• Journal of The Korean Association of Information Education
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• v.18 no.2
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• pp.295-306
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• 2014

A form of life is changed rapidly through development of ICT. And we need information ethics as a new norm of information-oriented society. The effect of information ethics can be maximized by education. Therefore, a new curriculum is demanded by new technology and circumstance. Korea Association of Information education has studied a new curriculum and suggested a new plan that contents of information education classified by 'Software', 'Computer System' and 'Convergence Activities' sections. And the 'Convergence Activities' section is composed of 'Information Ethics', 'Productivity Tools' and 'Robot'. In this paper, we studied on learning elements of the 'Information Ethics' of first grade to ninth grade. We analysed the domestic and foreign curriculum, research results and new issues about information ethics and selected the learning elements about information ethics. We nextly suggested the achievement goals, teaching-learning methods and evaluation methods of information ethics. We expect that the learning elements we suggested about information ethics will contribute to deal with wisely information dysfunction and to training correct talented individuals.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.107-116
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• 2016

Industrial robots have been usually controlled using text-based programming languages provided by each manufacturer with its button-based TP(Teaching Pendent) terminal. Unfortunately, when we consider that people who manipulate TPs in manufacturing sites are mostly unskilled with no background knowledge about computer programming, these text-based programming languages using button-based interaction on manufacturing sites are too difficult for them to learn and use. In order to overcome the weaknesses of the text-based programming language, we propose a visual programming language that can be easily used on gesture-enabled devices. Especially, in our visual programming environment, each command is represented as a block and robots are controlled by stacking those blocks using drag-and-drop gestures, which is easily learnable even by beginners. In this paper, we utilize a widely-spread device, Tablet PC as the gesture-enabled TP. Considering that Tablet PC has limited display space in contrast to PC environments, we designed different kinds of sets of command blocks and conducted user tests. Based on the experiment results, we propose an effective set of command blocks for Tablet PC environment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.