Relevant bibliographies by topics / Power training frequency

Academic literature on the topic 'Power training frequency'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Power training frequency"

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Tavares, Lucas D., Eduardo Oliveira De Souza, Carlos Ugrinowitsch, Gilberto Candido Laurentino, Hamilton Roschel, and Valmor Tricoli. "Effect Of Training Frequency On Muscle Mass, Strength And Power Performance During Reduced Resistance Training." Medicine & Science in Sports & Exercise 48 (May 2016): 475. http://dx.doi.org/10.1249/01.mss.0000486429.41955.81.

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Lee, Myeong Soo, Hwa Jeong Huh, Byung Gi Kim, Hoon Ryu, Ho-Sub Lee, Jong-Moon Kim, and Hun-Tae Chung. "Effects of Qi-Training on Heart Rate Variability." American Journal of Chinese Medicine 30, no. 04 (January 2002): 463–70. http://dx.doi.org/10.1142/s0192415x02000491.

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This study investigates changes in autonomic nervous function through Qi-training. The power spectrum of heart rate variability (HRV) was examined in 20 sedentary healthy subjects and 20 Qi-trainees. It was found that Qi-training in healthy young subjects during controlled respiration increases the high frequency (HF) power and decreases the low frequency / high frequency (LF/HF) power ratio of HRV. These results support the hypothesis that Qi-training increases cardiac parasympathetic tone. In addition, Qi-trainees were found to have higher parasympathetic heart modulation compared with their age-matched, sedentary counterparts. This augmented HRV in Qi-trainees provides further support for long-term Qi-training as a possible non-pharmacological cardio-protective maneuver. In conclusion, Qi-training may stabilize the autonomic nervous system by modulating the parasympathetic nervous system.
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Cardinale, Marco. "High-frequency vibration training able to increase muscle power in postmenopausal women." Archives of Physical Medicine and Rehabilitation 85, no. 4 (April 2004): 687–88. http://dx.doi.org/10.1016/j.apmr.2004.01.015.

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Cui, Chunhai, Enqian Xin, Meili Qu, and Shuai Jiang. "Fatigue and Abnormal State Detection by Using EMG Signal During Football Training." International Journal of Distributed Systems and Technologies 12, no. 2 (April 2021): 13–23. http://dx.doi.org/10.4018/ijdst.2021040102.

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This paper proposes to monitor and recognize the fatigue state during football training by analyzing the surface electromyography (EMG) signals. The surface electromyography (EMG) signal is closely connected with the state during sports and training. First, power frequency interference, motion artifacts, and baseline drift in the surface electromyography (EMG) signal are removed; second, the authors extract 6 features: rectified average value (ARV), integrated electromyography myoelectric value (IEMG), root mean square of electromyography value (RMS), median frequency (MF), average power frequency (MPF), and electromyography power (TP) to represent the surface electromyography (EMG) signal; lastly, the extracted features are input into a one-class support vector machine to determine whether the player has been fatigued and are input into a weighted support vector machine to determine the degree of fatigue if the player has been fatigued. The experimental results show that more than 95% of the fatigue state can be recognized by surface electromyography (EMG) signal.
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Stengel, S. V., W. Kemmler, R. Pintag, C. Beeskow, J. Weineck, D. Lauber, W. A. Kalender, and K. Engelke. "Power training is more effective than strength training for maintaining bone mineral density in postmenopausal women." Journal of Applied Physiology 99, no. 1 (July 2005): 181–88. http://dx.doi.org/10.1152/japplphysiol.01260.2004.

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Physical exercise has a favorable impact on bones, but optimum training strategies are still under discussion. In this study, we compared the effect of slow and fast resistance exercises on various osteodensitometric parameters. Fifty-three postmenopausal women were randomly assigned to a strength training (ST) or a power training group (PT). Both groups carried out a progressive resistance training, a gymnastics session, and a home training over a period of 12 mo. During the resistance training, the ST group used slow and the PT group fast movements; otherwise there were no training differences. All subjects were supplemented with Ca and vitamin D. At baseline and after 12 mo, bone mineral density (BMD) was measured at the lumbar spine, proximal femur, and distal forearm by dual-energy X-ray absorptiometry. We also measured anthropometric data and maximum static strength. Frequency and grade of pain were assessed by questionnaire. After 12 mo, significant between-group differences were observed for BMD at the lumbar spine ( P < 0.05) and the total hip ( P < 0.05). Whereas the PT group maintained BMD at the spine (+0.7 ± 2.1%, not significant) and the total hip (0.0 ± 1.7%, not significant), the ST group lost significantly at both sites (spine: −0.9 ± 1.9%; P < 0.05; total hip: −1.2 ± 1.5%; P < 0.01). No significant between-group differences were observed for anthropometric data, maximum strength, BMD of the forearm, or frequency and grade of pain. These findings suggest that power training is more effective than strength training in reducing bone loss in postmenopausal women.
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Hu, Wenping, Jifeng Liang, Yitao Jin, Fuzhang Wu, Xiaowei Wang, and Ersong Chen. "Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost." Energies 11, no. 11 (November 21, 2018): 3238. http://dx.doi.org/10.3390/en11113238.

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Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid’s stability. To evaluate the stability of the power system quickly and accurately, a low frequency oscillation stability evaluation method based on an improved XGboost algorithm and power system random response data is proposed in this paper. Firstly, the original input feature set describing the dynamic characteristics of the power system is established by analyzing the substance of low frequency oscillation. Taking the random response data of power system including the disturbance end time feature and the dynamic feature of power system as the input sample set, the wavelet threshold is applied to improve its effectiveness. Secondly, using the eigenvalue analysis method, different damping ratios are selected as threshold values to judge the stability of the system low-frequency oscillation. Then, the supervised training with improved XGboost algorithm is performed on the characteristics of stability. On this basis, the training model is obtained and applied to online low frequency oscillation stability evaluation of a power system. Finally, the simulation results of the eight-machine 36-node test system and Hebei southern power grid show that the proposed low frequency oscillation online evaluation method has the features of high evaluation accuracy, fast evaluation speed, low error rate of unstable sample evaluation, and strong anti-noise ability.
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CHEN, CHUAN-SHOW. "BIOMECHANICAL STUDY ON A NEW TRAINING MACHINE AND METHOD FOR POWER AND STRENGTH." Journal of Mechanics in Medicine and Biology 05, no. 02 (June 2005): 243–51. http://dx.doi.org/10.1142/s0219519405001448.

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Strength and power are important in sports competition, and increasing ability of explosive muscle contraction force is key to winning for sports performance in events like jumping or throwing. In this study, Passive Repeated Plyometric (PRP) training method and the machine were designed. This machine allow one to training for top gear power with high frequency and without danger as compared with the conventional way of training like common way of plyomerics or using machine for isotonic muscle contraction. The PRP has it specific effects, which can be summarized as follow: 1. Motor driving PRP machine allow athlete training with high frequency, up to 300 rpm (0.2 Hz), especially for lower extremities to increase thrust force and force of body trunk. 2. PRP training method bring about the stretching reflex and elastic energy to recruit into a powerful muscle contraction. 3. This training machine formed a natural and powerful muscle contraction by stretching and shortening in a cycle of contraction, which is called stretching-shortening cycle or SSC. 4. Well-documented evidence from both theoretical and practical were achieved in 1998 Bangkok Asian game and the following Pusan2002 applying to track & field and basketball players.
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YANG, QINGHAI, and KYUNG SUP KWAK. "OPTIMAL TRAINING DESIGN FOR MIMO–OFDM SYSTEMS UNDER SPATIALLY CORRELATED DOUBLY SELECTIVE FADING CHANNELS." Journal of Circuits, Systems and Computers 16, no. 05 (October 2007): 673–97. http://dx.doi.org/10.1142/s0218126607003988.

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In this paper, we design an optimal training scheme for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems under spatially correlated time- and frequency- (doubly) selective fading channels. We first develop the optimal pilot symbols and placement of pilot clusters with respect to the minimum mean square error (MMSE) of the linear channel estimate. We then derive the optimal power allocation for pilot symbols in a two-water-level way: by maximizing the averaged capacity lower bound, how much power to be allocated for training is determined subject to the global water level (or the constraint of total transmit power); subsequently, pouring power to the pilot symbols with an approximately optimal water-filling scheme subject to the local water level (or the constraint of assigned power for training). In addition, for a particular OFDM size, the optimal number of pilot clusters is derived by maximizing the capacity lower bound and by minimizing the channel estimate's MMSE.
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Shubina, O. S., M. Ye Melnikov, and I. A. Veretelnikov. "Investigation of alpha-wave frequency characteristics in the patients with chronic tension headache and its modification during electromyographic-temperature training." Bulletin of Siberian Medicine 9, no. 2 (April 28, 2010): 42–46. http://dx.doi.org/10.20538/1682-0363-2010-2-42-46.

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Chronic tension headache is the most commonly experienced headache. Low effectiveness of its treatment is connected with lack of understanding of this disorder pathogenensis. It was shown that electromyographic-temperature training provoked modification of alpha-wave power and frequency: power of alpha increased, frequency of maximal increased as well.
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Won, An Na, and An Na Won. "Study of Awareness on Power Failure Crisis Response Training and Cost Burden in the Residential Sector – in KOREA." Advanced Materials Research 955-959 (June 2014): 1850–53. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1850.

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A survey was carried out on 2,250 households for the purpose of understanding the awareness about power failure crisis response training and intention to bear cost of power failure measure in the residential sector. Implications of this study are as follows. Common households showed low awareness about power failure crisis response training. Also, while intention for cost burden on power failure measures such as emergency power facility slightly increased with increasing frequency and time of power failure, households generally had weak intention to bear cost.
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Dissertations / Theses on the topic "Power training frequency"

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Masters, Haydn, and res cand@acu edu au. "Frequency of in-season strength and power training for rugby league." Australian Catholic University. School of Human Movement, 2001. http://dlibrary.acu.edu.au/digitaltheses/public/adt-acuvp8.25072005.

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The purpose of this study was to determine the contribution of different in-season strength and power training frequencies to strength and power performance over the course of a 22 week rugby league competition period. Twenty-eight male (n=28) participants, with both high and low strength pre-training status, were divided into three groups following a 15 week pre-season strength and power training programme. A four week periodised in-season strength and power training programme, with intensities ranging from 75-100%, was cycled for the 22 week competition season. Strength and power training was conducted one day.week(-1) by the first high pre-training status group (HTFL, n=11), and two day.week(-1) by the second high pre-training status group (HTF2, n=9). The low pre-training status group (LTF1, n=8) performed the same strength and power training frequency and programme as HTF1. Training intensity (% 1RM) and volume (sets x repetitions) of in-season strength and power training sessions were standardised for both groups during each training week. Strength, power, and speed data were collected pre-season, and four times during the in-season period. No differences were found between HTF1 and HTF2 in performance variables throughout the 22-week in-season period. Both HTF1 and HTF2 displayed similar significant detraining effects in strength, power, and speed, regardless of in-season training frequency (p<0.05). LTF1 showed no change from pre-season strength and power performance following 22 weeks of the competition period (p<0.05). It was concluded that in-season strength and power training frequency may have a limited role in determining the success of the in-season strength and power training programme in highly trained footballers. The results of the present study suggest a number of factors other than in-season strength and power training frequency may affect in-season strength and power performance and detraining in high strength pre-training status athletes. The effect the start of a competition period has on dynamic athletic performance needs further investigation.
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Dvořák, Jiří. "Biofeedback a jeho použití." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217977.

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The aim of this work is describe common methods of biological feedback therapy that is used to treat some psychosomatic diseases. Subsequently, the description is focused on minimal brain dysfunction treatment by the help of EEG biofeedback. Properties and technical requirements for this therapy are concretized. The last part of this thesis is dedicated to the design and realization of practical software tool for EEG biofeedback therapy which is made in LabView 7.1. The M535 acquisition unit and NI USB-6221 measuring device are used for hardware solution.
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Books on the topic "Power training frequency"

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Winsley, Richard J. Overtraining syndrome. Edited by Neil Armstrong and Willem van Mechelen. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198757672.003.0038.

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Prevalence rates of overreaching/overtraining syndrome in young athletes are ~30–35%, and burnout ~5–10%, indicating that a significant minority of young athletes are thus affected at some time during their sporting careers. Presenting symptoms vary considerably, most commonly including a sustained reduction or stagnation in performance, increased perception of effort during exercise, feelings of muscle heaviness, frequent upper respiratory tract infections, persistent muscle soreness, mood changes, sleep disturbance, and loss of appetite. Excessive training is not always the cause and both training and non-training stressors need to be considered as potential culprits. Power imbalances, single identity, early specialization, coach and/or parent pressure, conditional love, perfectionism, and entrapment may all help explain overtraining in childhood and adolescence. Screening and prevention strategies should take a holistic overview of the young athlete’s sporting environment in order that he/she continues to enjoy and develop in their chosen sport(s).
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Thomas, Damion L. Epilogue. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037177.003.0007.

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This concluding chapter explores the continued usage of sports as a U.S. foreign policy tool after the Mexico City Olympic protests. The United States as well as other global powers continued to utilize sport as a means to solidify friendships, antagonize rivals, and advance claims about the viability of their political, social, and economic systems. Two of the most widely used tactics to express displeasure with other nations were boycotts and the denial of visas for potential competitors. Conversely, sport continued to be viewed as a means to initiate and foster positive relationships. In this regard, some of the most widely employed strategies included sports exchanges, training assistance, and facility construction. Hence, sport remained a venue through which nations articulated political alliances, battleground issues, and counternarratives that frequently went unnoticed by the general public when expressed through traditional diplomatic channels.
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Book chapters on the topic "Power training frequency"

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Ungureanu, Marilena, and Stefania Ioan. "Computer modelling and simulation of the high frequency disturbing processes for low voltage consumers applied in power system training and education." In Computer Aided Learning and Instruction in Science and Engineering, 399–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0022631.

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Lingelbach, Katharina, Sabrina Gado, and Wilhelm Bauer. "Neuro-adaptive tutoring systems - Neurophysiological-based recognition of affective-emotional and cognitive states of learners for intelligent neuro-adaptive tutoring systems." In Competence development and learning assistance systems for the data-driven future, 243–60. Goto Verlag, 2021. http://dx.doi.org/10.30844/wgab_2021_15.

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Monitoring learners’ mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner’s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners’ current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners’ affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 – 12 Hz) and theta (4 – 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners’ states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.
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Szmidt, Eulalia, and Marta Kukier. "A New Approach to Classification of Imbalanced Classes via Atanassov's Intuitionistic Fuzzy Sets." In Intelligent Data Analysis, 85–101. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch005.

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We present a new method of classification of imbalanced classes. The crucial point of the method lies in applying Atanassov’s intuitionistic fuzzy sets (which are a generalization of fuzzy sets) while representing the classes during the first training phase. The Atanassov’s intuitionistic fuzzy sets are generated according to an automatic and mathematically justified procedure from the relative frequency distributions representing the data. Next, we use the information about so-called hesitation margins (which besides membership and non-membership values characterize Atanassov’s intuitionistic fuzzy sets) making it possible to improve the results of data classification. The results obtained in the testing phase were examined not only in the sense of general error/accuracy but also by using confusion matrices, that is, exploring a detailed behavior of the intuitionistic fuzzy classifiers. Detailed analysis of the errors for the examined examples has shown that applying Atanassov’s intuitionistic fuzzy sets gives better results than the counterpart approach via fuzzy sets. Better performance of the intuitionistic fuzzy classifier concerns mainly the recognition power of a smaller class. The method was tested using a benchmark problem from UCI machine learning repository.
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Fragkou, Effrossyni (Effie). "Mode Switching in Medical Interpreting and Ramifications on Interpreters' Training." In Handbook of Research on Medical Interpreting, 291–332. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9308-9.ch013.

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Mode switching is a frequent practice in healthcare interpreting, but has received very little attention. This research aims to bridge the aforementioned gap by investigating the instances of mode switching in interpreter-mediated healthcare encounters and the implications of this practice (or lack thereof) in managing effectively the administration of patients' care. To achieve this aim, the investigator created an online survey intended for trained healthcare interpreters alone. Seventy-five responses were collected over a period of three months (May to July 2019) and analyzed using a mixed methods approach. The objective was to demonstrate how interpreters envisage mode switching from the perspective of the training they received, the applicability of switching in relation to the nature of assignments that call for such shift in modes, the differences in mode switching between spoken and sign language, the institutional or other constraints (such as time limitations, number of participants, power differential among interactants) that call for or hamper mode switching, etc. The collected answers reveal a discrepancy between training and practice as well as between prescriptive requirements and reality in the field of healthcare interpreting. The respondents' comments allow the investigator to make key training recommendations.
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Tüfekci, Aslıhan, and Esra Ayça Güzeldereli Yılmaz. "Educational Data Mining." In Engineering Education Trends in the Digital Era, 70–82. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2562-3.ch004.

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The education-training process and all activities related to it have the power to direct the future of societies. From this point of view, the process should be analyzed frequently in terms of input, output, and other process elements. Educational data mining is a multidisciplinary research area that develops methods and techniques for discovering data derived from various information systems used in education. It contributes to the understanding of the learning styles of learners and enables data-driven decision making to develop existing learning practices and learning materials. The number of academic and technical research on educational data mining is on the rise, and this has led to the need to systematically categorize the existing practices. This systematic mapping study was conducted to provide an overview of the current work on educational data mining and its results are based on 153 primary sources including journal papers, articles published in magazines, conference and symposium papers, theses, and others.
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Conference papers on the topic "Power training frequency"

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Sergienko, Alexander B., and Mark A. Baranov. "Closed-Form Training-Based Blind Estimation of Frequency Offset for QAM Signals with Unknown Power and Signal-to-Noise Ratio." In 2020 22th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2020. http://dx.doi.org/10.1109/dspa48919.2020.9213295.

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Kirchner, William, Steve Southward, and Mehdi Ahmadian. "Ultrasonic Acoustic Health Monitoring of Ball Bearings Using Neural Network Pattern Classification of Power Spectral Density." In 2010 Joint Rail Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/jrc2010-36240.

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This work presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20–120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios that are common in the rail industry. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN’s for failure classification is the need for large quantities of training data. This becomes a particularly important issue when considering applications involving higher value components such as the turbo mechanisms and traction motors on diesel locomotives. Artificial training data, based on the statistical properties of a significantly smaller experimental data set is created to train the artificial neural network. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist.
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Yesilli, Melih C., and Firas A. Khasawneh. "On Transfer Learning of Traditional Frequency and Time Domain Features in Turning." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8274.

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Abstract There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes. Some of the most common features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and the Auto-correlation Function (ACF). In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment. The experiment is performed using four different tool overhang lengths with varying cutting speed and the depth of cut. We then examine the resulting signals and tag them as either chatter or chatter-free. The tagged signals are then used to train a classifier. The classification methods include the most common algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Gradient Boost (GB). Our results show that features extracted from the Fourier spectrum are the most informative when training a classifier and testing on data from the same cutting configuration yielding accuracy as high as %96. However, the accuracy drops significantly when training and testing on two different configurations with different structural eigenfrequencies. Thus, we conclude that while these traditional features can be highly tuned to a certain process, their transfer learning ability is limited. We also compare our results against two other methods with rising popularity in the literature: Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). The latter two methods, especially EEMD, show better transfer learning capabilities for our dataset.
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Vaudrey, Michael A., and William R. Saunders. "Control of Combustor Instabilities Using an Artificial Neural Network." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0529.

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It is well-known that phase-shifting controllers used for active combustion control must be manually adjusted in order to maintain control over a broad range of operating combustor operating conditions. If one assumes that the thermoacoustic instabilities are linearly stabilizable, then what is needed is a method to determine, and ultimately predict, the frequency response of the plant for any range of operating conditions, so the controller design can be automatically updated to track the changing plant gain/phase relationships that are observed with changing heat release. A unique test-based, design process has been proposed to predict the gain/phase characteristics required of a proportional, phase-shifting controller that can stabilize the thermoacoustic instabilities. In this paper, that process is used to automate the design of a fixed-gain feedback controller that limits the amplitudes of any feedback induced instabilities (to some pre-specified level) while providing the best control of the targeted limit cycling pressure oscillations. The paper describes how a neural network was trained, using the suggested design process, to predict the frequency response of the thermoacoustics in a tube combustor at frequencies adjacent to the limit cycle frequency using certain operating conditions that included a sparsely-sampled temperature profile, total air/fuel flow rate, and equivalence ratio. The neural net training was performed using complex valued, open-loop frequency response function data as the desired signal with the previously mentioned operating conditions as the input signals. (The open loop data was collected for a narrow frequency range surrounding the limit cycle instability by performing a sine dwell at discrete frequencies). Once the neural network was trained, it was used to predict the approximate phase and gain margins as a function of temperature and flow conditions. The margins were then used to automatically update and design a fixed shape feedback controller having the proper phase and magnitude to ensure stability and control in the face of changing operating conditions. A companion paper describes the methodology that underlies the automated design of the feedback controller gain and phase delay.
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Watkins, Kenneth S. "Electrical Insulation System Degradation Sensors: Improving Reliability of Power Generation and Distribution." In 16th International Conference on Nuclear Engineering. ASMEDC, 2008. http://dx.doi.org/10.1115/icone16-48130.

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As insulation systems of power system components such as electrical motors, generators and transformers degrade, they become brittle, crack and, eventually, fail to perform their intended function. Failure of the insulation system of these components often leads to costly power interruptions that could be prevented if the actual condition of the insulation system is known. The degradation mechanisms of modern insulation systems are highly dependent on the actual environmental and operational conditions of the component. Current methods to measure insulation system condition are often complex, expensive and require specialized training to interpret. In contrast, conductive composite sensors made of the same polymeric components as the insulation system itself monitor the actual environmentally and operationally induced degradation of the component insulation and provide a quick, objective indication of the current condition and remaining design life of the insulation. This innovative technology utilizes low-cost, inert conductive particles compounded with a portion of the insulation polymer to provide a tiny degradation sensor embedded into the winding, core or stator of the component. Sensor output correlates with the degraded state of the insulation system relative to standard industry thermal endurance testing, giving advanced warning of a degraded condition of the insulation system before design conditions are exceeded. Maintenance personnel, utilizing a simple ohmmeter, can read sensor output quickly and reliably without specialized equipment or training. Alternately, threshold-warning devices connected to the sensor provide constant monitoring. Conductive composite degradation sensors provide advance warning of prematurely degraded insulation systems and reduce the need for complex, intrusive and sometimes destructive electrical testing. Because conductive composite degradation sensors require no electrical power during the aging process, they are ideally suited to wireless, passive radio frequency identification (RFID), and “smart label” technologies.
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Guimarães, Eliseu, Jonnathan Carvalho, Aline Paes, and Alexandre Plastino. "Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11972.

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Sentiment analysis on social media data can be a challenging task, among other reasons, because labeled data for training is not always available. Transfer learning approaches address this problem by leveraging a labeled source domain to obtain a model for a target domain that is different but related to the source domain. However, the question that arises is how to choose proper source data for training the target classifier, which can be made considering the similarity between source and target data using distance metrics. This article investigates the relation between these distance metrics and the classifiers’ performance. For this purpose, we propose to evaluate four metrics combined with distinct dataset representations. Computational experiments, conducted in the Twitter sentiment analysis scenario, showed that the cosine similarity metric combined with bag-of-words normalized with term frequency-inverse document frequency presented the best results in terms of predictive power, outperforming even the classifiers trained with the target dataset in many cases.
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Walters, Madeline A., Zhaoyan Fan, and Burak Sencer. "Data-Based Modeling for Reactive Ion Etching: Effectiveness of an Artificial Neural Network Model for Estimating Tungsten Silicon Nitride Etch Rate." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23992.

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Abstract This paper presents a data-based approach for modeling a plasma etch process by estimating etch rate based on controlled input parameters. This work seeks to use an Artificial Neural Network (ANN) model to correlate controlled tool parameters with etch rate and uniformity for a blanket 1100 Å WSiN thin film using Cl2 and BCl3 chemistry. Experimental data was collected using a Lam 9600 PTX plasma metal etch chamber in an industrial cleanroom. The WSiN film was deposited over 3000 Å TEOS to ensure adhesion, with an 8-inch bare silicon wafer as the base layer. Controlled tool parameters were radio frequency (RF) upper electrode power, RF lower electrode power, Cl2 gas flowrate, BCl3 gas flowrate, and chamber pressure. The full factorial design of experiment method was used to select the combinations of experimental configurations. The ANN model was validated using a subset of the training data.
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8

Karagiannidis, Pavlos, and Nikolaos Themelis. "Data-Driven Ship Propulsion Modeling with Artificial Neural Networks." In SNAME 7th International Symposium on Ship Operations, Management and Economics. SNAME, 2021. http://dx.doi.org/10.5957/some-2021-011.

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The paper examines data-driven techniques for the modeling of ship propulsion that could support a strategy for the reduction of emissions and be utilized for the optimization of a fleet’s operations. A large, high-frequency and automated collected data set is exploited for producing models that estimate the required shaft power or main engine’s fuel consumption of a container ship sailing under arbitrary conditions. A variety of statistical calculations and algorithms for data processing are implemented and state-of-the-art techniques for training and optimizing Feed-Forward Neural Networks (FNNs) are applied. Emphasis is given in the pre-processing of the data and the results indicate that with a proper filtering and preparation stage it is possible to significantly increase the model’s accuracy. Thus, increase our prediction ability and our awareness regarding the ship's hull and propeller actual condition.
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Comanescu, L., S. Sawh, M. Wei, B. Lee, A. Petrescu, A. Nainer, R. K. Jaitly, A. F. Jean, D. F. Basque, and D. S. Mullin. "Point Lepreau Refurbishment Project Level 2 PSA Applications." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75972.

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A Probabilistic Safety Assessment (PSA) for Point Lepreau Generating Station has been completed as part of the plant Refurbishment Project. The main objective of this PSA is to provide insights into plant safety design and performance, including the identification of dominant risk contributors and assessing options for reducing risk. The scope of this assessment covers Level 1 and 2 PSA and includes internal events for full power and shutdown, internal fires and internal floods as well as PSA-based seismic margin assessment (SMA) for full power operation. Following the accident sequence quantification for internal events, fire and flood, the results were integrated to provide an overall estimation of the Severe Core Damage Frequency (SCDF) and the Large Release Frequency (LRF) for the refurbished Point Lepreau plant. Importance analysis was performed on the integrated results to identify risk-significant failures, using Fussell-Vesely and Risk Achievement Worth indices, and risk-contributors using Risk Reduction Worth indices. Based on the importance measures, analysis was performed to evaluate the sensitivity of the SCDF and LRF results to the dominant contributors. Uncertainty analysis was also performed to provide qualitative discussions and quantitative measures of the uncertainties in the results of the PSA, namely the frequency of severe core damage or external releases. Based on the results, recommendations were made to improve maintenance, testing, training procedures as well as housekeeping. Also, results of the Level 1 and 2 PSA have been used to determine the safety important systems and components. This paper discusses the key results and recommendations of the Level 2 PSA as well as the methodology used to determine the safety important systems.
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Mann, Herˇman, and Michal Sˇevcˇenko. "Internet-Based Collaboration and Learning Environment for Efficient Simulation and Control Design." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-42436.

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A software system DYNAST for efficient modeling and simulation distributed across the Internet is freely accessible at http://virtual.cvut.cz/dyn/. DYNAST supports collaboration of remote engineering teams as well as hands-on training of Web-based-course learners. The DYNAST Server solves nonlinear algebro-differential equations, and automatically formulates them for multipole models characterizing configuration of real dynamic systems. DYNAST Server is also able to linearize the models and to provide their semisymbolic analysis in time- and frequency-domains. Clients can submit their problems and interpret the simulation results across the Internet using different user environments. The results can be animated in 3D by means of VRML. The DYNAST Server supports also publishing standardized reports on simulation experiments. The accompanying Web-based course with a knowledge-sharing system and interactively resolvable examples exploits innovative didactic approaches. In control design, the modeling efficiency of DYNAST can be combined to a great advantage with the control-design power of the MATLAB toolsets.
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