• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.143-154
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• 2020

With the increasing trend of making manufacturing system intelligent and autonomous, the introduction of robot-assist automation, like machine tending system for automated operation of CNC machine tools, is being actively carried out at many industrial sites. Most important part of this intelligent system to install machine tending system, is interface programming between the CNC machine tools and the industrial robot. Despite this importance, however, the machine tending system has many setup problems. it is necessary for difficult re-program of both controllers whenever a new CNC machine tool or robot is introduced. And, the helps of external engineers is required even though trivial changes due to the complex structure of the machine tending system. Authors of this paper introduces the integrated system of the interface between heterogeneous CNC machine tools and industrial robots. In addition, the digital twin implemented inside the machine tool controller enable shop-floor operators to change the interface programming easily. To implement this system, an integrated development environment for 1) an intelligent HMI platform that provide standardized interfaces to heterogeneous CNC machine tools and 2) a robot platform developing application software of various robots, was established. For easy un-tact environment, this paper explain the development of 3) a game-engine based web program of controlling and monitoring machine tending system remotely.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• Korean Journal of Childcare and Education
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• v.17 no.5
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• pp.83-103
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• 2021

Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children's Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.128-139
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• 2014

This study investigates the effects of machining parameters as they relate to the quality characteristics of machined features. Two most important quality characteristics are set as the dimensional accuracy and the surface roughness. Before any newly acquired machine tool is put to use for production, it is important to test the machine in a systematic way to find out how different parameter settings affect machining quality. The empirical verification was made by conducting a Design of Experiment (DOE) with 3 levels and 3 factors on a state-of-the-art Cincinnati Hawk Arrow 750 Vertical Machining Center (VMC). Data analysis revealed that the significant factor was the Hardness of the material and the significant interaction effect was the Hardness + Feed for dimensional accuracy, while the significant factor was Speed for surface roughness. Since the equally important thing is the capability of the instruments from which the quality characteristics are being measured, a comparison was made between the VMC touch probe readings and the measurements from a Mi-tutoyo coordinate measuring machine (CMM) on bore diameters. A machine mounted touch probe has gained a wide acceptance in recent years, as it is more suitable for the modern manufacturing environment. The data vindicated that the VMC touch probe has the capability that is suitable for the production environment. The test results can be incorporated in the process plan to help maintain the machining quality in the subsequent runs.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.459-466
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• 2010

Natural language processing has several ambiguity problems, and English-Korean machine translation especially includes those problems to be solved in each translation step. This paper focuses on resolving part-of-speech ambiguity of English words in order to improve the efficiency of English analysis, which is in part of efforts for developing practical English-Korean machine translation system. In order to improve the efficiency of the English analysis, the part-of-speech determination must be fast and accurate for being integrated with machine translation system. This paper proposes the probabilistic models for part-of-speech determination. We use Penn Treebank corpus in building the probabilistic models. In experiment, we present the performance of the part-of-speech determination models and the efficiency improvement of the machine translation system by the proposed part-of-speech determination method.

• Journal of Forest and Environmental Science
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• v.17 no.1
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• pp.62-73
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• 2001

This study was conducted to predict the tractive resistance for tree length logs being skidded by ground based logging machine. The mathematical models for predicting the tractive resistance of tree length log have been developed. The tractive resistance is expressed as a function of log weight, skidding coefficient, and ground gradient. The skidding coefficients for four species of Korean pine, Japanese larch, mongolian oak, and cork oak were determined under laboratory condition using universal testing machine and small soil bin, Three different tractive resistance models were applied to four species and compared with each other. The ratios (T/Wt) of skidding-line tensions to the skidding log weight increased linearly with increment in ground gradient. Semi-ground skidding generally required smaller tensions than ground skidding under given condition. Results of this study can be utilized as basic information for logging machine selection and power requirement of skidding winch.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.603-605
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• 2015

Recent IoT (Internet Of Things) development of the technology is growing rapidly. When multiple devices to perform operations on the IoT environment, it is possible to improve the efficiency of operations by different devices to join the collaborative relationship (Relation) between. Research on existing methods and has been used and the user to issue commands to each device P2M (Person to Machine) method, is now being replaced by effective M2M (Machine to Machine) manner than by way bring forth the relationship between the device P2M. In this paper, we define the relationship between the device and bring forth proposals for collaborative data transfer algorithms. To block the operation duplicated between different work through the proposed algorithm and is believed to improve the efficiency of work to do.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.885-896
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• 2013

MTC(Machine Type Communications), providing a variety of services anytime and anywhere by connecting the cellular network to the machine and things without human intervention, is being considered as a major challenge of the next-generation communications. Currently, When a massive MTC devices simultaneously connect to the network, each MTC device needs an independent access authentication process. Because of this process, authentication signaling congestion and overload problems will cause in LTE-Advanced. In this paper, we propose a group-based authentication protocol and a key management protocol. For managing the MTC devices as group units, the proposed protocol elects a group leader and authentications only once with the core network. After the authentication is completed, a group leader manages the rest members and MME(Mobility Management Entity) by constructing a binary tree. Finally, the propose protocol analysis show that the proposed protocol not only can reduces the authentication signaling which generated in between the MTC devices and the core network but also can manages the MTC devices, efficiently.

• Convergence Security Journal
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• v.20 no.3
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• pp.53-60
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• 2020

At the present stage of the fourth industrial revolution, machine learning and artificial intelligence technologies are rapidly developing, and there is a movement to apply machine learning technology in the security field. Malicious code, including new and transformed, generates an average of 390,000 a day worldwide. Statistics show that security companies ignore or miss 31 percent of alarms. As many malicious codes are generated, it is becoming difficult for humans to detect all malicious codes. As a result, research on the detection of malware and network intrusion events through machine learning is being actively conducted in academia and industry. In international conferences and journals, research on security data analysis using deep learning, a field of machine learning, is presented. have. However, these papers focus on detection accuracy and modify several parameters to improve detection accuracy but do not consider the ratio of dataset. Therefore, this paper aims to reduce the cost and resources of many machine learning research by finding the ratio of dataset that can derive the highest detection accuracy in CNN Mobile net-based malware detection model.