• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.53-59
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• 2021

In this paper, we propose a system that can perform dumbbell exercise by recognizing the weight of dumbbells without wearing and device. With the development of virtual reality technnology, many studies are being conducted to simulate the pysical feedback of the real world in the virtual world. Accurate motion recognition is important to the elderly for rehabilitation exercises. They cannot lift heavy dumbbells. For rehabilitation exercise, correct body movement according to an appropriate weight must be performed. We use a machine learning algorithm for the accuracy of motion data input in real time. As an experiment, we was test three types of bicep, double, shoulder exercise and verified accuracy of exercise. In addition, we made a virtual gym game to actually apply these exercise in virtual reality.

• Korean Linguistics
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• v.76
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• pp.187-211
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• 2017

The purpose of this study is to investigate the speech act realization of speculative expressions and to present their teaching-learning contents. It is hard for Korean learners to use speculative expressions appropriately because there are various similar expressions and their meaning is distinctive in detail. This study describes speech act realizations of '-는 것 같다, -을까, -나 보다, -을걸'. All these forms have the meaning of speculations, so they are mainly used to present uncertain information or thoughts of speaker. But they show distinctive aspects. '-는 것 같다' is mainly used to present contents contrary to their counterparts' opinions or irritating for their counterparts. It is used as polite forms because it conveys meanings of uncertainty. Especially in these contexts, it performs the refusal speech acts. '-을까' has the characteristic feature in the complex forms such as '뭐랄까', '뭐라고 할까' and it performs request speech acts more frequently than '-는 것 같다'. Also it is used to express the speakers' opinions contrary to their counterparts'. '-나 보다' expresses speaker's speculations based on hearer's conditions or his speech, so it is used to respond to hearer actively and express interests unlike other speculative expressions. '-을걸' isn't used to perform request, to express interests to hearer. However, it is mainly used when speaker has the contrary assumptions or expectations to hearer's. Based on the analyze, this study presents and grades teaching-learning contents of speculative expressions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1165-1171
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• 2022

Human behavior recognition using an accelerometer has been applied to various fields. As smartphones have become used commonly, a method for human behavior recognition using the acceleration sensor built into the smartphone is being studied. In the case of the elderly, falling often leads to serious injuries, and falls are one of the major causes of accidents at construction fields. In this article, we proposed recognition method for human falling direction using built-in acceleration sensor and orientation sensor in the smartphone. In the past, it was a common method to use the magnitude of the acceleration vector to recognize human behavior. These days, deep learning has been actively studied and applied to various areas. In this article, we propose a method for recognizing the direction of human falling by applying the deep learning multilayer technique, which has been widely used recently.

• Journal of Engineering Education Research
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• v.26 no.4
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• pp.11-21
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• 2023

In engineering education, stimulating students' questions and encouraging learning participation are crucial for achieving higher-order thinking abilities. This study aims to investigate the use and effect of an online communication channel in fostering engineering students' questioning abilities. Consequently, in this research, we gauged students' satisfaction with an engineering class that implemented a communication channel, and scrutinized the changes in their perceptions regarding the significance of questions, their engagement in learning, and their academic self-efficacy. In addition, we interviewed the students who participated in the class. The outcomes are as follows: Firstly, student satisfaction improved compared to the previous semester's class where the communication channel was not utilized. Secondly, learners' understanding of the importance of asking questions positively escalated, alongside their actual frequency of posing questions. Thirdly, there was an improvement in learners' active engagement in their studies and their academic self-confidence. The findings of this research suggest that communication channels should be employed to motivate learners to pose questions and involve students in effective learning.

• Steel and Composite Structures
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• v.49 no.1
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• pp.109-123
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• 2023

Headed studs welded to steel beams and embedded within the concrete of deck slabs are vital components of modern composite floor systems, where safety and economy depend on the accurate predictions of the stud shear resistance. The multitude of existing deck profiles and the complex behavior of studs in deck slab ribs makes developing accurate and reliable mechanical or empirical design models challenging. The paper addresses this issue by presenting a machine learning (ML) model developed from the natural gradient boosting (NGBoost) algorithm capable of producing probabilistic predictions and a database of 464 push-out tests, which is considerably larger than the databases used for developing existing design models. The proposed model outperforms models based on other ML algorithms and existing descriptive equations, including those in EC4 and AISC 360, while offering probabilistic predictions unavailable from other models and producing higher shear resistances for many cases. The present study also showed that the stud shear resistance is insensitive to the concrete elastic modulus, stud welding type, location of slab reinforcement, and other parameters considered important by existing models. The NGBoost model was interpreted by evaluating the feature importance and dependence determined with the SHapley Additive exPlanations (SHAP) method. The model was calibrated via reliability analyses in accordance with the Eurocodes to ensure that its predictions meet the required reliability level and facilitate its use in design. An interactive open-source web application was created and deployed to the cloud to allow for convenient and rapid stud shear resistance predictions with the developed model.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• ETRI Journal
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• v.46 no.4
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• pp.671-682
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• 2024

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short-term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1-score. The use of ResNet50V2 for feature extraction can improve model performance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.627-636
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• 2023

This study was conducted to lay the basis of the need of the self-directed learning ability, critical thinking, communicative ability, problem solving skills for nursing students by confirming the effect of problem based learning classes of nursing students. The data collection period was from March 1 to June 7, 2022. It was provided problem based learning classes to 165 nursing students located at B city. Problem based learning classes were conducted at total of 14 times, and 100 minutes/time. The collected data were analyzed using the frequency and percentage, Cronbach's α, mean and standard deviation with the SPSS Win 21.0 program, and the effectiveness verification of problem based learning classes was analyzed with a paired t-test. As a result of the effectiveness of the problem based learning class, the self-directed learning ability(t=-2.08, p=.039), critical thinking(t=-2.49, p=.014), communicative ability(t=-4.90, p<.001), problem solving skills(t=-4.84, p<.001) of nursing students who took 14 weeks of problem based learning was enhanced. Based on the results of this study, by applying it in various ways to first-year nursing students, it will be possible to use them to improve their competence, major satisfaction, and adapt to college life.

• The Journal of Korean Association of Computer Education
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• v.12 no.4
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• pp.13-21
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• 2009

Educational robots have various potentialities to support programming learners because it is interesting enough to improve the learners' participation and motivation. Nonetheless, some researches assert that the use of educational robot does not necessarily lead to effective and successful learning. With respect to these serious problems, the researchers are emphasizing that it is needed to overcome the probable 'Novelty Effect' by means of considering specific features of the robot programming environment and the participants. We analyzed and found some features of robot programming teaching and learning environment and professional high school students through reviewing of the literatures, and then conducted delphi research to abstract motivational strategies and to develop their applying methods with the specific features. We developed a robot programming teaching and learning model for stimulating and maintaining professional high school student's motivation, which includes 5 factors and 21 strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1143-1148
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• 2017

Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.