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OALib Journal期刊

ISSN: 2333-9721

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Implicit Approximation of Photovoltaic Panel Characteristics Using a Stochastic Approach
ZAPLATILEK, K.,LEUCHTER, J.
Advances in Electrical and Computer Engineering , 2012, DOI: 10.4316/aece.2012.04008
Abstract: In this article, an original system is described for an implicit approximation of photovoltaic panel characteristics. Photovoltaic panels (PV panel) are considered stochastic systems. Long-term measured basic characteristics are input data of the system. Each measurement is one of the stochastic PV realizations. Basic 2-D PV characteristics are approximated using implicit circle equations. Calculated circle passes through the three chosen points of a so-called stochastic cloud and it is an quasi-average PV model. The described approximation system includes all of practice influences over the PV, e.g. solar irradiation, temperature, PV wear, random events, etc. An original 3-D implicit final approximation is also introduced. The mentioned method is unambiguous and it also enables the user to intervene. The method is strictly based on measured data and it was developed and verified in MATLAB environment.
Behavioral Model of Photovoltaic Panel in Simulink
ZAPLATILEK, K.,LEUCHTER, J.
Advances in Electrical and Computer Engineering , 2011, DOI: 10.4316/aece.2011.04013
Abstract: This article deals with creation and application of a model of photovoltaic panel in the MATLAB and Simulink environments. An original model of the real PV panel is applied using the model based design technique. A so-called physical model is also developed using the SimPowerSystems library. The described PV panel model is applied for maximum power optimization in the one-shot and the continuous modes. A few illustrating examples and source code parts are also presented.
Photovoltaic Panel Modeling in MATLAB Environment
K. Zaplatilek,J. Leuchter
Radioengineering , 2011,
Abstract: This article is focused on the original photovoltaic panel model identification method. The method is based on the measured characteristics for given irradiation. The system can automatically create a mathematical model for a particular panel, and a specific temperature. Attention is paid to the approximation error progress, which is at minimum in the Maximum Power Points (MPP). During the application of well-known Shockley’s equation to the entire panel the nonlinear and parametric power losses are corrected. This approach provides an accurate approximation with good agreement with measured data. The method can be applied to any measured photovoltaic panel. The model identification procedure and its use are demonstrated on the example of a particular panel. A detailed methodology for application is presented for users of the MATLAB environment.
Optimization Algorithms Testing and Convergence by Using a Stacked Histogram
ZAPLATILEK, K.,TALPA, M.,LEUCHTER, J.
Advances in Electrical and Computer Engineering , 2011, DOI: 10.4316/aece.2011.01002
Abstract: The article describes an original method of optimization algorithms testing and convergence. The method is based on so-called stacked histogram. Stacked histogram is a histogram with its features marked by a chosen colour scheme. Thus, the histogram maintains the information on the input digital sequence. This approach enables an easy identification of the hidden defects in the random process statistical distribution. The stacked histogram is used for the testing of the convergent quality of various optimization techniques. Its width, position and colour scheme provides enough information on the chosen algorithm optimization trajectory. Both the classic iteration techniques and the stochastic optimization algorithm with the adaptation were used as examples.
The relationship between brain oscillatory activity and therapeutic effectiveness of transcranial magnetic stimulation in the treatment of major depressive disorder
Andrew F. Leuchter,Ian A. Cook
Frontiers in Human Neuroscience , 2013, DOI: 10.3389/fnhum.2013.00037
Abstract: Major depressive disorder (MDD) is marked by disturbances in brain functional connectivity. This connectivity is modulated by rhythmic oscillations of brain electrical activity, which enable coordinated functions across brain regions. Oscillatory activity plays a central role in regulating thinking and memory, mood, cerebral blood flow, and neurotransmitter levels, and restoration of normal oscillatory patterns is associated with effective treatment of MDD. Repetitive transcranial magnetic stimulation (rTMS) is a robust treatment for MDD, but the mechanism of action (MOA) of its benefits for mood disorders remains incompletely understood. Benefits of rTMS have been tied to enhanced neuroplasticity in specific brain pathways. We summarize here the evidence that rTMS entrains and resets thalamocortical oscillators, normalizes regulation and facilitates reemergence of intrinsic cerebral rhythms, and through this mechanism restores normal brain function. This entrainment and resetting may be a critical step in engendering neuroplastic changes and the antidepressant effects of rTMS. It may be possible to modify the method of rTMS administration to enhance this MOA and achieve better antidepressant effectiveness. We propose that rTMS can be administered: (1) synchronized to a patient's individual alpha frequency (IAF), or synchronized rTMS (sTMS); (2) as a low magnetic field strength sinusoidal waveform; and, (3) broadly to multiple brain areas simultaneously. We present here the theory and evidence indicating that these modifications could enhance the therapeutic effectiveness of rTMS for the treatment of MDD.
What Shapes Depressed Individuals' Pre-Treatment Expectation in Antidepressant Clinical Trials?
Tally Moses,Andrew F. Leuchter,Ian Cook,Michelle Abrams
The Internet Journal of Mental Health , 2007,
Abstract: Objective: To examine the relationship between patients' treatment outcome expectation and a set of socio-demographic factors, clinical course variables, symptom severity, health locus of control, and Temperament and Character Inventory (TCI) dimensions. Method: Logistic regression analyses were performed on data collected at screen and baseline interviews from 45 participants enrolled into one of two randomized placebo controlled antidepressant trials. Results: Participants with high outcome expectations reported shorter depressive episodes and scored lower on Harm Avoidance (TCI). The data also suggest that external locus of control, gender, ethnicity/race, employment status, and the dimension of self-directedness (TCI) may have a role in shaping treatment expectation. Conclusion: Depressed patients' treatment outcome expectations were found to be associated with depression characteristics, personality traits, locus of control, and certain socio-demographic factors. If these findings are replicated, this information can be used to identify individuals needing additional interpersonal support or motivation at the onset of treatment.
Yoga as a Complementary Treatment of Depression: Effects of Traits and Moods on Treatment Outcome
David Shapiro,Ian A. Cook,Dmitry M. Davydov,Cristina Ottaviani,Andrew F. Leuchter,Michelle Abrams
Evidence-Based Complementary and Alternative Medicine , 2007, DOI: 10.1093/ecam/nel114
Abstract: Preliminary findings support the potential of yoga as a complementary treatment of depressed patients who are taking anti-depressant medications but who are only in partial remission. The purpose of this article is to present further data on the intervention, focusing on individual differences in psychological, emotional and biological processes affecting treatment outcome. Twenty-seven women and 10 men were enrolled in the study, of whom 17 completed the intervention and pre- and post-intervention assessment data. The intervention consisted of 20 classes led by senior Iyengar yoga teachers, in three courses of 20 yoga classes each. All participants were diagnosed with unipolar major depression in partial remission. Psychological and biological characteristics were assessed pre- and post-intervention, and participants rated their mood states before and after each class. Significant reductions were shown for depression, anger, anxiety, neurotic symptoms and low frequency heart rate variability in the 17 completers. Eleven out of these completers achieved remission levels post-intervention. Participants who remitted differed from the non-remitters at intake on several traits and on physiological measures indicative of a greater capacity for emotional regulation. Moods improved from before to after the yoga classes. Yoga appears to be a promising intervention for depression; it is cost-effective and easy to implement. It produces many beneficial emotional, psychological and biological effects, as supported by observations in this study. The physiological methods are especially useful as they provide objective markers of the processes and effectiveness of treatment. These observations may help guide further clinical application of yoga in depression and other mental health disorders, and future research on the processes and mechanisms.
Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression
Andrew F. Leuchter, Ian A. Cook, Aimee M. Hunter, Chaochao Cai, Steve Horvath
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0032508
Abstract: Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) frequency bands. The frontopolar region contained the greatest number of “hub nodes” (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD.
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