Search results for: positive reinforcement training

Authors: H. Cano, P. Ferrer, N. Garcia, M. Popovic, J. Zapata

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Positive Reinforcement Training is a well-known methodology which has been reported frequently to be used in captive non-human primates. As a matter of fact, it is an invaluable tool for different purposes related with animal welfare, such as primate husbandry and environmental enrichment. It is also essential to perform some cognitive experiments. The main propose of this pilot study was to establish an efficient protocol to train captive olive baboons (Papio anubis). This protocol seems to be vital in the context of a larger research program in which it will be necessary to train a complete population of around 40 baboons. Baboons were studied at the Veterinary Research Farm of the University of Murcia. Temporally isolated animals were trained to perform three basic tasks. Firstly, they were required to take food prices directly from the researchers’ hands. Then a clicker sound or bridge stimulus was added each time the animal acceded to the reinforcement. Finally, they were trained to touch a target, consisted of a whip with a red ball in its end, with their hands or their nose. When the subject completed correctly this task, it was also exposed to the bridge stimulus and awarded with a food price, such as a portion of banana, orange, apple, peach or a raisin. Two protocols were tested during this experiment. In both of them, there were 6 series of 2min training periods each day. However, in the first protocol, the series consisted in 3 trials, whereas in the second one, in each series there were 5 trials. A reliable performance was obtained with only 6 days of training in the case of the 5-trials protocol. However, with the 3-trials one, 26 days of training were needed. As a result, the 5-trials protocol seems to be more effective than the 3-trials one, in order to teach these three basic tasks to olive baboons. In consequence, it will be used to train the rest of the colony.

Keywords: captive primates, olive baboon, positive reinforcement training, Papio anubis, training

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Authors: Shaila Rana, Wasim Alhamdani

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Effective cybersecurity learning relies on an engaging, interactive, and entertaining activity that fosters positive learning outcomes. VR cybersecurity training may promote these aforementioned variables. However, a methodological approach and framework have not yet been created to allow trainers and educators to employ VR cybersecurity training methods to promote positive learning outcomes to the author’s best knowledge. Thus, this paper aims to create an approach that cybersecurity trainers can follow to create a VR cybersecurity training module. This methodology utilizes concepts from other cybersecurity training frameworks, such as NICE and CyTrONE. Other cybersecurity training frameworks do not incorporate the use of VR. VR training proposes unique challenges that cannot be addressed in current cybersecurity training frameworks. Subsequently, this ontology utilizes concepts unique to developing VR training to create a relevant methodology for creating VR cybersecurity training modules. The outcome of this research is to create a methodology that is relevant and useful for designing VR cybersecurity training modules.

Keywords: virtual reality cybersecurity training, VR cybersecurity training, traditional cybersecurity training, ontology

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

Procedia PDF Downloads 193

Authors: Yihao Kuang, Bowen Ding

Abstract:

With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Jungbhin You, Taekyun Kim, Jongho Park, Sungnam Hong, Sun-Kyu Park

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Reinforced concrete has been mainly used in construction field because of excellent durability. However, it may lead to reduction of durability and safety due to corrosion of reinforcement steels according to damage of concrete surface. Recently, research of textile is ongoing to complement weakness of reinforced concrete. In previous research, only experiment of longitudinal length were performed. Therefore, in order to investigate the adhesion performance according to the lattice shape and the embedded length, the pull-out test was performed on the roving with parameter of the number of lateral reinforcement, the lateral reinforcement length and the lateral reinforcement spacing. As a result, the number of lateral reinforcement and the lateral reinforcement length did not significantly affect the load variation depending on the adhesion performance, and only the load analysis results according to the reinforcement spacing are affected.

Keywords: adhesion performance, lateral reinforcement, pull-out test, textile

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Authors: Julia A. Ermakova

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This study aimed to investigate the efficacy of a physical rehabilitation program for postpartum women. The program was developed with the purpose of restoring physical performance in women during the postpartum period. The research employed a variety of methods, including an analysis of scientific literature, pedagogical testing and experimentation, mathematical processing of study results, and physical performance assessment using a range of tests. The program recommends refraining from abdominal exercises during the first 6-8 months following a cesarean section and avoiding exercises with weights. Instead, a feasible training regimen that gradually increases in intensity several times a week is recommended, along with moderate cardio exercises such as walking, bodyweight training, and a separate workout component that targets posture improvement. Stretching after strength training is also encouraged. The necessary equipment includes comfortable sports attire with a chest support top, mat, push-ups, resistance band, timer, and clock. The motivational aspect of the program is paramount, and the mentee's positive experience with the workout regimen includes feelings of lightness in the body, increased energy, and positive emotions. The gradual reduction of body size and weight loss due to an improved metabolism also serves as positive reinforcement. The mentee's progress can be measured through various means, including an external assessment of her form, body measurements, weight, BMI, and the presence or absence of slouching in everyday life. The findings of this study reveal that the program is effective in restoring physical performance in postpartum women. The mentee achieved weight loss and almost regained her pre-pregnancy shape while her self-esteem improved. Her waist, shoulder, and hip measurements decreased, and she displayed less slouching in her daily life. In conclusion, the developed physical rehabilitation program for postpartum women is an effective means of restoring physical performance. It is crucial to follow the recommended training regimen and equipment to avoid limitations and ensure safety during the postpartum period. The motivational component of the program is also fundamental in encouraging positive reinforcement and improving self-esteem.

Keywords: physical rehabilitation, postpartum, methodology, postpartum recovery, rehabilitation

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Authors: Bernardo Cortez, Florian Peter, Thomas Lausenhammer, Paulo Oliveira

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Designing missiles’ autopilot controllers have been a complex task, given the extensive flight envelope and the nonlinear flight dynamics. A solution that can excel both in nominal performance and in robustness to uncertainties is still to be found. While Control Theory often debouches into parameters’ scheduling procedures, Reinforcement Learning has presented interesting results in ever more complex tasks, going from videogames to robotic tasks with continuous action domains. However, it still lacks clearer insights on how to find adequate reward functions and exploration strategies. To the best of our knowledge, this work is a pioneer in proposing Reinforcement Learning as a framework for flight control. In fact, it aims at training a model-free agent that can control the longitudinal non-linear flight dynamics of a missile, achieving the target performance and robustness to uncertainties. To that end, under TRPO’s methodology, the collected experience is augmented according to HER, stored in a replay buffer and sampled according to its significance. Not only does this work enhance the concept of prioritized experience replay into BPER, but it also reformulates HER, activating them both only when the training progress converges to suboptimal policies, in what is proposed as the SER methodology. The results show that it is possible both to achieve the target performance and to improve the agent’s robustness to uncertainties (with low damage on nominal performance) by further training it in non-nominal environments, therefore validating the proposed approach and encouraging future research in this field.

Keywords: Reinforcement Learning, flight control, HER, missile autopilot, TRPO

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Authors: Kate Duggan

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Introduction: Functional Communication Training (FCT) is an approach which aims to reduce behaviours of concern by teaching more effective ways to communicate. It requires identification of the function of the behaviour of concern, through gathering information from key stakeholders and completing observations of the individual’s behaviour including antecedents to, and consequences of the behaviour. Appropriate communicative alternatives are then identified and taught to the individual using systematic instruction techniques. Behaviours of concern demonstrated by individuals with autism spectrum conditions (ASC) frequently have a communication function. When contributing to positive behavior support plans, speech and language therapists and other professionals working with individuals with ASC need to identify alternative communicative behaviours which are equally reinforcing as the existing behaviours of concern. Successful implementation of FCT is dependent on an effective ‘response match’. The new way of communicating must be equally as effective as the behaviour previously used and require the same amount or less effort from the individual. It must also be understood by the communication partners the individual encounters and be appropriate to their communicative contexts. Method: Four case studies within a non-maintained special school environment were described and analysed. A response match framework was used to identify the effectiveness of functional communication training delivered by the student’s speech and language therapist, teacher and learning support assistants. The success of systematic instruction techniques used to develop new communicative behaviours was evaluated using the CODES framework. Findings: Functional communication training can be used as part of a positive behaviour support approach for students within this setting. All case studies reviewed demonstrated ‘response success’, in that the desired response was gained from the new communicative behaviour. Barriers to the successful embedding of new communicative behaviours were encountered. In some instances, the new communicative behaviour could not be consistently understood across all communication partners which reduced ‘response recognisability’. There was also evidence of increased physical or cognitive difficulty in employing the new communicative behaviour which reduced the ‘response effectivity’. Successful use of ‘thinning schedules of reinforcement’, taught students to tolerate a delay to reinforcement once the new communication behaviour was learned.

Keywords: augmentative and alternative communication, autism spectrum conditions, behaviours of concern, functional communication training

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Authors: Ahmed M. Eltohamy

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In this paper, an experimental and numerical study was adopted to investigate the effect geogrid soil reinforcement pre-stressing on the pressure settlement relation of sand bed supporting a strip foundation. The studied parameters include foundation depth and pre-stress ratio for the cases of one and two pre-stressed reinforcement layers. The study reflected that pre-stressing of soil reinforcement resulted in a marked enhancement in reinforced bed soil stiffness compared to the reinforced soil without pre-stress. The best benefit of pre-stressing reinforcement was obtained as the overburden pressure and pre-straining ratio increase. Pre-stressing of double reinforcement topmost layers results in further enhancement of stress strain relation of bed soil.

Keywords: geogrid reinforcement, prestress, strip footing, bearing capacity

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Authors: Norshaffika I. Zaiedy Nor, Andrew P. Smith

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Background: Training is essential to develop individuals’ expertise to meet current and future job demands and to improve work performance. At the same time, individuals’ well-being is crucial to ensure that they can fully and positively carry out their daily duties. In addition to the studies that have examined what constitutes well-being and the factors behind it, many researchers have investigated the predictors of training effectiveness and transfer of training. However, there has been very little integration between them. This study was an attempt to bridge the gap between training effectiveness predictors and well-being. Purpose: This research paper aimed to investigate the association between well-being among employees and psychosocial characteristics, together with training variables. Training variables consist of motivation to learn; learning; implementation intention; and cognitive dissonance. Methodology: In total, 210 workers who had undergone various training programs completed an online survey measuring various psychosocial characteristics, four training variables, and level of well-being. Findings: The results showed that certain types of positive psychosocial characteristics (e.g., positive personality, positive work behaviors, positive work and resources) predict motivation to learn, learning and implementation intention. Meanwhile, negative psychosocial characteristics (e.g. negative work demands and resources, negative coping) predict cognitive dissonance. Also, all the training variables had a moderate to high correlation with well-being. However, after controlling other variables (age, gender, education and psychosocial characteristics), none of the training variables predicted well-being. Self-determination theory, cognitive dissonance theory, and the DRIVE model were used to explain these findings. Conclusion: As there is limited research on the integration of training variables with well-being, this study gives a new perspective in the field of both training and well-being. Further investigations are needed to examine the relationships between them.

Keywords: cognitive dissonance, implementation intention, learning, motivation to learn, psychosocial characteristics, well-being

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Authors: K. Passbakhsh

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Determination of reinforcement forces is one of the most important and main discussions in designing retaining walls. By determining these forces we refrain from conservative planning. By numerically modeling the reinforced soil retaining walls under dynamic loading reinforcement forces can be calculated. In this study we try to approach the gained forces by pseudo-static method according to FHWA code and gained forces from numerical modeling by finite element method, by selecting seismic horizontal coefficient for different wall height. PLAXIS software was used for numerical analysis. Then the effect of reinforcement stiffness and soil type on reinforcement forces is examined.

Keywords: reinforced soil, PLAXIS, reinforcement forces, retaining walls

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Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis

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Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent, reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.

Keywords: deep reinforcement learning, Python, PyTorch, distributed training, modularity, library

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Authors: Seyfollah Fazlollahi, Ahmad Bayan Memar

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Background: A review of manpower planning includes a training analysis based on job descriptions and job specifications which looks carefully at training from the points of view of the company, its various departments and personnel. This may show weaknesses in some departments and as a result, training is needed for the staff. Purpose: The aim of this research is to investigate the effects of training on employee’s efficiency in different aspects of work. Methodology: This is a descriptive-survey study. Statistical population was 85 official employees of University of Qom, Iran. 70 of these individuals were selected on a statistical random sampling method using Morgan&Gorki table. The instrument used in this study was a questionnaire including 22 questions. Result: Findings in this study according to data analysis indicate that majority of respondents had positive attitude towards training programs, in the job or off the job. They believed that training programs promoted and enhanced their behavior positively which leads to high efficiency in their job. In fact, data support the main hypothesis that training has positive effects on job performance and efficiency. Conclusion: It is concluded from this study and other related researches that training (on the job and off the job) has positive and effective role in human development and labor as employee’s efficiency. Employees get acquainted with different tasks of a job. Group co-operation, creativity and innovation will be enforced. Training leads to job skills, increasing knowledge and information about a job. It also increases technical and conceptual human skills, which are important in an organization. We can also mention workers' increasing positive motivation toward their job, enforcement of coordinating moral, their good human relations and good contact with clients.

Keywords: training, work efficiency, employee, human relation, job satisfaction

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Authors: Romisaa Ali

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This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Mahsa Kallhor Mohammadi, Hamideh Reshadatjoo

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This study verified the relationship between in-service training and employees’ feeling of psychological ownership. This research applied a descriptive survey that investigated a correlation between variables. The target population was 140 employees of a Drilling Fluid and Waste Management Service Company, and the sample was 123 employees who were selected randomly and encouraged to complete an electronic questionnaire which was designed based on standard questionnaires for research variables covering 62 questions. The face validity of the questionnaire was supported by an experimental test, and its content validity was approved by the thesis supervisor and consulting advisor. For the descriptive statistics frequency tables and diagrams, measures of central tendency such as mode, median, and mean and measures of variability such as variance, standards deviation, and quartile deviation were used. In the inferential statistics section, the Pearson correlation coefficient was used to verify the relationship between the variables of the research. According to the results, all of the research hypotheses were supported. According to hypothesis 1, there was a positive and significant relationship between training policy-making and employees’ psychological ownership (r=0/408, α=0/05). According to hypothesis 2, there was a positive and significant relationship between training planning and employees’ psychological ownership (r=0/446, α=0/05). According to hypothesis 3, there was a positive and significant relationship between providing the training and employees’ psychological ownership (r=0/512, α=0/05). According to hypothesis 4, there was a positive and significant relationship between training performance management and employees’ psychological ownership (r=0/462, α=0/05). According to hypothesis 5, there was a positive and significant relationship between employees’ motivation and psychological ownership (r=0/694, α=0/05). Therefore, through systematic in-service training, which is in the same line with the strategic goals of an organization and is based on scientific needs analysis, design, implementation, and evaluation, it is possible to improve employees’ sense of psychological ownership toward an organization.

Keywords: in-service training, motivation, organizational behavior, psychological ownership

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Authors: Fares Jnaid, Riyad Aboutaha

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In this paper, a nonlinear Finite Element Analysis (FEA) was carried out using ANSYS software to build a model able of predicting the behavior of Reinforced Concrete (RC) beams with unbonded reinforcement. The FEA model was compared to existing experimental data by other researchers. The existing experimental data consisted of 16 beams that varied from structurally sound beams to beams with unbonded reinforcement with different unbonded lengths and reinforcement ratios. The model was able to predict the ultimate flexural strength, load-deflection curve, and crack pattern of concrete beams with unbonded reinforcement. It was concluded that when the when the unbonded length is less than 45% of the span, there will be no decrease in the ultimate flexural strength due to the loss of bond between the steel reinforcement and the surrounding concrete regardless of the reinforcement ratio. Moreover, when the reinforcement ratio is relatively low, there will be no decrease in ultimate flexural strength regardless of the length of unbond.

Keywords: FEA, ANSYS, unbond, strain

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Authors: Liping Qiu, Xiaofeng Zhang

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In its long history, precious tomb murals suffered from various diseases due to natural and man-made destruction. The key to how to protect tomb murals is how to strengthen and protect the tomb murals. In order to maximize the life of the tomb murals, the artistic, historic, and scientific values of the tomb murals can be continued. In this paper, four kinds of traditional reinforcement materials (silicone acrylic lotion, pure acrylic lotion, polyvinyl acetate lotion, and B72) are selected to reinforce the ground support layer of tomb murals, and the reinforcement effect of each reinforcement material on the ground support layer of murals is compared and analyzed, and the best protection material is obtained.

Keywords: mural, destruction cycle, reinforcement material, disease

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Authors: Waqed H. Almohammed, Mohammed Y. Fattah, Sajjad E. Rasheed

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Dynamic traffic loads cause deformation of underground pipes, resulting in vehicle discomfort. This makes it necessary to reinforce the layers of soil above underground pipes. In this study, the subbase layer was reinforced. Finite element software (PLAXIS 3D) was used to in the simulation, which includes geocell reinforcement, vehicle loading, soil layers and Glass Fiber Reinforced Plastic (GRP) pipe. Geocell reinforcement was modeled using a geogrid element, which was defined as a slender structure element that has the ability to withstand axial stresses but not to resist bending. Geogrids cannot withstand compression but they can withstand tensile forces. Comparisons have been made between the numerical models and experimental works, and a good agreement was obtained. Using the mathematical model, the performance of three different pipes of diameter 600 mm, 800 mm, and 1000 mm, and three different vehicular speeds of 20 km/h, 40 km/h, and 60 km/h, was examined to determine their impact on surface settlement and vertical pressure at the pipe crown for two cases: with and without geocell reinforcement. The results showed that, for a pipe diameter of 600 mm under geocell reinforcement, surface settlement decreases by 94 % when the speed of the vehicle is 20 km/h and by 98% when the speed of the vehicle is 60 km/h. Vertical pressure decreases by 81 % when the diameter of the pipe is 600 mm, while the value decreases to 58 % for a pipe with diameter 1000 mm. The results show that geocell reinforcement causes a significant and positive reduction in surface settlement and vertical stress above the pipe crown, leading to an increase in pipe safety.

Keywords: dynamic loading, finite element, geocell-reinforcement, GRP pipe, PLAXIS 3D, surface settlement

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: C. H. S. Vidyasagar, D. B. Karunakar

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In this present work, five different composite samples with AA2024 as matrix and varying amounts of yttrium (0.1-0.5 wt.%) as reinforcement are developed through cold compaction. The microstructures of the developed composite samples revealed that the yttrium reinforcement caused grain refinement up to 0.3 wt.% and beyond which the refinement is not effective. The microstructure revealed Al2Cu precipitation which strengthened the composite up to 0.3 wt.% yttrium reinforcement. Upon further increase in yttrium reinforcement, the intermetallics and the precipitation coarsen and their corresponding strengthening effect decreases. The mechanical characterization revealed that the composite sample reinforced with 0.3 wt.% yttrium showed highest mechanical properties like 82 HV of hardness, 276 MPa Ultimate Tensile Strength (UTS), 229 MPa Yield Strength (YS) and an elongation (EL) of 18.9% respectively. However, the relative density of the developed composites decreased with the increase in yttrium reinforcement.

Keywords: mechanical properties, AA 2024 matrix, yttrium reinforcement, cold compaction, precipitation

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Authors: Spencer C. Pratt

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Anxiety is an emotional response that many, if not all, elite athletes struggle with on a daily basis. Recently, attention has been drawn to the strong need for athletes to receive mental training in order to help remedy the situation. The conceptual paper explores the effectiveness of a mental training component, based on the anxiolytic effects of exercise by investigating the positive relationship between physical activity and mental health through a comprehensive literature review. The review synthesizes pertinent research regarding the need for mental skills training among elite athletes and the anxiolytic effects of exercise. The paper concludes that with clear positive results from further experimentation with a (moderate intensity) running regimen, a wide range of elite athletes experiencing anxiety problems may have a viable solution.

Keywords: anxiety, mental training component, anxiolytic effects, elite athletes, moderate intensity running, mental skills training, running regimen

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Authors: Festus M. Epetimehin

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This study was conducted to identify the effect of National Directorate of Employment (NDE) training on the profit of Agricultural-Small and Medium Enterprises (SMEs) and to evaluate the factors that influenced farmers' participation in NDE training, as well as the type and frequency of training farmers and other agro-allied entrepreneurs in Nigeria. Using a multi-stage sampling procedure, a total of 384 respondents were sampled, including 192 beneficiaries and 192 non-beneficiaries in Oyo and Lagos States, respectively. Data were analysed using Binary Logit regression and Propensity Score Matching techniques. According to the binary logit analysis, respondents’ gender, availability to extension services, and the location of respondent’s operation were determinant factors influencing NDE training enrolment. All identified factors are related to the probability of respondents’ involvement in a positive way. Propensity score matching revealed that Agricultural-SMEs who participated in the NDE program boosted their profit by N341,072.18. The positive outcome of the effect implies that NDE training enhances Agri-SME performance in Nigeria. The study concluded that greater funding should be provided for the NDE for performance-enhancing training of the Agri-SMEs.

Keywords: PSM, binary logit model, Agri-SME

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Authors: Wesam El Bana

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The current research aimed at designing a training program for improving specific muscular strength through using the especial strength and identifying its effects on the performance level skills of hockey players. The researcher used the quasi-experimental approach (two – group design) with pre- and post-measurements. Sample: (n= 35) was purposefully chosen from sharkia sports club. Five hockey player were excluded due to their non-punctuality. The rest were divided into two equal groups (experimental and control). The researcher concluded the following: The traditional training program had a positive effect on improving the physical variables under investigation as it led to increasing the improvement percentages of the physical variables and the performance level skills of the control group between the pre- and post-measurement. The recommended training program had a positive effect on improving the physical variables under investigation as it led to increasing the improvement percentages of the physical variable and the performance level skills of the experimental group between the pre- and post-measurements. Exercises using the especial strength training had a positive effect on the post-measurement of the experimental group.

Keywords: hockey, especial strength, performance skills

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Meenakshi Srivastava, Anke A. De Boer, Sip Jan Pij

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Teacher’s preparedness towards special educational needs (SEN) of the students in regular schools is an important factor in making education inclusive as a goal to provide education for all. The current study measured the long term effects of an in-service teacher training programme which focused on the inclusion of students with a range of SEN. The programme was on three particular aspects: teachers’ attitudes, their knowledge about SEN and knowledge about teaching methods. A refresher course was also organized for participants of the initial training programme. The long term effects were examined by teachers using a self-report questionnaire (n = 38). The wider effects of the initial training were recorded by interviewing school principals (n = 4). Repeated measures of ANOVA revealed significant effects: more positive attitudes and increased knowledge about SEN among teachers who took the refresher course (n = 18) compared to those who had not (n = 19). Principals also found a more positive attitude, sensitivity and increased awareness about SEN among the participants.

Keywords: inclusion, students with special educational needs, teacher training, follow-up, attitudes change

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

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We propose the use of reinforcement learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making makes it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and statistical analysis of the results. We study the generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: inventory management, reinforcement learning, supply chain optimization, uncertainty

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Authors: Hakan Ozturk, Aydin Demir, Kemal Edip, Marta Stojmanovska, Julijana Bojadjieva

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There are many factors that affect the behavior of reinforced concrete beams. These can be listed as concrete compressive and reinforcement yield strength, amount of tension, compression and confinement bars, and strain hardening of reinforcement. In the study, support condition of short beams is selected statically indeterminate to first degree. Experimental and numerical analysis are carried for reinforcement concrete (RC) short beams. Dimensions of cross sections are selected as 250mm width and 500 mm height. The length of RC short beams is designed as 2250 mm and these values are constant in all beams. After verifying accurately finite element model, a numerical parametric study is performed with varied diameter of tension reinforcement. Effect of change in diameter is investigated on behavior of RC short beams. As a result of the study, ductility ratios and failure modes are determined, and load-displacement graphs are obtained in order to understand the behavior of short beams. It is deduced that diameter of tension reinforcement plays very important role on the behavior of RC short beams in terms of ductility and brittleness.

Keywords: short beam, reinforced concrete, finite element analysis, longitudinal reinforcement

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Authors: Shweta Singh, Sudaman Katti

Abstract:

The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

Procedia PDF Downloads 91

Authors: A. Ardianto, M. A. Putera Agung, S. Pramusandi

Abstract:

Power waterway is one of dam structures and as an intake vertical tunnel or well function for hydroelectric power plants in Jatigede area, Sumedang, West Java. Gate shaft is also one of parts the power waterway system. The paper concerns some consideration in determining a critical state parameter on the back stability analysis of gate shaft or excavation wall stability during excavation. Study analysis was carried out using without and with reinforcement system. Results study showed that reinforcement shaft could reduce the total displacement and safety factor could increases significantly. Based on the back calculation results, it was recommended to install some reinforcement materials and drainage system to reduce pore water pressure.

Keywords: power waterway, reinforcement, displacement, safety

Procedia PDF Downloads 374