• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.682-691
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• 2004

We present a novel haptic sculpting system where the user intuitively adds to and carves out material from a volumetric model using new sculpting tools in the similar way to handling real clay Haptic rendering and model deformation are implemented based on volumetric implicit surface. We enhance previous volume-based haptic sculpting systems by presenting fast and stable force computation on 3D models to be deformed. In order to bridge the gap between fast haptic process (1 KHz) and much slower visual update frequency(~30Hz), the system generates intermediate implicit surfaces between two consecutive physical models being deformed. It performs collision detection and force computation on the intermediate surface in haptic process. The volumetric model being sculpted is visualized as a geometric model which is adaptively polygonized according to the surface complexity. We also introduce various visual effects for the real-time sculpting system including mesh-based solid texturing, painting, and embossing/engraving techniques.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.465-476
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• 2005

The wireless sensor network consists of a number of sensor nodes which have physical constraints. Each sensor node senses surrounding environments and sends the sensed information to Sink. In order to alleviate the inherent vulnerability in security of the wireless sensor nodes with the hardware constraints, the lightweight security protocol is needed and a variety of research is ongoing. In this paper, we propose a non-hierarchical sensor network and a security protocol that is suitable for monitoring man-made objects such as bridges. This paper, furthermore, explores a two-layer authentication, key distribution scheme which distributes the key and location of a sensor node in advance, and an effective security routing protocol which can take advantage of the Sleep and Awake state. This also results in the increased data transfer rate by increasing the number of alternative routing paths and the reduced energy consumption rate.

• Journal of Korean language education
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• v.29 no.3
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• pp.325-344
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• 2018

The purpose of this study is to summarize PWIM (Picture Words Inductive Model) which is one of learner-centered vocabulary teaching-learning models, and suggest ways to implement them in Korean language education. The pictures that are used in the Korean language education field help visualize the specific shape, color, and texture of the vocabulary that is the learning target; thus, helping beginner learners to recognize the meaning of the sound. Visual material stimulates the intrinsic schema of the learner and not only becomes a 'bridge' connecting the mother tongue and the Korean language, but also reduces difficulty in learning a foreign language because of the ambiguity between meaning and sound in Korean and all languages. PWIM shows commonality with existing learning methods in that it uses visual materials. However, in the past, the teacher-centered learning method has only imitated the teacher because the teacher showed a piece-wise, out-of-life photograph and taught the word. PWIM is a learner-centered learning method that stimulates learners to find vocabulary on their own by presenting visual information reflecting the context. In this paper, PWIM is more suitable for beginner learners who are learning specific concrete vocabulary such as personal identity (mainly objects), residence and environment, daily life, shopping, health, climate, and traffic. The purpose of this study was to develop a method of using PWIM suitable for Korean language learners and teaching procedures. The researchers rearranged the previous research into three steps: brainstorming and word organization, generalization of semantic and morphological rules of extracted words, and application of words. In the case of PWIM, you can go through all three steps at once. Otherwise, it is possible to divide the three steps of PWIM and teach at different times. It is expected that teachers and learners using the PWIM teaching-learning method, which uses realistic visual materials, will enable making an effective class together.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.451-456
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• 2018

UAVs (Unmanned Aerial Vehicles) are widely used for maintenance and monitoring of facilities. It is necessary to acquire a high-resolution image for evaluating the appearance state of the facility in safety inspection. In addition, it is essential to acquire the video data in order to acquire data over a wide area rapidly. In general, since video data does not include position information, it is difficult to analyze the actual size of the inspection object quantitatively. In this study, we evaluated the utilization of 3D point cloud data of bridges using a matching between video frames and reference photos. The drones were used to acquire video and photographs. And exterior orientations of the video frames were generated through feature point matching with reference photos. Experimental results showed that the accuracy of the video frame data is similar to that of the reference photos. Furthermore, the point cloud data generated by using video frames represented the shape and size of bridges with usable accuracy. If the stability of the product is verified through the matching test of various conditions in the future, it is expected that the video-based facility modeling and inspection will be effectively conducted.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.187-192
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• 2018

This thesis is about the study of inverter terminal boxes in a explosion proof type exhaust fan that can control wind volume and operating time. In Korea, there is no ventilator to adjust the amount of wind and operating time when working in poor conditions. The purpose of the project is to create a explosion terminal box that can control the operating time and wind speed of a suitable explosion ventilator in hazardous environments. The two explosion-proof switches allow the operation time to be driven 1 hour, 3 hours and continuous time, and the speed of the induction motor rotation was set in 3 stages at 2000 rpm, 2600 rpm and 3000 rpm to control the volume. The tested motor used a half-horsepower barrier three-phase induction motor and a full-bridge inverter to set the desired flow rate and operating time.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Industry Promotion Research
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• v.8 no.2
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• pp.1-9
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• 2023

To bridge the research gap in the area of the Fourth Industrial Revolution, we explore the correlation between the core technologies of the Fourth Industrial Revolution and the economic performance of companies. The results show that the technologies have a statistically significant positive (+) correlation with company sales. The size of the correlation is highest for 3D printing (139%), followed by big data (129%), cloud computing (127%), artificial intelligence (78%), and the internet of things (70%). We also found a statistically significant negative (-) interaction effect between the internet of things and 3D printing, cloud computing and big data, and cloud computing and 3D printing when examining the interaction effect of introducing core technologies of the Fourth Industrial Revolution on company sales. This paper represents an early attempt to examine the correlation between the core technologies of the Fourth Industrial Revolution and the economic performance of companies and may serve as a basis for further empirical research.

• Earthquakes and Structures
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• v.22 no.3
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• pp.245-261
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• 2022

Key questions to researchers interested in nonlinear analysis of skeletal structures are whether the distributed plasticity approach - albeit computationally demanding - is more reliable than the concentrated plasticity to adequately capture the extent and severity of the inelastic response, and whether force-based formulation is more efficient than displacement-based formulation without compromising accuracy. The present research focusing on performance-based seismic response of mid-span concrete bridges provides a pilot holistic investigation opting for some hands-on answers. OpenSees software is considered adopting different modeling techniques, viz. distributed plasticity (through either displacement-based or force-based elements) and concentrated plasticity via beam-with-hinges elements. The pros and cons of each are discussed based on nonlinear pushover analysis results, and fragility curves generated for various performance levels relying on incremental dynamic analyses under real earthquake records. Among prime conclusions, distributed plasticity modeling albeit inherently not relying on prior knowledge of plastic hinge length still somewhat depends on such information to ensure accurate results. For instance, displacement-based and force-based approaches secure optimal accuracy when dividing, for the former, the member into sub-elements, and satisfying, for the latter, a distance between any two consecutive integration points, close to the expected plastic hinge length. On the other hand, using beam-with-hinges elements is computationally more efficient relative to the distributed plasticity, yet with acceptable accuracy provided the user has prior reasonable estimate of the anticipated plastic hinge length. Furthermore, when intrusive performance levels (viz. life safety or collapse) are of concern, concentrated plasticity via beam-with-hinges ensures conservative predicted capacity of investigated bridge systems.