• International conference on construction engineering and project management
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• 2007.03a
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• pp.450-458
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• 2007

This paper presents the results of a statistical analysis and its research findings focusing on the learning aspect in the process of international joint ventures (IJVs). The contents of this paper is derived from a sample of 96 field cases based on a proposed conceptual model of effective learning for international construction joint ventures (ICJVs). The paper presents a brief review on the conceptual model with hypotheses and summarized the key results of statistical analysis including factor and multiple regression analysis for the testing of the validity of the proposed conceptual model and its associated research hypotheses. Among other research findings, the research confirms that ICJVs provides an excellent platform of in-action learning for construction organization and suggests that good outcomes in learning could be reaped by a company who has a clear learning intent from the beginning and subsequently take corresponding learning actions during the full process of the joint venture.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.749-758
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• 2007

Learning has become an important aspect for any organization to stay relevant and competitive in the corporate world of survival. In construction industry, the international construction joint ventures (ICJVs) provide an excellent platform with opportunity of learning among partners seeking to develop new area of competency and improve their overall competitiveness for their next project endeavor. This paper discusses the development of a conceptual model of effective learning in ICJVs using four major stages of development in a typical joint venture (JV) 's process. The study identified that there are three key constructs that contribute to effective learning comprising learning conditions in the JV's pre-inception stage, success factors of JV for learning in the forming & organizing stage, and learning actions in the implementation & adjustment stage. The effective learning outcomes are measured by the characteristics of learning organization during the JV's completion & evaluation stage. Details and issues of each stage and the methodology of research will be presented and discussed.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.17
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• pp.25-37
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• 1988

Joint inventory 방법을 다룬 기존의 연구는 생산비용이 일정하다는 조건만을 고려하였다. 본 논문은 기존의 연구에다 새로운 변수(learning curve ratio and learning retension)를 제조업자 측면에서 고려하여 보다 확장된 모델을 다룬다. Joint inventory 모델은 첫째 단일구매자와 둘째 학습곡선비율과 learning retention의 정도에 있어서 그 범위를 결합시키는데 이용되기 위해 개발되어 졌다. 구매자와 제조업자를 위한 로트 사이즈를 결정하기 위하여 증분비용접근방법 (Incremental Cost Approach, ICA)을 쓴다. 총결합비용은 기존모델보다 현저하게 적은데 그 이유는 학습과 learning retention 효과로 인한 제조업자의 생산비 절감과 재고유지 비용의 감소 때문이다. 학습과 learning retention이 현격한 경우, 총결합비용은 제조업자와 구매자의 개별적인 최적정책에서의 비용합(합)보다 적다. 소개된 모델의 효과를 보이기 위해 수치예제를 이용하였다.

• Journal of muscle and joint health
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• v.28 no.1
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• pp.70-78
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• 2021

Purpose: Flipped learning is a novel teaching strategy for encouraging students to engage in the learning process. This study aimed to redesign the public health nursing course and examine the implications of flipped learning on learning outcomes, self-efficacy, and self-leadership in undergraduate programs. Methods: A one-group, pretest-posttest design was used. A total of 80 students participated in this study. The flipped learning method was employed between April 14 and June 5, 2016. The data were analyzed using descriptive statistics and an independent t-test. Results: The self-efficacy of the lower 25% group based on academic performance was significantly increased; however, self-leadership did not show any change after utilizing flipped learning. Overall, 65.4% of the students were satisfied with their class. Conclusion: Flipped learning was an effective strategy for students with low achievement. Despite these advantages, it was considered to reduce the burden of studying.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.474-474
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• 2000

This paper shows that a system with two-link arm can obtain hand reaching movement to a target object projected on a visual sensor by reinforcement learning using a layered neural network. The reinforcement signal, which is an only signal from the environment, is given to the system only when the hand reaches the target object. The neural network computes two joint torques from visual sensory signals, joint angles, and joint angular velocities considering the urn dynamics. It is known that the trajectory of the voluntary movement o( human hand reaching is almost straight, and the hand velocity changes like bell-shape. Although there are some exceptions, the properties of the trajectories obtained by the reinforcement learning are somewhat similar to the experimental result of the human hand reaching movement.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.785-796
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• 2019

Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.792-812
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• 2022

Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

• Journal of muscle and joint health
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• v.29 no.3
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• pp.185-193
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• 2022

Purpose: This study aims to provide basic data for future nursing education by identifying the effects of flipped learning-type project-based learning on nursing college students' self-directed learning ability, self-leadership, and academic self-efficacy. Methods: It is a pre-experimental study designed before and after a single group to verify the effect of flipped learning project-based learning on nursing students' self-directed learning ability, self-leadership, and academic self-efficacy in 81 third-grade nursing students. Results: No statistically significant difference in self-efficacy (t=-0.80, p=.545) but self-directed learning ability (t=-3.85, p<.001) and self-leadership (t=-5.18, p<.001) were found to have a statistically significant difference before and after. Conclusion: Flipped learning-type project-based learning was confirmed effective in improving nursing college students' self-directed learning ability and self-leadership. Therefore, instructors will need to develop and apply teaching methods that provide learners with opportunities for pre-learning and carry out learner-centered projects to improve nursing college students' self-directed learning ability and self-leadership.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.17-24
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• 2004

We present a P-type iterative learning control(ILC) scheme for uncertain robotic systems that perform the same tasks repetitively. The proposed ILC scheme comprises a linear feedback controller consisting of position error, and a feedforward and feedback teaming controller updated by current velocity error. As the learning iteration proceeds, the joint position and velocity mrs converge uniformly to zero. By adopting the learning gain dependent on the iteration number, we present joint position and velocity error bounds which converge at the arbitrarily tuned rate, and the joint position and velocity errors converge to zero in the iteration domain within the adopted error bounds. In contrast to other existing P-type ILC schemes, the proposed ILC scheme enables analysis and tuning of the convergence rate in the iteration domain by designing properly the learning gain.