Training Speed Research Articles

Impact Of Combined Aerobic, Plyometric, And Speed Training On Aggression And Self-Confidence In Indian Secondary School Athletes.

Impact Of Combined Aerobic, Plyometric, And Speed Training On Aggression And Self-Confidence In Indian Secondary School Athletes.

Efficient image generation with Contour Wavelet Diffusion

The burgeoning field of image generation has captivated academia and industry with its potential to produce high-quality images, facilitating applications like text-to-image conversion, image translation, and recovery. These advancements have notably propelled the growth of the metaverse, where virtual environments constructed from generated images offer new interactive experiences, especially in conjunction with digital libraries. The technology creates detailed high-quality images, enabling immersive experiences. Despite diffusion models showing promise with superior image quality and mode coverage over GANs, their slow training and inference speeds have hindered broader adoption. To counter this, we introduce the Contour Wavelet Diffusion Model, which accelerates the process by decomposing features and employing multi-directional, anisotropic analysis. This model integrates an attention mechanism to focus on high-frequency details and a reconstruction loss function to ensure image consistency and accelerate convergence. The result is a significant reduction in training and inference times without sacrificing image quality, making diffusion models viable for large-scale applications and enhancing their practicality in the evolving digital landscape.

Analytical learning classifier based on predefined evenly-distributed class centroids

For the pattern recognition, most classification models are solved iteratively, except for Linear LDA, KLDA and ELM etc. In this paper, a nonlinear classification network model based on predefined evenly-distributed class centroids (PEDCC) is proposed. Its analytical solution can be obtained and has good interpretability. Using the characteristics of maximizing the inter-class distance of PEDCC and derivative weighted minimum mean square error loss function to minimize the intra-class distance, we can not only realize the effective nonlinearity of the network, but also obtain the analytical solution of the network weight. Then, the sample is classified based on GDA. In order to further improve the performance of classification, PCA is used to reduces the dimensionality of the original sample, meanwhile, the CReLU activation function are adopted to enhances the expression ability of the features. The network transforms the samples into the higher dimensional feature space through the weighted minimum mean square error, so as to find a better separation hyperplane. In experiments, the feasibility of the network structure is verified from pure linear 𝑾, 𝑾+Tanh, and PCA+𝑾+Tanh respectively on many small data sets and large data sets, and compared with SVM and ELM in terms of training speed and recognition rate. The results show that, in general, this model has advantages on small data set both in recognition accuracy and training speed, while it has advantages in training speed on large data sets. Finally, by introducing a multi-stage network structure based on the latent feature norm, the classifier network can further significantly improve the classification performance, the recognition rate of small data sets is effectively improved and much higher than that of existing methods, while the recognition rate of large data sets is similar to that of SVM.

Quantifying the impact of rail longitudinal level changes on dynamic load magnitudes through instrumented wheelset measurements

Track geometry significantly influences train-track dynamic interactions, primarily due to irregularities at the wheel-rail interface. These irregularities generate dynamic load excitations, leading to defects and damage. This study evaluates how track surface profile longitudinal level vertical changes affect vertical wheel-to-rail loads, quantified by the dynamic load factor ( ϕ ), due to the importance of ϕ in track design. A limitation of the existing studies is their focus on instrumented sections and numerical modellings without considering different track structures. This study conducted dynamic load measurements using two instrumented wheelsets (IWS) over a 340 km section operated by a North American Class 1 freight railway in the Canadian Prairies under various track structures and train speeds. Rail surface longitudinal level measurements were obtained as routine measurements through a track geometry car. The paper presents statistical distributions of rail longitudinal level across different track structures and evaluates two widely used and recently developed ϕ equations within the context of North American freight railways to quantify the impact of rail longitudinal level (i.e. profile) changes on dynamic load magnitudes. The study also evaluates regulated threshold values by considering observed trends in the measured data, which aims to quantify and compare these values with our current understanding.

A Multi-Model Architecture Based on Deep Learning for Longitudinal Available Overload Prediction of Commercial Subsonic Aircraft with Actuator Faults

Assessing the real-time longitudinal available overload onboard under fault conditions offers vital insights for the fault-tolerant reconfiguration and trajectory planning of commercial subsonic aircraft. After actuator failures in a commercial subsonic aircraft, its aerodynamic model undergoes changes. Traditional methods based on analytical models rely on precise aerodynamic models. However, due to the complexities of the flight environment and uncertainties in disturbances, establishing an accurate aerodynamic model after actuator failures is often challenging. Consequently, traditional methods can yield significant errors when evaluating the available overload under actuator faults. To address this, we introduce a multi-model architecture based on deep learning for the longitudinal available overload prediction of a commercial subsonic aircraft with actuator faults. For flight state data under different working conditions and different faults, Spearman correlation coefficient analysis and the gradient boosting decision tree (GBDT) algorithm are used to remove redundant feature parameters, thereby enhancing the training and prediction speed of the model while reducing the risk of overfitting. To meet prediction accuracy and speed demands, we employ the multi-layer perceptron (MLP) deep learning network to fully explore the environmental features, including uncertainties and disturbances, within the flight state, and the mapping relationships between the flight state and the available overload variations. We incorporate the light gradient boosting machine (LightGBM) and the categorical boosting (CatBoost) algorithms to enhance the model’s prediction speed and fuse it with a longitudinal available overload analytical model to elevate the model’s prediction accuracy, thereby achieving the real-time estimation of the commercial subsonic aircraft’s longitudinal available overload with actuator faults. The results demonstrate that the proposed method achieves a higher accuracy than traditional methods, with a relative error of less than 5%.

General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.

The use of functional training methods to increase speed and strength training-karate news

Relevance. Nowadays, successes in the sports training of martial artists play a decisive role, and without systematic physical training from an early age it becomes difficult to count on outstanding results in a sports career. The importance of issues related to the physical training of young athletes cannot be overestimated, since not only the development of physical qualities of athletes, but also their future success in technique and tactics depends on how effectively they will be solved.The purpose of the research: is to develop and test an effective functional training program adapted to increase the speed and strength training of karate students at the stage of in-depth specialization.Research methods and organization: analysis of scientific and methodological literature, method of pedagogical observation, pedagogical testing, pedagogical experiment, methods of mathematical statistics.Conclusions. Training programs aimed at developing physical abilities at a young age should take into account not only the aspects of strength and endurance, but also focus on the development of coordination, flexibility and reaction, which play an important role in karate. It is these fundamental qualities that determine further technical progress and the overall skill level of athletes. Consequently, scientific research aimed at developing optimal physical training techniques for young karate players is an important stage in improving the educational and training process and contributes to the formation of highly qualified athletes capable of achieving outstanding results in the international arena.

PreparedLLM: effective pre-pretraining framework for domain-specific large language models

ABSTRACT The direct application of large language models (LLMs) to specific domain tasks frequently encounters challenges due to the scarcity of domain data, variations in domain semantics, and the complexity of domain knowledge. Further pretraining of advanced foundational models on extensive domain-specific corpora can infuse these models with domain-specific knowledge and enhancing their ability of solving domain-specific tasks. However, the development of most domain-specific models focuses primarily on collecting large-scale domain data, often overlooking the crucial optimization of the pre-pretraining stage, which significantly impacts both model performance and training efficiency. This paper introduces PRE -PretrAining FRamEwork for Domain-specific Large Language Models (PreparedLLM), a framework designed to enhance the pre-pretraining stage for domain specialization of LLMs. PreparedLLM employs advanced techniques in data recipe, data cleaning, vocabulary expansion, and embedding initialization. These techniques are implemented to optimize both the composition and quality of the training data, and to enhance understanding of domain terminologies and concepts, as well as improve token embedding initialization. Utilizing the geoscience domain as a case study, this paper applies PreparedLLM for the domain specialization of the Llama, a widely recognized general-purpose LLM. Experimental results demonstrate that PreparedLLM enhances model convergence speed, training speed, inference speed, the text volume of the context window, and overall performance in domain specialization. The utilization of PreparedLLM in developing domain-specific LLMs has significantly increased performance while reducing both time and resource investment. The case study provides valuable insights into the development of domain-specific LLMs.

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

Short-term multi-energy consumption forecasting for integrated energy system based on interactive multi-scale convolutional module

Accurate consumption forecasting is of great importance to grasp the energy consumption habits of consumers and promote the stable and efficient operation of integrated energy system (IES). To this end, this paper proposes an interactive multi-scale convolutional module-based short-term multi-energy consumption forecasting method for IES. Firstly, based on multi-scale feature fusion and multi-energy interactive learning, a novel interactive multi-scale convolutional module is proposed to extract and share the coupling information between energy consumption from different scales without increasing network parameters. Then, a short-term multi-energy consumption forecasting method is proposed, where different forecasting network structures are selected in different seasons to make full use of seasonal and coupling characteristics of the energy consumption, thus enhancing prediction performance. Furthermore, a Laplace distribution-based loss function weight optimization method is proposed to dynamically balance the loss magnitude and training speed of joint forecast tasks more robustly. Finally, the effectiveness and superiority of the proposed method are verified by comparative simulation experiments.

Developing Business Strategies to Grow the Business of PT Asia Civil Indonesia

The significant market growth translate into business opportunity for construction such toll road, office building, bridge, LRT, High speed train, including huge opportunity in IKN and data centre with potential The Indonesia Construction Market size is estimated at USD 264.34 billion in 2023, and is expected to reach USD 379.41 billion by 2028, growing at a CAGR of 7.50% during the forecast period (2023-2028). This opportunity in industry up trend but no impact in growth PT Asia Civil Indonesia and company need to renew and review their focus on business model development with business strategy approach and need to evaluation they are market growth and cost structure. In qualitative research utilizing depth interviews, the research design serves as the blueprint guiding the investigation’s execution. The primary objective of this research approach is to delve deeply into participants’ thoughts, feelings, and experiences to gain rich and nuanced insights into the phenomena under study. In conclusion, the research conducted on PT ACI underscores the importance of strategic initiatives in enhancing its business operations within the construction industry. Through a comprehensive analysis of current business practices and market dynamics, PT ACI can identify areas for improvement and leverage opportunities for growth. The implementation plan outlined in the study emphasizes the significance of networking strategies and technology adaptation in achieving business objectives. Networking plays a pivotal role in expanding PT ACI’s reach within the industry, facilitating valuable connections with potential clients, partners, and industry peers. By actively participating in industry events, forums, and networking opportunities, PT ACI can build relationships, gain insights into market trends, and secure new business opportunities.

Artificial neural network-based sound insulation optimization design of composite floor of high-speed train

Increasing the speed of high-speed trains requires the lightweight design of vehicles to meet the economic and ecological efficiency requirements of such trains. However, these objectives conflict with the interior noise control in high-speed trains because the sound insulation of panel structures follows the mass law principle. The train floor, the main train body structure of the high-speed train, is vital for interior noise control because its sound insulation performance directly affects the interior noise levels. Owing to the complexity of the composite floor system, reliable measurement and accurate estimation of its sound insulation performance are often time-consuming and laborious. To address this situation, this study proposes an artificial neural network (ANN)-based model to predict the sound insulation characteristics of a composite floor. First, a sound insulation model of the composite floor is built based on statistical energy analysis (SEA). The sound insulation performance of 200 cases of composite floors is calculated by varying the dimensions of the extruded floor, thickness of the webs, sound-absorbing material, and wooden floor to formulate a sound insulation database of composite floors. Next, an ANN model is introduced and trained on the sound insulation database. The sound insulation prediction results obtained using the ANN model are compared to the prediction results obtained using the experiment to validate its effectiveness. Subsequently, the NSGA-II optimization method is used to optimize the sound insulation structure of the composite floor. Compared with the regular composite floor structure, the optimized structure reduced the mass of the composite floor by 10.93 kg and increased the weight of the sound insulation ( Rw) by 6.3 dB. The proposed method can be an effective, economical, and efficient tool for vehicle designers and can help promote the sound insulation optimization design of high-speed train composite floors.

Effect of Speed, Agility, and Quickness Training on Linear Sprint, Jump, and Change of Direction Speed Performance in Soccer Players: A Systematic Review and Three-Level Meta-Analysis

Effect of Speed, Agility, and Quickness Training on Linear Sprint, Jump, and Change of Direction Speed Performance in Soccer Players: A Systematic Review and Three-Level Meta-Analysis

Highly textured CMOS-compatible hexagonal boron nitride-based neuristor for reservoir computing

Significant advancements in artificial neural networks (ANNs) have driven the rapid progress of artificial intelligence and machine learning. While current feedforward neural networks primarily handle static data, recurrent neural networks (RNNs) are designed for dynamical systems. However, RNNs demand extensive training on specific tasks, limiting their scalability and affordability for edge computing. Physical reservoir computing (RC) offers an alternative approach by mapping inputs into high-dimensional states, allowing for pattern analysis within a fixed reservoir. Unlike RNNs, RC is well-suited for temporal and sequential data processing with rapid speed and low training costs. This makes RC suitable for hardware implementation across various research domains. Nonetheless, existing demonstrations of RC remain constrained to small-scale device arrays. As electronic synapse arrays aim to approach very large-scale and highly complex hardware as in the human brain, managing heat dissipation becomes a formidable challenge. In this work, we successfully developed the neuristors based on textured h-BN films, prepared using a CMOS-compatible technique, and constructed a physical RC system based on as-fabricated devices. Our approach leverages vertically aligned BN to provide aligned diffusion paths for the reproducible migration process of metal ions from the electrodes and offers a potential solution for thermal management in electronic devices. This achievement highlights the promising potential of our neuristors for future high-density and energy-efficient neuromorphic computing.

Toward effective SVM sample reduction based on fuzzy membership functions

Support vector machine (SVM) is known for its good generalization performance and wide application in various fields. Despite its success, the learning efficiency of SVM decreases significantly originating from the assumption that the number of training samples increases rapidly. Consequently, the traditional SVM with standard optimization methods faces challenges such as excessive memory requirements and slow training speed, especially for large-scale training sets. To address this issue, this paper draws inspiration from the fuzzy support vector machine (FSVM). Considering that each sample has varying contributions to the decision plane, we propose an effective SVM sample reduction method based on the fuzzy membership function (FMF). This method uses FMF to calculate the fuzzy membership of each training sample. Training samples with low fuzzy memberships are then deleted. Specifically, we propose SVM sample reduction algorithms based on class center distance, kernel target alignment, centered kernel alignment, slack factor, entropy, and bilateral weighted FMF, respectively. Comprehensive experiments on UCI and KEEL datasets demonstrate that our proposed algorithms outperform other comparative methods in terms of accuracy, F-measure, and hinge-loss measures.

Research on Rail Surface Defect Detection Based on Improved CenterNet

Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection.

Estimation of Lactate Thresholds, Aerobic Capacity and Recovery Rate from Muscle Oxygen Saturation in Highly Trained Speed Skaters and Healthy Untrained Individuals.

Objective: The main objective of this study was to compare lactate thresholds and aerobic capacity from a graded-intensity exercise test (GXT) for near-infrared spectroscopy measurements in healthy, untrained individuals and highly trained athletes. Methods: This study included 29 untrained students (13 females) and 27 highly trained speed skaters (13 females). A maximal effort GXT was performed on a cycloergometer. The lactate-based aerobic and anaerobic thresholds, and the corresponding thresholds for muscle oxygen saturation (SmO2), were determined. Results: The power values determined for all thresholds were significantly higher in female and male speed skaters compared to male and female college students. SmO2 at anaerobic thresholds was significantly lower in female speed skaters than in female students. Both female and male skaters showed greater changes in SmO2 after the GXT compared to students. The recovery did not significantly differ between groups within gender. There was a significant positive correlation in females between the rate of muscle reoxygenation and VO2max power (r = 0.610). In speed skaters, the rate of muscle reoxygenation was not significantly higher than students and correlated positively with VO2max (r = 0.449). Conclusions: The SmO2 at the exercise thresholds, during and after maximal exercise, depends on the training status of the individual. The participants with a higher physical fitness level showed greater decreases in ΔSmO2 at the AT level, as well as after maximal exercise. SmO2 corresponding to the well-established exercise thresholds may be applied to guide training prescription. The rate of muscle reoxygenation after a GXT was also dependent on the aerobic capacity of the participants.

Pulsar classification: comparing quantum convolutional neural networks and quantum support vector machines

Well-known quantum machine learning techniques, specifically quantum kernel-assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparative study, it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. While QCNNs are superior in terms of training and prediction speed compared to QSVMs, the preference shifts toward QSVMs when the present noisy NISQ-era devices are incorporated into the comparison. QSVMs demonstrate superior overall performance compared to QCNNs when assessed using binary classification performance metrics. Classical methods are implemented to serve as a benchmark for comparison with the quantum approaches.

Long railway track modelling – A parallel computing approach

This paper presents the development of a dynamics model for long track sections. It is based on an established short track model that utilises the Finite Element Method to describe rails and block models to describe sleepers, ballast and subballast. By implementing a parallel computing method, this innovation enables the construction of a true long track model: by segmenting the long track into shorter segments that are easier to compute. The model facilitates simulations to be run in parallel, thereby permitting simultaneous calculations of various numerical track variables. The model employs a Message Passing Interface framework to seamlessly link the track segments, handling the flow of data among the computing cores designated to each subdivided section. This strategic framework allows the long track model with the capability to simulate tracks of virtually any length, with the only constraints being the available computational resources and time. The claimed contribution about modelling capability is verified using two case studies on a 6km-long track involving different practical and conceptual train operational scenarios: emergency braking and constant braking force with constant train speed. These case studies show the flexibility and scalability of the method and its capability to handle complex track dynamic systems.

Vibration based dual-criteria damage detection method using deep neural networks in highway bridges with steel girders

This article introduces a new method for assessing the damage in steel girders of highway bridges using deep neural networks. The method uses modified forms of modal strain energy damage index and modal flexibility damage index to train the neural network and prevent unsafe decisions. The neural network is trained using damage indexes achieved from numerical simulations of the bridge with different damage scenarios. Three types of neural networks, including convolutional neural network (CNN), Long short-term memory (LSTM) neural network, and conventional artificial neural network (ANN) are compared to achieve the best performance. The proposed method overcomes previous detection problems such as false-positive indications, limited damage locations, and scenarios, and insufficient accuracy in cases with multiple damage scenarios. The results show that the proposed method and CNN can accurately identify unspecified damage locations and severity in multi-span highway bridges. The new training method of the CNN deep neural network systems overcomes some shortcomings in ANN, such as being time-consuming, having a low convergence rate, and experiencing data overfitting. Additionally, the CNN training scheme can limit the need for massive amounts of input data and increase the speed and accuracy of network training, especially in multiple damage scenarios.