• Journal of KIBIM
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• v.6 no.4
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• Asia Marketing Journal
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• v.15 no.3
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• pp.159-172
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• 2013

In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.1
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• pp.45-52
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• 2015

In this paper, the repeated learning technique of neural network was used for gripping force control algorithm. The hybrid control system was introduced and the manipulator's finger reorganized form 2 ea to 3 ea for comfortable gripping. The data was obtained using the gripping force of repeated learning techniques. In the fucture, the adjustable gripping force will be obtained and improved the accuracy using the artificial intelligence techniques.

• Journal of Dental Rehabilitation and Applied Science
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• v.33 no.2
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• pp.88-96
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• 2017

Purpose: The purpose of this study is to assess the relationship between the time spent designing custom abutments and repeated learning using dental implant computer aided design (CAD) software. Materials and Methods: The design of customized abutments was performed four stages using the 3DS CAD software and the EXO CAD software, and measured repeatedly three times by each stage. Learning effect by repetition was presented with the learning curve, and the significance of the reduction in the total time and the time at each stage spent on designing was evaluated using the Friedman test and the Wilcoxon signed rank test. The difference in the design time between groups was analyzed using the repeated measure two-way ANOVA. Statistical analysis was performed using the SPSS statistics software (P < 0.05). Results: Repeated learning of the customized abutment design displayed a significant difference according to the number of repetition and the stage (P < 0.001). The difference in the time spent designing was found to be significant (P < 0.001), and that between the CAD software programs was also significant (P = 0.006). Conclusion: Repeated learning of CAD software shortened the time spent designing. While less design time on average was spent with the 3DS CAD than with the EXO CAD, the EXO CAD showed better results in terms of learning rate according to learning effect.

• Journal of the Korean School Mathematics Society
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• v.24 no.1
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• pp.59-81
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• 2021

This study aimed to investigate how the learners' mathematics learning processes change with repeatedly reading mathematical text. As a way to teach and learn mathematics, we also wanted to examine the effect of repeated reading and to explore the implications for a more efficient teaching and learning strategy. To help us with this study, we mainly used eye tracking and heart rate (HR) measurement. There were four cycles in a cycle of repeated reading, and the number of repeated readings for all cycles was fixed to three times. Eight prospective mathematics teachers in the Department of Mathematics Education of a National University in South Korea participated. Data were analyzed in five aspects: (1) the total reading time per round, the total reading time per slide; (2) the change trends of total reading time per round and slide; (3) the order of slides read; (4) the change trends of HR per round. We found that most participants read in a similar pattern in the first reading, but the second and third reading patterns appeared more diverse for each learner. Also, the first reading required the most time regardless of the repeat cycle, and the time it took to repeatedly read afterward varied depending on the individual. Based on the findings of this study, the most primary conclusion is that self-directed mathematics learning by using repeated reading is effective regardless of cycle. In addition, we suggested four strategies to improve the efficiency of this teaching and learning method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.269-274
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• 1990

Usually, robot manipulators in production lines are operated with reperting work trajectories. This paper presents the repeated adaptive learning algorithm for robot manipulates for the case of a trajectory. This algorithm uses the nonlinear dynamic model including the repeated friction compensating term, The advantage of the scheme is that It allows friction compensation which may be otherwise difficult for differently constructed models. A secondary advantage of the sheme is that it can also adapt to torque calculation in order to reduce the computational load of the control computer. To show the efficiency of the proposed controller, a computer simulation is performed for the planar robot manipulator with a 2 degree of freedom.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.585-594
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• 2007

The back propagation algorithm took a long time to learn the input patterns and was difficult to train the additional or repeated learning patterns. So Aleksander proposed the binary neural network which could overcome the disadvantages of BP Network. But it had the limitation of repeated learning and was impossible to extract a generalized pattern. In this paper, we proposed a dynamic 3 dimensional Neuro System which was consisted of a learning network which was based on weightless neural network and a feedback module which could accumulate the characteristic. The proposed system was enable to train additional and repeated patterns. Also it could be produced a generalized pattern by putting a proper threshold into each learning-net's discriminator which was resulted from learning procedures. And then we reused the generalized pattern to elevate the recognition rate. In the last processing step to decide right category, we used maximum response detector. We experimented using the MNIST database of NIST and got 99.3% of right recognition rate for training data.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• Journal of Ginseng Research
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• v.20 no.3
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• pp.226-232
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• 1996

This study was performed to investigate the effect of red ginseng extract including some vitamin B groups as test drug on learning and memory in mice. Single and repeated administrations of the test drug improved the acquisition and the process of consolidation in the tests using step-through and step-down apparatus, indicating this test drug improved learning and memory. However, the test drug did not improve scopolamine-induced amnesia. These results suggest that test drug may be useful as a nootropic agent.

• KSCI Review
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• v.14 no.1
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• pp.213-219
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• 2006

We need the customized e-Learning service according to not only developing the wireless mobile and hardware technology, also developing the multimedia process skills. Especially, the beginner who start to learn the first programming course must be provided the personalized learning. The beginner require the repeated practices to obtain the programming skills, also they reveal the different learning effects following the learner capability In this paper. we develop a new e-Learning contents which give the individual service for learner and show the simulation which is program execution to maximize the learning effects.