High-performance Training Research Articles

Reconceptualising professional learning through knowing-in-practice: a case study of a coaches high performance centre

In response to learning development literature that is negative regarding the formal education coaches’ encounter, there has been a conceptual/practical shift towards recognising the coaching workplace as a legitimate site for professional knowledge development. Building upon contemporary studies of learning ‘in situ’, this paper draws upon the theory of practice architectures to provide an innovative language by which to capture the complexity of learning within this context. In doing so, the cultural-discursive, material-economic, and socio-political arrangements of practice are shown to either enable or constrain learning activities. Findings from a 10-month ethnographic study of a high-performance training centre (n= 9 coaches/support staff), highlighted the significant role the macro-structural features of sport, and the inherent ‘learning culture’, played in determining the learning valued within this context. This study draws attention to the challenges a transient coaching workforce, within a dynamic environment, presents to those attempting to foster learning in this context.

High Performance Geological Disaster Recognition using Deep Learning

Geological disaster recognition on optical image is one of the key techniques in disaster control and disaster relief. Comparing with optical images, remote sensing images contain much higher resolution and more visualized contents. In this paper, we propose a landslide recognition framework which trains a deep auto-encoder network on the compressed domain. ANN or SVM is used as the classifier for decision making. In addition, in order to meet the requirement of some real-time applications, a high performance training network on CUDA-enabled GPUs is designed and implemented. Experiments are conducted on optical images from Google Earth.

Analysis of the influence of plyometric training in improving the performance of athletes in rhythmic gymnastics

Rhythmic gymnastics (RG) athletes need high-performance training since a high degree of precision is required in their exercises. Plyometric training (PT) has been used to improve athletes' neuromuscular function, explosive performance and strength in competition. The object of this study was to assess the efficiency of PT in improving the performance of RG athletes in the juvenile and adult categories over 12 months, by incorporating PT into two training macrocycles. Thirty athletes were selected; they were divided randomly into a control group (CG) and an experimental group (EG) of 15 athletes each. Two 6-month training macrocycles were drawn up for the EG in which they maintained normal training with the addition of PT. The CG maintained its normal training. Three tests were used for the assessment: vertical jump, horizontal jump and agility, assessed at 5 different moments. After 12 months it was observed an improvement in test performance in both groups, with a more significant improvement in the EG when compared to the CG. The addition of PT to normal training improved the performance of athletes by developing greater power in the lower limbs, increasing their capacity in vertical jump, horizontal jump and agility.

Development of a practical high frequency brain–computer interface based on steady-state visual evoked potentials using a single channel of EEG

Brain–computer interfaces based on steady-state visual evoked potentials have recently gained increasing attention due to high performance and minimal user training. Stimulus frequencies in the range of 4–60Hz have been used in these systems. However, eye fatigue when looking at low-frequency flickering lights, higher risk of induced epileptic seizure for medium-frequency flickers, and low signal amplitude for high-frequency flickers complicate appropriate selection of flickering frequencies. Here, different flicker frequencies were evaluated for development of a brain–computer interface speller that ensures user's comfort as well as the system's efficiency. A frequency detection algorithm was also proposed based on Least Absolute Shrinkage and Selection Operator estimate that provides excellent accuracy using only a single channel of EEG. After evaluation of the SSVEP responses in the range of 6–60Hz, three stimulus frequency sets of 30–35, 35–40 and 40–45Hz were adopted and the system's performance and corresponding eye fatigue were compared. While the accuracy of the asynchronous speller for all three stimulus frequency sets was close to the maximum (average 97.6%), repeated measures ANOVA demonstrated that the typing speed for 30–35Hz (8.09char/min) and 35–40Hz (8.33char/min) are not significantly different, but are significantly higher than for 40–45Hz (6.28char/min). On the other hand, the average eye fatigue scale for 35–40Hz (80%) is comparable to that for 40–45Hz (85%), but very higher than for 30–35Hz (60%). Therefore, 35–40Hz range was proposed for the system which resulted in 99.2% accuracy and 67.1bit/min information transfer rate.

A preliminary investigation into the discriminant and ecological validity of the athletic ability assessment in elite Australian rules football

Objectives: To establish the discriminant validity of the athletic ability assessment in elite Australian rules football. Secondarily, to examine the association between athletic ability assessment and maximum running velocity extracted from elite Australian rules football game-play. Methods: To establish the discriminant validity of the athletic ability assessment, 43 Australian Football League players from one club were split into two groups based upon playing status; ‘starters’ ( n = 17; selected in 50% of the first 10 games of the Australian Football League season), ‘non-starters’ ( n = 26; not selected in 50% of the first 10 games of the Australian Football League season). Each group performed a modified version of the athletic ability assessment consisting of five foundational athletic movements. An independent-sampled t-test modelled the effect of player group (starters; non-starters) on the total score and for each individual exercise. Pearson product moment correlation was also conducted to establish the association between maximum running velocity and athletic ability assessment. Results: The starters attained a significantly greater total mean score on the athletic ability assessment ( d = 1.04, p < 0.05), overhead squat ( d = 0.96, p < 0.05), double lunge left ( d = 0.64, p < 0.05), single leg Romanian deadlift left ( d = 1.19, p < 0.05) and right single leg Romanian dead lift ( d = 0.79, p < 0.05) relative to the non-starters. Also noted were moderate, positive correlations between maximum running velocity and athletic ability assessment ( r = 0.31–0.46; p < 0.05). Conclusions: The results support the use of the athletic ability assessment to measure movement competency in elite Australian rules football. Accordingly, physical development coaches may consider its integration in high-performance training programs.

Long-term effects of gradual shoe drop reduction on young tennis players’ kinematics

Worldwide, tennis is practiced by millions of children, several thousands being intensive players. The participation in high-performance training programmes and competition places them at risk to e...

Aortic Root Size in Elite Male Athletes with Bicuspid Aortic Valve

INTRODUCTION: The bicuspid aortic valve (BAV) is the most common congenital cardiac malformation. However there is a lack of studies relating the effect of high performance training and the size of the aortic root in elite athletes with BAV. PURPOSE: Evaluate the impact of BAV on aortic root remodeling in elite male athletes. METHODS: A retrospective cross-sectional study including 28 Spanish elite male athletes with BAV diagnosed by transthoracic echocardiography (age 23.2 ± 5.6 years, BSA 1.96 ± 0.16 m2, 8.7 ± 4 years/training and 17.2 ± 8.5 hours/day of training) were studied to assess end-diastolic aortic diameters in the parasternal long axis at: aortic annulus (AA), sinus of Valsalva (SV), sinotubular junction (SJ) and the maximum diameter of the proximal ascending aorta (AsPx); using the inner edge-to-inner edge (I-I) convention. The values were compared with a control group composed of 2041 healthy Spanish elite male athletes with normal tricuspid aortic valve (TAV) (age 24.1 ± 5.8 years, BSA 1.9 ± 0.2 m2, 9.6 ± 5.1 years/training and 19.2 ± 8.7 hours/day of training). Also the control group was used to establish the physiological limits of aortic remodeling associated to exercise training in elite athletes. RESULTS: BAV was associated with increased aortic dimension in comparison to the TAV at the level of AA (28.6 ± 4.8 mm vs 25.5 ± 3.3, p=0.001), SV (35.6 ± 5.7 vs 31.6 ± 3.6 mm, p=0.0001), SJ (29.7 ± 5.2 vs 26.4 ± 3.4 mm, p=0.0001) and AsPx (31.9 ± 6.9 vs 26.9 ± 3.6 mm, p=0.0001). Indexed aortic diameter was also increased in BAV at the level of AA (14.6 ± 2.5 vs 13.1 ± 1.7 mm/m2, p=0.002), SV (18.3 ± 3.2 vs 16.3 ± 1.9 mm/m2, p=0.001), SJ (15.2 ± 2.9 vs 13.6 ± 1.8 mm/m2, p=0.003) and AsPx (16.4 ± 3.8 vs 13.9 ± 1.9 mm/m2, p=0.0001). The dimensions of the aortic root in male elite athletes with BAV exceeding the upper normal range limit based on control group measurements were observed in 11 athletes at AA (39.3%), 11 at SV (39.3%), 8 at SJ (28.6%) and 8 at AsPx (28.6%). From this cohort, 7 elite male athletes with BAV (25%) had an aortic diameter greater than 40 mm at any level of measurement. CONCLUSIONS: Diameters of the aortic root in elite male athletes with BAV are greater than athletes with TAV at all levels of measurements. Almost two fifths of the athletes with BAV are over the physiological limit of the aortic root size.

CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

The channel matched filter (CMF) is the opti- mum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF con- volved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the in- verse of the autocorrelation function can be used to equal- ize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind esti- mation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast conver- gence because its performance is independent of the char- acteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques.

The impact of intensive high performance training on adult height of female artistic gymnasts: a retrospective study

Abstract The purpose of this study was to identify the potential impact of the high performance training on the anthropometric and maturational characteristics of female artistic gymnasts and to compare the adult target height with international standards of growth. A sample consisted of 23 elite Brazilian female former gymnasts was evaluated. From a historic retrospective, about 29.56 yrs. ago (average of all) they devoted themselves to the training for 9.3 (2.6) years, with a weekly 24.26 (4.2) training hours, and had been retired from competition around at 13.61 (5.12) years old. From the anthropometric data of the gymnasts (while still competing), their parents and their older sister (1) and younger sister (2), the target height was calculated by Tanner method {[(father’s height -13 cm) + mother’s height] ÷ 2}. Additionally, training history, age of menarche occurrence, and growth classifying in accordance with the international standards was recorded, aimed at comparisons. The average stature shown normal adult stature (NCHS), and some cases surpassed the standard 75th, 90th and 95th percentile. They are higher than their mother (p = 0.039), but not their sister 1 (0.952) or sister 2 (p = 0.998), but the age of menarche was significantly later only than their sisters 1 (p = 0.008) and 2 (p = 0.017). According to the Brazilian percentiles reference (IBGE), the final height of the former gymnasts was always higher of the standard, slightly smaller than their sisters 2 (5th and 10th), but was taller than their mothers and sisters 1. In conclusion, there is no evidence of artistic gymnastics high performance training having adverse effects on the final adult height.

A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain)

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in surface waters used for drinking or for recreation. Additionally, cyanobacteria blooms are often called blue–green algae. A harmful algal bloom (HAB) is an algal bloom that causes negative impacts to other organisms via production of natural toxins such as cyanotoxins. Consequently, anticipating its presence is a matter of importance to prevent risks. This study presents a novel hybrid algorithm, support vector machines with Mexican hat wavelet kernel function (wavelet SVMs) in combination with the artificial bee colony (ABC) technique, for predicting the cyanotoxin content from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training center of canoeing in the Northern Spain). This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, cyanotoxin contents have been predicted here by using the hybrid wavelet ABC–SVM-based model from the remaining measured water quality parameters (input variables) in the Trasona reservoir (Northern Spain) with success. In other words, the results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variable on the cyanotoxin content in the reservoir is presented through the model. Secondly, a predictive model able to predict the possible presence of cyanotoxins is obtained. The agreement of the wavelet ABC–SVM-based model with experimental data confirmed its good performance. Indeed, a coefficient of determination equal to 0.91 was obtained. Finally, conclusions of this innovative research work are exposed.

Cloud Management Tools for Sustainable SMEs

The purpose of this present work is to share the new developments of manufacturing support services. They are based on a set of tools available and accessible through Internet, and mainly oriented to Small and Medium Enterprises (SMEs). This set of manufacturing and business tools is comprised of three main streams: Computer-aided engineering (CAE) software packages, a Product Life Cycle (PLM) tool, and an Open Innovation (OI) platform. These services are the output of the CloudPyme2 project (CPP2). It has the main goal of providing innovation tools for product development and manufacturing processes to SMEs in a sustainable and affordable way. This project provides these tools as an infrastructure using Cloud Services, including High Performance Computing (HPC), hosting platforms, support services and training. This new platform allows SMEs to improve their products and processes, or create new ones, in an affordable and efficient way.

A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins termed cyanotoxins and, as a result, anticipate its presence is a matter of importance to prevent risks. Cyanobacteria blooms occur frequently and globally in water bodies, and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Therefore, the aim of this study is to build a cyanotoxin diagnostic model by using support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training center of canoeing in the Northern Spain). The Trasona reservoir is near Aviles estuary and after a short tour, the brackish waters of the Aviles estuary empty into the Cantabrian sea. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, cyanotoxin contents have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured water quality parameters (input variables) in the Trasona reservoir (Northern Spain) with success. In other words, the results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variable on the cyanotoxin content in the reservoir is presented through the model. Second, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the PSO–SVM-based model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

Productivity and quality management – education and training for high performance

The paper discusses the relationship between productivity and quality in a performance measurement and management context. The important input of knowledge to the business process must be continuously nurtured through appropriate education and training. Productivity improvement is shown to be linked to technological advance and to knowledge and resources. The progress from benchmarking to bench–learning in productivity and quality management is expounded. Recommendations are given for the development of intellectual assets in the pursuit of increased efficiency of operations and creating value to the customer.

A longitudinal assessment of adolescent student-athletes’ school performance

In Germany, extraordinarily talented student-athletes are offered the special school setting of elite schools of sport (ESSs). These schools seek to optimally adjust the daily affordances of athletic high-performance training with those of regular school education. The aim of the present analysis was to empirically explore whether elite student-athletes’ school grades develop differently than those of fellow students who dropped out of the ESS’s athletic program. One cohort of student-athletes (N = 260) was tracked from the moment of their enrollment in the ESS until the end of lower secondary school (from school level 7 to 10). Multilevel modeling was used to analyze this longitudinal dataset. Elite student-athletes who manage to still meet their school’s athletic standards after class level 10 (n = 158) came to their ESSs with better school grades in mathematics and German from primary school than program dropouts (n = 102). Additional comparisons show that after class level 10, all ESS students’ school grades in mathematics and German were not significantly different from the federal state’s general average at schools without high-performance sport programs. Findings are interpreted against the background of the hypothesis that the system of high-performance sports tends to produce an instance of total commitment and that educational disadvantages for ESS student-athletes could follow as a consequence.

MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era

In the big data era, extreme learning machine (ELM) can be a good solution for the learning of large sample data as it has high generalization performance and fast training speed. However, the emerging big and distributed data blocks may still challenge the method as they may cause large-scale training which is hard to be finished by a common commodity machine in a limited time. In this paper, we propose a MapReduce-based distributed framework named MR-ELM to enable large-scale ELM training. Under the framework, ELM submodels are trained parallelly with the distributed data blocks on the cluster and then combined as a complete single-hidden layer feedforward neural network. Both classification and regression capabilities of MR-ELM have been theoretically proven, and its generalization performance is shown to be as high as that of the original ELM and some common ELM ensemble methods through many typical benchmarks. Compared with the original ELM and the other parallel ELM algorithms, MR-ELM is a general and scalable ELM training framework for both classification and regression and is suitable for big data learning under the cloud environment where the data are usually distributed instead of being located in one machine.

대피선 최소화를 고려한 광역철도 급행화 방안 연구

In metropolitan railway systems, the average commuting time keeps increasing as the scheduled speed increases, and this leads to a decline of rail service usage and competitiveness. Therefore, effective express operation for urban trains is required to improve the scheduled speed. In this study, based on the obtainable time shortening efficiency and economic viability, several express operations are suggested for urban railways and these suggestions are compared by considering high performance trains with acceleration/deceleration and maximum speed improvement. As a result, the optimum express system, which can minimize the cost for sidetrack construction, is suggested.

Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

Cyanobacteria also known as blue-green algae can be found in almost every conceivable environment. Cyanobacteria blooms occur frequently and globally in water bodies and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Consequently, anticipation of cyanotoxins presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines and multilayer perceptron networks from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training centre of canoeing in the Northern Spain). The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxins presence in the reservoir is presented through the model. Secondly, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

2P1-B10 歩行訓練用高機能靴に関する研究(福祉ロボティクス・メカトロニクス(3))

One of the most popular reasons that fall down of elderly person is a gap of the center of balance due to power down of muscle for aging. So we began to product new rehabilitation system that treat a gap of the center of the balance. We produced high-performance training shoes that shows status of walking balance. A method of communication that show status of walking balance is using tablet, Head Mounted Display and WiPort. The software of those devices are android because it is very easy to use. Further, we also produced an evaluation device for walking due to clear basic characteristics of the developed shoes. A source of power of this device is air pressure that we can revive smooth action of human. We try to control this device using method of master-slave control. Master device is goniometer that measure human joint angle. By using the master-slave system, we would not only evaluate high-performance training shoes but also analysis complicated walking of human.

Hybrid fuzzy control of wind turbine generator by pitch control using RNN

This article presents the design of a hybrid fuzzy sliding mode loss-minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training recurrent neural network (RNN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low- and high-wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.

Organization and multidisciplinary work in an olympic high performance centers in USA

The organization and methodology of providing services to athletes through Olympic high performance centers varies among the National Olympic Committees (NOC). Between NOCs, provider composition and methodology for the delivery of services differs. Services provided typically include sports medicine and sports performance. NOCs may provide service through a university-based system or high performance centers. The United States Olympic Committee (USOC) provides services using multiple approaches through a hybrid model that includes three Olympic Training Centers, National Governing Bodies (NGB) high performance centers and independent specialty care centers. Some highly developed National Governing Bodies have dedicated high performance training centers that serve only their sport. The model of sports medicine and sports performance programming utilized by the USOC Olympic Training Centers is described in this manuscript.