Power Train Research Articles

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X-ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitivenessin a better competitiveness.

Functional mobility and physical fitness are improved through a multicomponent training program in institutionalized older adults

Physical exercise has demonstrated its effectiveness in the management of the deleterious process of aging. However, it is less studied in institutionalized elderly people. This investigation aims to clarify the benefits of a multicomponent training program in institutionalized older adults. A randomized controlled trial was conducted with institutionalized older adults (≥ 70 years old). Intervention group (IG; N = 18) were submitted to a multicomponent training program based on muscle power training and interval endurance exercise, 2 times/week for 12 weeks. Control group (CG; N = 16) continued their usual mobility exercises. Independence was estimated with the Barthel index, and physical fitness and functional mobility were evaluated by the Short Physical Performance Battery (SPPB), the Timed Up and Go (TUG) test, the 6-min Walking Test (6’WT), the 10-Meter Walking Test (10MWT), hand grip strength dynamometry, and lower limb muscle strength and power. The IG improved, compared with the CG, in TUG scores in -7.43 s (95% IC: 3.28, 11.59; p < 0.001); in 10MWT scores in -5.19 s (95% IC: 1.41, 8.97; p = 0.004) and -4.43 s (95% IC: 1.14, 7.73; p = 0.002), 6’WT scores in + 54.54 m (95% IC: 30.24, 78.84; p < 0.001); and SPPB in + 2.74 points (95% IC: 2.10, 3.37; p < 0.001). Maximum muscle power and maximum strength did not show statistically significant differences. The multicomponent training program based on muscle power and interval endurance exercise was shown to be safe, well tolerated and effective for the improvement of functional mobility and physical fitness, but not for independence in institutionalized older adults.

Network Meta-analysis of Combined Strength and Power Training for Countermovement Jump Height.

Combined strength and power training in a training program is considered to improve the vertical jump, which is frequently quantified using the countermovement jump height. It is not yet clear whether one of the different training set structures, such as complex training, contrast training, compound training and traditional training, is superior to another. The aim of this review is to describe and assess the comparative effects of the set structures on countermovement jump height in healthy subjects. A systematic review and network meta-analysis (NMA) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Network Meta-Analyses. Three databases were systematically searched. Risk of bias was assessed using the Risk of Bias 2 tool. NMAs were performed using a random-effects model. Twenty-four studies were included. All interventions were superior to control (no intervention) with mean differences ranging from 2.87 [95% confidence interval (CI): 1.99 to 3.74] for complex training to 3.43 (95% CI: 2.61 to 4.26) for traditional training. None of the training interventions were superior compared to each other in strength and/or power trained subjects, as well as in non-strength and/or power trained subjects. The findings support the combination of strength and power training to improve countermovement jump height.

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model’s performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the past 15 years. We start by categorizing the current threat models and attacks and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning and shed light on the current limitations and open research questions in this research field.

Simulation as a key training method for inculcating public health leadership skills: a mixed methods study.

Successful management of public health challenges requires developing and nurturing leadership competencies. We aimed to evaluate the effectiveness of training simulations to assess public health leadership and decision-making competencies during emergencies as an effective learning and training method. We examined the effects of two simulation scenarios on public health school students in terms of their experience (compared to face-to-face learning) and new skills acquired for dealing with similar emergent situations in the future. A mixed-methods design included developing a validated and pre-tested questionnaire with open-and closed-ended questions that examined the simulation impact and the degree of student satisfaction with the conditions in which it was conducted. Semi-structured in-depth interviews were conducted with the students after going through the simulations. The questionnaire results were evaluated using descriptive analytics. The interviews were analyzed using thematic analyses. All data were collected during June 2022. The questionnaire results indicate that students strengthened their interpersonal communication skills and learned about the importance of listening to the opinions of others before formulating their positions. Four themes emerged from 16 in-depth interviews, according to Kolb's experimental learning cycle. Students emphasized the effectiveness of experiential learning versus traditional classroom learning. The simulation scenarios were felt to realistically convey critical issues regarding leadership, decision-making, and teamwork challenges. They effectively conveyed the importance of building a culture of conducting substantive and respectful discussions. Simulation is a powerful pedagogical training tool for public health leadership competencies. Simulations were seen to be advantageous over face-to-face learning in imparting a range of leadership skills and hands-on practice. We recommend integrating simulations in all public health leadership training programs.

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

The Current Burden and Future Solutions for Preoperative Cataract-Refractive Evaluation Diagnostic Devices: A Modified Delphi Study.

To obtain consensus on the key areas of burden associated with existing devices and to understand the requirements for a comprehensive next-generation diagnostic device to be able to solve current challenges and provide more accurate prediction of intraocular lens (IOL) power and presbyopia correction IOL success. Thirteen expert refractive cataract surgeons including three steering committee (SC) members constituted the voting panel. Three rounds of voting included a Round 1 structured electronic questionnaire, Round 2 virtual face-to-face meeting, and Round 3 electronic questionnaire to obtain consensus on topics related to current limitations and future solutions for preoperative cataract-refractive diagnostic devices. Forty statements reached consensus including current limitations (n = 17) and potential solutions (n = 23) associated with preoperative diagnostic devices. Consistent with existing evidence, the panel reported unmet needs in measurement accuracy and validation, IOL power prediction, workflow, training, and surgical planning. A device that facilitates more accurate corneal measurement, effective IOL power prediction formulas for atypical eyes, simplified staff training, and improved decision-making process for surgeons regarding IOL selection is expected to help alleviate current burdens. Using a modified Delphi process, consensus was achieved on key unmet needs of existing preoperative diagnostic devices and requirements for a comprehensive next-generation device to provide better objective and subjective outcomes for surgeons, technicians, and patients.

Basic Concepts of Plyometric Training in Soccer Players

Exercises that involve plyometrics are typically broken up into a stretch-shortening cycle with three phases: an eccentric phase (rapid muscle lengthening), an amortization phase (short resting interval), and a concentric phase (explosive muscle shortening). How quickly an athlete transitions from the eccentric to the concentric phase is a measure of his power. Plyometric training (PT) affects the neuromuscular components of the body for maximum contraction of muscle in minimum time. Soccer is a multidimensional sport and requires various strength training, endurance training, power and agility training, and soccer tactical training. Considering all facts, PT has got a strong tendency to develop cardiovascular and neuromuscular fitness. PT improves VO2 max, explosive strength, linear sprint speed, effective kicking, endurance, agility, soccer skills, and vertical jump ability in athletes of any age. PT improves tendon strengthening and increases muscle elasticity, which results in avoiding injuries.

A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network

Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.

The Effect of Limb Muscle Explosive Power Training on the Speed of the Results of Pencak Silat Kicks of PSHT Students

Internal problem study this is there any effect of leg muscle explosive power training on the speed of sickle kick results of Pencak Silat PSHT students at SMA Negeri 4 Sekayu. The purpose of this study was to determine whether or not there was an effect of leg muscle explosive power training on the speed of sickle kick results of Pencak Silat PSHT Students of SMA Negeri 4 Sekayu. The method used in this study uses the method Experiment Design That is pre- Experiment Design Often also called “Almost Experiment” or mock experiments. The type of design used one group pretest-posttest design. Data collection techniques with tests. Data analysis techniques using statistical analysis. Researchers use several method statistics, namely simple linear regression, product moment correlation, and testing the hypothesis used t test The conclusion shows the results of the regression Y = 0.008 + 1.16 X the results of calculating the product moment correlation (rxy) of 0.17 is included in the category of the level of a strong relationship between the independent variables and the variables bound, and the determinant coefficient (r2) = 51% of the speed of sickle kick results and the rest is influenced by other factors. Also, through hypothesis testing, namely t-count&gt; t-table (5.29 &gt; 1.701). clarifies that Ha is accepted and Ho rejected significantly. Thus, there is an effect of muscle explosive power training limbs on the speed of the sickle kick results of Pencak Silat PSHT students at SMA Negeri 4 Sekayu.

The Effects of Power Training Frequency on Muscle Power and Functional Performance in Older Women: A Randomized Controlled Trial.

Katsoulis, K and Amara, CE. The effects of power training frequency on muscle power and functional performance in older women: a randomized controlled trial. J Strength Cond Res 37(11): 2289-2297, 2023-Low-intensity power training (PT) has emerged as an effective method for improving muscle power and functional performance in older adults. However, effects of low training frequencies are less understood and could expand the repertoire of exercise prescription, particularly in older women who experience greater functional disability with age compared with men. This study investigated the impact of frequency of low-intensity (40% of 1 repetition maximum, 1RM) PT on lower-body power and functional performance in healthy older women. Women (74 ± 4 years) were randomized to 12 weeks of PT of 1 (PT1, n = 14), 2 (PT2, n = 17), or 3 (PT3, n = 17) d·wk -1 or wait control (CON, n = 15). Measures included leg press 1RM, knee extension power (KEP), and functional performance (stair climb power, stair climb time, 30-second chair stands, 400-m walk, Short Physical Performance Battery). There were no differences between the frequency of training in changes in leg press 1RM, KEP, or functional performance after 12 weeks. Pre-post data for individual training groups revealed that leg press 1RM improved in all PT groups (20-33%, p < 0.05). Furthermore, KEP improved in PT2 and PT3 by 10 and 12%, respectively, and all PT groups improved in the 30-second chair stands and Short Physical Performance Battery (6-22%), whereas PT1 and PT3 improved in the 400-m walk and PT2 improved in stair climb power and stair climb time after training (4-7%, p < 0.05). One to 3 weekly low-intensity PT sessions can improve functional performance, although improvements in both functional performance and power might require 2 or 3 sessions per week in older healthy women.

Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.

Residual effects of 12 weeks of power-oriented resistance training plus high-intensity interval training on muscle dysfunction, systemic oxidative damage, and antioxidant capacity after 10 months of training cessation in older people with COPD.

This study aimed to assess the residual effects of a 12-week concurrent training program (power training + high-intensity interval training) in older adults with chronic obstructive pulmonary disease (COPD). A total of 21 older adults with COPD [intervention (INT), n = 8; control (CON), n = 13; 76.9 ± 6.8 years] were assessed at baseline and 10 months after the completion of the intervention by the short physical performance battery (SPPB), health-related quality of life (EQ-5D-5L), vastus lateralis muscle thickness (MT), peak pulmonary oxygen uptake (peak VO2 ) and peak work rate (Wpeak ), early and late isometric rate of force development (RFD), leg and chest press maximum muscle power (LPmax and CPmax ), and systemic oxidative damage and antioxidant capacity. Compared to baseline, after 10 months of detraining, the INT group presented increased SPPB (∆ = 1.0 point), health-related quality of life (∆ = 0.07 points), early RFD (∆ = 834 N∙s-1 ), LPmax (∆ = 62.2 W), and CPmax (∆ = 16.0 W) (all p < 0.05). In addition, a positive effect was noted in INT compared to CON regarding MT and Wpeak (both p < 0.05). No between-group differences were reported in peak VO2 , late RFD, systemic oxidative damage, and antioxidant capacity from baseline to 10 months after the completion of the intervention (all p > 0.05). Twelve weeks of concurrent training were enough to ensure improved physical function, health-related quality of life, early RFD and maximum muscle power and to preserve MT and Wpeak but not peak VO2 , late RFD, systemic oxidative damage and antioxidant capacity in the subsequent 10 months of detraining in older adults with COPD.

Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data

Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data

Analysis of the effect of compound training for explosive power on improving the psychological resilience of football athletes

Abstract In view of the problem that in the training of football athletes, coaches pay attention to their football skills and physical quality and ignore their psychological quality training, which leads to poor psychological quality of the athletes, this paper studies the effect of compound training on improving the psychological resilience of football athletes in depth. On the one hand, this paper modeled human lower-limb motion chain. First of all, the explosive power of lower limbs of football athletes was evaluated, and the maximum value of the product was obtained in the power-velocity curve, which aims to further increase the velocity and power of movements. Then, the human multi-rigid body model of lower limbs was established, and the motion chain curve planning of zero-point moment and center of gravity was carried out, which transforms the motion modeling problem of human lower-limb motion chain into the basic variable set problem of human lower-limb motion state. Finally, through the basic variables planning of human lower-limb motion state, the accurate modeling of human lower-limb motion chain was obtained. On the other hand, this paper analyzed and summarized the theory that compound training is capable of improving psychological resilience of football athletes, sorted out and integrated the influencing factors of psychological resilience, and on this basis conducted research and put forward a new development direction of psychological resilience. The experimental results show that the PEM value of goal focus and emotion control is 0.82, and the PEM value of positive cognition is 0.73, indicating that athletes’ performance in positive cognition has been significantly improved. Therefore, it is concluded that psychological resilience can reduce athletes’ athletic burnout, stimulate athletes’ athletic motivation, and enable athletes to take a more positive and optimistic attitude toward training and competition, so as to achieve better athletic performance.

The Effect of High-Speed Power Training on Physical Frailty in Older Adults: Effect of a Visual-Guided Exercise Program in South Korean Rural Areas.

Exercise has been shown to be an effective intervention; the difficulty still lies in providing exercise programs to the older adults in rural areas. Therefore, this study aimed to examine the effects of a 12-week exercise program provided with visual guidelines (prerecorded video) on frailty among older adults in rural areas. Fifty participants (71.7 ± 4.9 years) from 5 different rural areas were recruited and divided into two groups: the exercise group (EX, n = 24 (male: 8, female: 18)) and the control group (CON, n = 26 (male: 7, female: 17)). With the commencement of the exercise intervention, a prerecorded high-speed power training program for frail older adults was distributed to the EX group. A new prerecorded exercise program was delivered to the EX group every 4 weeks. Frailty status was diagnosed with Fried's criteria before and after the intervention. Muscle strength was measured in the upper and lower limb strength (hand-grip strength and leg extension and flexion), and physical function was measured using a short physical performance battery and gait speed. Fasting blood was collected before and after the intervention and analyzed for blood lipid profile. After 12 weeks of the intervention period, a significant difference in frailty status (P < 0.01) and score (P < 0.01) favoring the EX group was observed. Physical functions (gait speed (P=0.01) and time for sit to stand (P < 0.01)) were significantly improved in the EX group with a significant increase in knee extensor strength (P < 0.01). A significant difference in serum high-density lipoprotein levels favoring the EX group (P=0.03) was also observed. This study confirmed the positive effect of a visual-guided exercise program on older adults' residents in rural areas and provided alternative methods to effectively provide exercise program for the older adults with limited resources.

Open-circuit fault detection of inverter fed PMSM for electrical powertrain applications

&lt;p&gt;This paper aims to enhance the reliability of power electronic converters fed&lt;br /&gt;permanent magnet synchronous machine for electrical powertrain applications.&lt;br /&gt;Many faults like open circuit may affect the power converter and quickly lead to&lt;br /&gt;a decline in supplied power quality and all the power train components behavior.&lt;br /&gt;In this framework, this paper proposes an open circuit fault detection scheme for&lt;br /&gt;inverters. First, the dynamic models of the powertrain subsystems were developed.&lt;br /&gt;In fact, all the mathematics models of the motor, inverter, power converter,&lt;br /&gt;battery and charger are presented. Then, a detection scheme of open-circuit fault&lt;br /&gt;in inverter/motor. The control schemes were elaborated based on current analysis&lt;br /&gt;method. The diagnosis strategy under discontinuous switch-ing dynamics of&lt;br /&gt;the DC/AC inverter is proposed.&lt;/p&gt;

Modelling of a battery supported fuel cell electric power train topology for a regional aircraft

Electrification of aircraft propulsion may offer a way towards carbon dioxide (CO2) neutral air travel. Here, the electric aircraft demonstrators already flying mostly rely on the use of batteries as energy source. While battery electric concepts may be suitable solutions for short range applications, such as urban air vehicles, the energy density of state of the art batteries is not yet sufficient to power regional aircraft with a typical range of 1000 nautical miles and 70 passengers. One possible topology option of a propulsion concept suitable for a regional aircraft is a hybrid composed of a fuel cell system (FCS) and a battery. On the one hand, this concept uses hydrogen (H2) as the primary energy carrier, resulting in a significant reduction of the required battery stack mass in comparison to a battery only aircraft. On the other hand, a battery support of flight phases with high power demand, such as take-off or climb, allows a smaller dimensioning of the fuel cell system and the corresponding thermal management system (TMS) and therefore additional overall system mass benefits in comparison to a fuel cell only aircraft. The present paper analyses the weight reduction potential of a battery stack supported fuel cell system electric power train architecture for a typical regional aircraft with regard to the hybridization factor (HF) and battery specific energy (BSE). The modelling includes the sizing of the fuel cell system and the battery stack, other mechanical and electric components, such as gearboxes, electric motors and power electronics and the corresponding TMS. The according electrified aircraft is resized, keeping wing loading and power-to-weight ratio constant. The best combination of HF and BSE yielding the lowest MTOM with 27 100 kg is still about 19.9 % heavier than the conventionally powered reference aircraft with 22 800 kg. The study shows that future aircraft of similar weight and hence size require very advanced battery technology with regard to the BSE compared to available state of the art solutions.

Sprint skating profile of competitive male bandy players: determination of positional differences and playing level.

This study aimed to compare sprint skating profile characteristics of the different playing positions of junior and senior bandy players. In total, 111 male national-level bandy players (age: 20.7 ± 5.0years, height: 1.80 ± 0.05m, body mass: 76.4 ± 0.4kg, training experience: 13.8 ± 5.0yrs) were tested on their sprint skating profile over 80m. The main findings were that no differences between positions were found in sprint skating performance (speed and acceleration), but that elite players were in general heavier (p < 0.05) than junior players (80.0 ± 7.1 vs. 73.1 ± 8.1kg), they could accelerate faster (2.96 ± 0.22 vs. 2.81 ± 0.28m/s2), and they reached a higher velocity (10.83 ± 0.37 vs. 10.24 ± 0.42m/s) earlier over 80m than the junior players. This implies that junior level players should spend more time in power and sprint training to meet the specific demands of playing at a higher, elite level.

Online quantum time series processing with random oscillator networks

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.