650 doctors completing both tests, mean (SD knowledge scores increased from 47.4% (12.6 to 66.8% (11.5 [intervention group (n = 321, 64%] and 47.3% (12.9 to 67.8% (10.8 [control group (n = 329, 66%], (ANCOVA p = 0.186. Both groups were satisfied with the usability and usefulness of the learning materials. Seventy seven percent (218/284 of the intervention group reported combining the DNAT with the recommended reading materials was "very useful"/"useful". The majority in both groups (184/287, 64.1% intervention group and 206/299, 68.9% control group [95% CI for the difference (-2.8 to 12.4] reported integrating the learning into their clinical practice. Conclusions Both groups experienced a similar and significant improvement in knowledge. The learning materials were acceptable and participants incorporated the acquired knowledge into practice. Trial registration ISRCTN: ISRCTN67215088

Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

Science.gov (United States)

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-09-07

In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

Science.gov (United States)

Lee, Sung-Ho; Kim, Minsung

2017-12-01

This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Science.gov (United States)

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

Science.gov (United States)

2013-01-01

Background By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. Objective To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). Methods A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Results Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a

Analysis of learning curves in the on-the-job training of air traffic controllers

NARCIS (Netherlands)

Oprins, E.A.P.B.; Bruggraaff, E.; Roe, R.

2011-01-01

This chapter describes a competence-based assessment system, called CBAS, for air traffic control (ATC) simulator and on-the-job training (OJT), developed at Air Traffic Control The Netherlands (LVNL). In contrast with simulator training, learning processes in OJT are difficult to assess, because

Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

DEFF Research Database (Denmark)

Petrick, Ronald; Kraft, Dirk; Mourao, Kira

We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledgelevel planning, and connectionist machine......-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level lepresentation from low-level world state information....... learning, and focuses on the representational needs of these components.We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high...

LOCUS OF CONTROL AND LEARNED HELPLESSNESS PHENOMENON IN PATIENTS WITH CHRONIC INTERNAL DISEASES

Directory of Open Access Journals (Sweden)

Grekhov R.A.

2016-04-01

Full Text Available The article discloses the concept of locus of control (or the level of subjective control, the phenomenon of learned helplessness in the framework of psychosomatic medicine, and their impact on the efficacy of treatment process. The data on the impact of these factors on the daily living and emotional state of patients, their interpersonal and social relationships, the reasons for the formation of learned helplessness are listened. The alternative psychophysiological treatment methods for emotional and behavioral disorders in psychosomatic diseases, in particular the effectiveness of biofeedback therapy in different types of physical pathology, which opens up the possibility of the patient to implement self-regulation mechanisms are presented. Biofeedback is a practically single psychophysiological evidence-based method of alternative medicine and it is regarded as a branch of behavioral therapy, which aims not only to the regulation of psychophysiological state, but also to shift the external locus of control to the inside. During the application of biofeedback, developed “functional system of self-regulation” form its perfect result. Biofeedback is the process of achieving a greater patient’s awareness of many physiological functions of his body, primarily with the use of tools that provide him with information on his activities, in order to obtain the possibility to manage the systems of his body by his own discretion. The probable mechanism of therapeutic action is the cognitive effect of biofeedback experiences, learning skills of self-control which patients had never happened before. The faith of the patient in his ability to control the symptoms of the disease is considered as a critical value, not a degree of measurable physiological changes.

Self-learning basic life support: A randomised controlled trial on learning conditions.

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Pedersen, Tina Heidi; Kasper, Nina; Roman, Hari; Egloff, Mike; Marx, David; Abegglen, Sandra; Greif, Robert

2018-05-01

To investigate whether pure self-learning without instructor support, resulted in the same BLS-competencies as facilitator-led learning, when using the same commercially available video BLS teaching kit. First-year medical students were randomised to either BLS self-learning without supervision or facilitator-led BLS-teaching. Both groups used the MiniAnne kit (Laerdal Medical, Stavanger, Norway) in the students' local language. Directly after the teaching and three months later, all participants were tested on their BLS-competencies in a simulated scenario, using the Resusci Anne SkillReporter™ (Laerdal Medical, Stavanger, Norway). The primary outcome was percentage of correct cardiac compressions three months after the teaching. Secondary outcomes were all other BLS parameters recorded by the SkillReporter and parameters from a BLS-competence rating form. 240 students were assessed at baseline and 152 students participated in the 3-month follow-up. For our primary outcome, the percentage of correct compressions, we found a median of 48% (interquartile range (IQR) 10-83) for facilitator-led learning vs. 42% (IQR 14-81) for self-learning (p = 0.770) directly after the teaching. In the 3-month follow-up, the rate of correct compressions dropped to 28% (IQR 6-59) for facilitator-led learning (p = 0.043) and did not change significantly in the self-learning group (47% (IQR 12-78), p = 0.729). Self-learning is not inferior to facilitator-led learning in the short term. Self-learning resulted in a better retention of BLS-skills three months after training compared to facilitator-led training. Copyright © 2018 Elsevier B.V. All rights reserved.

An Experience Sampling Study of Learning, Affect, and the Demands Control Support Model

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Daniels, Kevin; Boocock, Grahame; Glover, Jane; Hartley, Ruth; Holland, Julie

2009-01-01

The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per…

Recent development in clusters of rare earths and actinides. Chemistry and materials

Energy Technology Data Exchange (ETDEWEB)

Zheng, Zhiping (ed.) [Arizona Univ., Tucson, AZ (United States). Dept. of Chemistry and Biochemistry

2017-02-01

This book contains the following eight contributions: 1. Lanthanide Hydroxide Cluster Complexes via Ligand-Controlled Hydrolysis of the Lanthanide Ions (Zhonghao Zhang, Yanan Zhang, and Zhiping Zheng); 2. Synthesis and Structures of Lanthanide-Transition Metal Clusters (Xiu-Ying Zheng, Xiang-Jian Kong, and La-Sheng Long); 3. Hydrothermal Synthesis of Lanthanide and Lanthanide-Transition-Metal Cluster Organic Frameworks via Synergistic Coordination Strategy (Jian-Wen Cheng and Guo-Yu Yang); 4. Oxo Clusters of 5f Elements (Sarah Hickam and Peter C.); 5. Construction and Luminescence Properties of 4f and d-4f Clusters with Salen-Type Schiff Base Ligands (Xiaoping Yang, Shiqing Wang, Chengri Wang, Shaoming Huang, and Richard A.); 6. 4f-Clusters for Cryogenic Magnetic Cooling (Yan-Cong Chen, Jun-Liang Liu, and Ming-Liang Tong); 7. Lanthanide Clusters Toward Single-Molecule Magnets (Tian Han, You-Song Ding, and Yan-Zhen Zheng); 8. Molecular Rare Earth Hydride Clusters (Takanori Shima and Zhaomin Hou).

Recent development in clusters of rare earths and actinides. Chemistry and materials

International Nuclear Information System (INIS)

Zheng, Zhiping

2017-01-01

This book contains the following eight contributions: 1. Lanthanide Hydroxide Cluster Complexes via Ligand-Controlled Hydrolysis of the Lanthanide Ions (Zhonghao Zhang, Yanan Zhang, and Zhiping Zheng); 2. Synthesis and Structures of Lanthanide-Transition Metal Clusters (Xiu-Ying Zheng, Xiang-Jian Kong, and La-Sheng Long); 3. Hydrothermal Synthesis of Lanthanide and Lanthanide-Transition-Metal Cluster Organic Frameworks via Synergistic Coordination Strategy (Jian-Wen Cheng and Guo-Yu Yang); 4. Oxo Clusters of 5f Elements (Sarah Hickam and Peter C.); 5. Construction and Luminescence Properties of 4f and d-4f Clusters with Salen-Type Schiff Base Ligands (Xiaoping Yang, Shiqing Wang, Chengri Wang, Shaoming Huang, and Richard A.); 6. 4f-Clusters for Cryogenic Magnetic Cooling (Yan-Cong Chen, Jun-Liang Liu, and Ming-Liang Tong); 7. Lanthanide Clusters Toward Single-Molecule Magnets (Tian Han, You-Song Ding, and Yan-Zhen Zheng); 8. Molecular Rare Earth Hydride Clusters (Takanori Shima and Zhaomin Hou).

The effectiveness of a clinically integrated e-learning course in evidence-based medicine: A cluster randomised controlled trial

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2009-05-01

Full Text Available Abstract Background To evaluate the educational effects of a clinically integrated e-learning course for teaching basic evidence-based medicine (EBM among postgraduates compared to a traditional lecture-based course of equivalent content. Methods We conducted a cluster randomised controlled trial in the Netherlands and the UK involving postgraduate trainees in six obstetrics and gynaecology departments. Outcomes (knowledge gain and change in attitude towards EBM were compared between the clinically integrated e-learning course (intervention and the traditional lecture based course (control. We measured change from pre- to post-intervention scores using a validated questionnaire assessing knowledge (primary outcome and attitudes (secondary outcome. Results There were six clusters involving teaching of 61 postgraduate trainees (28 in the intervention and 33 in the control group. The intervention group achieved slightly higher scores for knowledge gain compared to the control, but these results were not statistically significant (difference in knowledge gain: 3.5 points, 95% CI -2.7 to 9.8, p = 0.27. The attitudinal changes were similar for both groups. Conclusion A clinically integrated e-learning course was at least as effective as a traditional lecture based course and was well accepted. Being less costly than traditional teaching and allowing for more independent learning through materials that can be easily updated, there is a place for incorporating e-learning into postgraduate EBM curricula that offer on-the-job training for just-in-time learning. Trial registration Trial registration number: ACTRN12609000022268.

A Mock Randomized Controlled Trial With Audience Response Technology for Teaching and Learning Epidemiology.

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Baker, Philip R A; Francis, Daniel P; Cathcart, Abby

2017-04-01

The study's objective was to apply and assess an active learning approach to epidemiology and critical appraisal. Active learning comprised a mock, randomized controlled trial (RCT) conducted with learners in 3 countries. The mock trial consisted of blindly eating red Smarties candy (intervention) compared to yellow Smarties (control) to determine whether red Smarties increase happiness. Audience response devices were employed with the 3-fold purposes to produce outcome data for analysis of the effects of red Smarties, identify baseline and subsequent changes in participant's knowledge and confidence in understanding of RCTs, and assess the teaching approach. Of those attending, 82% (117 of 143 learners) participated in the trial component. Participating in the mock trial was a positive experience, and the use of the technology aided learning. The trial produced data that learners analyzed in "real time" during the class. The mock RCT is a fun and engaging approach to teaching RCTs and helping students to develop skills in critical appraisal.

Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

Science.gov (United States)

Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

2017-02-01

Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Toxicity evaluation of biodegradable chitosan nanoparticles using a zebrafish embryo model

OpenAIRE

Hu YL; Qi W; Han F; Shao JZ; Gao JQ

2011-01-01

Yu-Lan Hu1, Wang Qi1, Feng Han2, Jian-Zhong Shao3, Jian-Qing Gao11Institute of Pharmaceutics, College of Pharmaceutical Sciences, 2Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, 3College of Life Sciences, Key Laboratory for Cell and Gene Engineering of Zhejiang Province, Zhejiang University, Hangzhou, Zhejiang, People's Republic of ChinaBackground: Although there are a number of reports regarding the toxicity evaluation of inorganic nanoparticles, knowledge on bi...

Repeated Strains, Social Control, Social Learning, and Delinquency: Testing an Integrated Model of General Strain Theory in China

Science.gov (United States)

Bao, Wan-Ning; Haas, Ain; Chen, Xiaojin; Pi, Yijun

2014-01-01

In Agnew's general strain theory, repeated strains can generate crime and delinquency by reducing social control and fostering social learning of crime. Using a sample of 615 middle-and high-school students in China, this study examines how social control and social learning variables mediate the effect of repeated strains in school and at home on…

Algebraic and adaptive learning in neural control systems

Science.gov (United States)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system

Directory of Open Access Journals (Sweden)

C. Boldisor

2009-12-01

Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.

Anodal transcranial direct current stimulation over the primary motor cortex does not enhance the learning benefits of self-controlled feedback schedules.

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Carter, Michael J; Smith, Victoria; Carlsen, Anthony N; Ste-Marie, Diane M

2018-05-01

A distinct learning advantage has been shown when participants control their knowledge of results (KR) scheduling during practice compared to when the same KR schedule is imposed on the learner without choice (i.e., yoked schedules). Although the learning advantages of self-controlled KR schedules are well-documented, the brain regions contributing to these advantages remain unknown. Identifying key brain regions would not only advance our theoretical understanding of the mechanisms underlying self-controlled learning advantages, but would also highlight regions that could be targeted in more applied settings to boost the already beneficial effects of self-controlled KR schedules. Here, we investigated whether applying anodal transcranial direct current stimulation (tDCS) to the primary motor cortex (M1) would enhance the typically found benefits of learning a novel motor skill with a self-controlled KR schedule. Participants practiced a spatiotemporal task in one of four groups using a factorial combination of KR schedule (self-controlled vs. yoked) and tDCS (anodal vs. sham). Testing occurred on two consecutive days with spatial and temporal accuracy measured on both days and learning was assessed using 24-h retention and transfer tests without KR. All groups improved their performance in practice and a significant effect for practicing with a self-controlled KR schedule compared to a yoked schedule was found for temporal accuracy in transfer, but a similar advantage was not evident in retention. There were no significant differences as a function of KR schedule or tDCS for spatial accuracy in retention or transfer. The lack of a significant tDCS effect suggests that M1 may not strongly contribute to self-controlled KR learning advantages; however, caution is advised with this interpretation as typical self-controlled learning benefits were not strongly replicated in the present experiment.

Framework for robot skill learning using reinforcement learning

Science.gov (United States)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

Experimental Learning of Digital Power Controller for Photovoltaic Module Using Proteus VSM

Directory of Open Access Journals (Sweden)

Abhijit V. Padgavhankar

2014-01-01

Full Text Available The electric power supplied by photovoltaic module depends on light intensity and temperature. It is necessary to control the operating point to draw the maximum power of photovoltaic module. This paper presents the design and implementation of digital power converters using Proteus software. Its aim is to enhance student’s learning for virtual system modeling and to simulate in software for PIC microcontroller along with the hardware design. The buck and boost converters are designed to interface with the renewable energy source that is PV module. PIC microcontroller is used as a digital controller, which senses the PV electric signal for maximum power using sensors and output voltage of the dc-dc converter and according to that switching pulse is generated for the switching of MOSFET. The implementation of proposed system is based on learning platform of Proteus virtual system modeling (VSM and the experimental results are presented.

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

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Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparatively evaluating the pharmacokinetic of fifteen constituents in normal and blood deficiency rats after oral administration of Xin-Sheng-Hua Granule by UPLC-MS/MS.

Science.gov (United States)

Pang, Han-Qing; Tang, Yu-Ping; Cao, Yu-Jie; Tan, Ya-Jie; Jin, Yi; Shi, Xu-Qin; Huang, Sheng-Liang; Sun, Da-Zheng; Sun, Jin; Tang, Zhi-Shu; Duan, Jin-Ao

2017-09-01

Xin-Sheng-Hua Granule (XSHG), a famous traditional Chinese medicine prescription, are clinically applied for the treatment of postpartum disease through nourishing blood and promoting blood circulation. In this investigation, a multi-constituents (trigonelline, stachydrine hydrochloride, hydroxysafflor yellow A, chlorogenic acid, amygdalin, leonurine, liquiritin, ferulic acid, senkyunolide I, senkyunolide H, glycyrrhizic acid, senkyunolide A, ligustilide, butylidenephthalide and glycyrrhetinic acid) pharmacokinetic study of XSHG was conducted for the first time. These fifteen constituents in both normal and blood deficiency rat plasma were monitored by using the established and validated ultra-high-performance liquid chromatography coupled with a triple quadrupole electrospray tandem mass spectrometry (UPLC-TQ-MS/MS) method. The samples were prepared through removing protein from plasma with three volumes of methanol. Sufficient separation of target constituents and internal standards (chloramphenicol and clarithromycin) was obtained on a Thermo Scientific Hypersil GOLD column (100mm×3mm, 1.9μm) within a 20min gradient elution (0.1% formic acid aqueous - acetonitrile). Multiple reaction monitoring (MRM) mode was applied to monitor target analytes in both positive and negative electrospray ionization. For the fifteen selected target analytes, this method was fully validated with excellent linearity (r≥0.9925), satisfactory intra- and inter-day precisions (RSD≤11.87%), as well as good accuracies (RE, between -12.84 and 11.69). And the stabilities, matrix effects and extraction recoveries of the rat plasma samples were also within acceptable limits (RSD<15%). Compared to normal group, the pharmacokinetics of major active constituents (except liquiritin and glycyrrhetinic acid) had significant differences (P<0.05) in the model rats, indicated that several metabolite enzymes activities could be altered at disease condition. Copyright © 2017 Elsevier B.V. All

Dopaminergic control of motivation and reinforcement learning: a closed-circuit account for reward-oriented behavior.

Science.gov (United States)

Morita, Kenji; Morishima, Mieko; Sakai, Katsuyuki; Kawaguchi, Yasuo

2013-05-15

Humans and animals take actions quickly when they expect that the actions lead to reward, reflecting their motivation. Injection of dopamine receptor antagonists into the striatum has been shown to slow such reward-seeking behavior, suggesting that dopamine is involved in the control of motivational processes. Meanwhile, neurophysiological studies have revealed that phasic response of dopamine neurons appears to represent reward prediction error, indicating that dopamine plays central roles in reinforcement learning. However, previous attempts to elucidate the mechanisms of these dopaminergic controls have not fully explained how the motivational and learning aspects are related and whether they can be understood by the way the activity of dopamine neurons itself is controlled by their upstream circuitries. To address this issue, we constructed a closed-circuit model of the corticobasal ganglia system based on recent findings regarding intracortical and corticostriatal circuit architectures. Simulations show that the model could reproduce the observed distinct motivational effects of D1- and D2-type dopamine receptor antagonists. Simultaneously, our model successfully explains the dopaminergic representation of reward prediction error as observed in behaving animals during learning tasks and could also explain distinct choice biases induced by optogenetic stimulation of the D1 and D2 receptor-expressing striatal neurons. These results indicate that the suggested roles of dopamine in motivational control and reinforcement learning can be understood in a unified manner through a notion that the indirect pathway of the basal ganglia represents the value of states/actions at a previous time point, an empirically driven key assumption of our model.

Altitude control in honeybees: joint vision-based learning and guidance.

Science.gov (United States)

Portelli, Geoffrey; Serres, Julien R; Ruffier, Franck

2017-08-23

Studies on insects' visual guidance systems have shed little light on how learning contributes to insects' altitude control system. In this study, honeybees were trained to fly along a double-roofed tunnel after entering it near either the ceiling or the floor of the tunnel. The honeybees trained to hug the ceiling therefore encountered a sudden change in the tunnel configuration midways: i.e. a "dorsal ditch". Thus, the trained honeybees met a sudden increase in the distance to the ceiling, corresponding to a sudden strong change in the visual cues available in their dorsal field of view. Honeybees reacted by rising quickly and hugging the new, higher ceiling, keeping a similar forward speed, distance to the ceiling and dorsal optic flow to those observed during the training step; whereas bees trained to follow the floor kept on following the floor regardless of the change in the ceiling height. When trained honeybees entered the tunnel via the other entry (the lower or upper entry) to that used during the training step, they quickly changed their altitude and hugged the surface they had previously learned to follow. These findings clearly show that trained honeybees control their altitude based on visual cues memorized during training. The memorized visual cues generated by the surfaces followed form a complex optic flow pattern: trained honeybees may attempt to match the visual cues they perceive with this memorized optic flow pattern by controlling their altitude.

The proportion valid effect in covert orienting: strategic control or implicit learning?

Science.gov (United States)

Risko, Evan F; Stolz, Jennifer A

2010-03-01

It is well known that the difference in performance between valid and invalid trials in the covert orienting paradigm (i.e., the cueing effect) increases as the proportion of valid trials increases. This proportion valid effect is widely assumed to reflect "strategic" control over the distribution of attention. In the present experiments we determine if this effect results from an explicit strategy or implicit learning by probing participant's awareness of the proportion of valid trials. Results support the idea that the proportion valid effect in the covert orienting paradigm reflects implicit learning not an explicit strategy.

The Effects of Level of Autonomy on Human-Agent Teaming for Multi-Robot Control and Local Security Maintenance

Science.gov (United States)

2013-11-01

Research: Ready to Deliver the Promises. Mind 2003, 2 (3), 4. Jian, J.; Bisantz, A. M.; Drury , C. G. Foundations for an Empirically Determined Scale...collectively account for 21.3% of the screen area. Objects appear on the horizon in AOI 10 (straight ahead), then as the vehicles approach the object... account for 21.7% of the screen area. Objects appear along the periphery in either AOI 12 (on the right) or AOI 14 (on the left) before disappearing

Long-term results of a randomized controlled trial evaluating preoperative chemotherapy in resectable non-small cell lung cancer