• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.51-58
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• 2016

The importance of SW education is being stressed recent days, and the App Inventor is getting attention as a tool of SW education. In this study, I have developed an app Inventor instruction model, which is based on the Design Based Learning and integrated with elements of computational thinking. And I taught the student to apply this model. and then analyzed the app production process and the changes of student. In developing the app, students defined the problem and made a plan to resolve them. And this student had have a sense of accomplishment and self-confidence through practical experience to implement it in their own source code.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Annual Conference of KIPS
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• 2019.10a
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• pp.288-290
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• 2019

해가 갈수록 스마트홈을 구성하는 다양한 IoT 상품들이 출시되고 있고 이것들을 통합 관제하기 위한 IoT 허브(gateway) 등의 제어 장치들이 필요해 지고 있다. 구글의 'Google home', 아마존의 'Echo' 등이 대표적이다. 그러나 이러한 제어 장치들은 클라우드 기반으로 동작되기 때문에 비용이 발생하고 개인으로부터 생성되는 민감한 개인 데이터들의 보관방법에 대한 다양한 문제들을 내포하고 있다. 본 연구팀은 독립형 스마트 IoT 허브 개발을 통해 개인정보를 보호하고 다양한 IoT 단말기들을 손쉽고 간편하게 제어하고자 하였다. 그리고 IoT 단말기와 연결된 센서의 실시간 모니터링 및 분석을 인공지능 기술인 강화 학습 기술을 이용해 구현할 수 있었다. 네트워크 끊김, 고장 등 IoT 단말기 들의 다양한 통신값을 분석하고 이를 기반으로 안정적이고 효율적인 제어를 가능할 수 있게 되었다. IoT 단말기는 아두이노를 이용했으며 스마트 IoT 허브는 라즈베리 파이로 구현해 개인정보를 보다 안전하게 보호하고 다양한 IoT 단말기를 모니터링 하고 제어할 수 있는 독립형 IoT 허브를 설계하고 구현할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.7-16
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• 2011

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot's posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot's posture and payload change. That's why the moments $M_x$, $M_y$ and $M_z$ working on every joint of a robot vary with robot's posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot's posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot's posture and payload through neural network learning.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1643-1652
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• 2022

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.

• Asian Journal for Public Opinion Research
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• v.9 no.4
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• pp.310-330
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• 2021

Driven by the need for remote learning, the COVID-19 pandemic led to the rise of use of videoconferencing tools. Scholars began noticing an emerging phenomenon of feeling tired and exhausted during virtual meetings. This study determined the predictors of videoconference or Zoom fatigue among nursing students in a large, private, non-sectarian university in the Philippines. This cross-sectional online survey involves 597 nursing students in the Philippines using the Zoom Exhaustion and Fatigue Scale. Multiple linear regression analysis was used to examine predictors of videoconference fatigue. Results indicated that nursing students experienced high levels of videoconference fatigue. Gender, self-reported academic performance, Internet connection stability, attitude toward videoconferencing, frequency, and duration of videoconferences predicted videoconference fatigue. The regression model explained 25.3% of the variances of the videoconference fatigue. Videoconference fatigue is relatively prevalent and may be taking its toll on nursing students. Developing strategic interventions that can protect or mitigate the impact of fatigue during virtual meetings is needed.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.16-24
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• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Cross-Cultural Studies
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• v.26
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• pp.73-94
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• 2012

In the present context of systemic global crisis, this paper focuses on a sustainable society. Throughout the World there are vast members of the unemployes. A secure job lasting a lifetime has become more and more rare. Nowadays majority of jobs are part-time or temporary. $Andr{\acute{e}}$ Gorz found a solution in a policy of the progessive reduction in labor time. This is the potential which automated production opens up for a culture society. Nevertheless, Gorz's proposal is based on utopion ideals. This paper focuses on a dynamic force for a culture society, especially art, learning and the third sector. Adrienne Goehler underlines that a culture in the broad sense of the word produces economical and social productivity. In this connection Goehler give attention to 'Cultrual Creatives' and the Creative Class. Cultural creatives are comprised of people who have participated in the process of creating a new culture with enlightened creativity. The Creative Class is a class of workers whose job is to create economic growth through innovation. Creativity is important for a sustainable society. Gore and Rifkin both come close to the ecological thinking. Gore claims that ecosystem of nature have a self-organizing capacity. In this context tried to prove this article that ecosystem is closely connected with a creative environment.