• 동아시아식생활학회지
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• 제21권1호
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• pp.108-117
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• 2011

The purpose of this study was to analyze the association between educational environment (physical environment of cooking institutes and curriculum) and satisfaction with learning of students at local cooking institutes. Self-administered questionnaires were distributed to 300 student enrolled at cooking institutes located in Pohang and Gyeongju, and a total of 265 were usable. Collected data were statistically analyzed using SPSS 12.0 by frequency, factor, reliability, t-test and Duncan's multiple range test. The results can be summarized as follows. Most of the subjects were enrolled at cooking institutes to learn Korean and Western cuisine. There were significant differences in learning according to institution, facility, method and instructor. There were also significant differences in learning according to gender, age, education, and attended classes. Based on the results, the physical environment of cooking institutes contributed to learning in the students, but the operation system also needs to be improved. However, study was limited in sample size and area, the results can-not be generalized.

• 제어로봇시스템학회논문지
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• 제3권4호
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• pp.363-372
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• 1997

In this paper, the design and the implementation of the adaptive learning rate neural network controller for an articulate robot, which is being developed (or) has been developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies hardware structures by the time-division control with TMS32OC31 DSP chip. Proposed neural network controller with adaptive learning rate structure using expert's heuristics can improve learning speed. The proposed controller verifies its superiority by comparing response characteristics of conventional controller with those of the proposed controller that are obtained from the experiments for the 5 axis vertical articulated robot. We, also, present the generalization property of proposed controller for unlearned trajectory and the change of load through experimental data.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• 한국전자통신학회논문지
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• 제18권1호
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• pp.85-90
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• 2023

보건의료분야 데이터 분석 방법론이 기존의 통계 중심의 연구방법에서 머신러닝을 이용한 예측 연구로 전환되고 있다. 본 연구에서는 다양한 머신러닝 도구들을 살펴보고, 보건의료분야에서 많이 사용하고 있는 통계 도구인 R을 빅데이터 머신러닝에 적용하기 위해 R과 스파크를 연계한 프로그래밍 모델들을 비교한다. 그리고, R을 스파크 환경에서 수행하는 SparkR을 이용한 선형회귀모델 학습의 성능을 스파크의 기본 언어인 스칼라를 이용한 모델과 비교한다. 실험 결과 SparkR을 이용할 때의 학습 수행 시간이 스칼라와 비교하여 10~20% 정도 증가하였다. 결과로 제시된 성능 저하를 감안한다면 기존의 통계분석 도구인 R을 그대로 활용 가능하다는 측면에서 SparkR의 분산 처리의 유용성을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• 유통과학연구
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• 제13권6호
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• pp.105-118
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• 2015

Purpose - Currently, strengthening small and medium-sized enterprises (SME) in terms of competitiveness is a key economic issue. However, the problem is that many SMEs lack the internal competence required to cope with a rapidly changing market structure. Such problems can act as an obstacle to economic development, yet most SMEs in Korea are dealing with this problem today. A company's source of competitive advantage is changing from quantity to quality, facility to knowledge, and hardwork to creativity. Under such circumstances, a company should place learning and sharing of knowledge and continuously creating new knowledge as its priority. This study aims to identify the effect of a chief executive officer's (CEO) compassion and forgiveness - positive factors in organizational emotion - on learning organization activities and organizational performance, through a theoretical comparison. Research design, data, and methodology - For this study, SMEs based in Daejeon and Chungcheong area were selected. To secure credibility of the data, the subjects were selected among those who have been working at the business for six months or longer. The survey was conducted for 30 days from March 5, 2015 to April 5, 2015. Both offline and online surveys were conducted. Fifty companies were chosen and 700 questionnaires were distributed, with 506 used for analysis. Fifty subject companies (25 from Daejeon, 10 from Chungnam, 10 from Chungbuk, and five from Sejong) were selected and the objective, target, and survey content were explained to a manager at each company either face-to-face or on the phone. Of the total of 700 questionnaires distributed via mail or e-mail, 78.6% or 550 copies were returned. Excluding 44 insufficient questionnaires, the remainder, 506 questionnaires, were used for analysis. Results - This study analyzed how the CEO's compassion and forgiveness affects learning organization activities and organizational performance. First, compassion of the CEO at the SMEs directly affected the learning organization activities and indirectly affected the organizational performance. Second, forgiveness of the CEO at the SMEs did not affect the learning organization activities and organizational performance directly or indirectly. Conclusions - The study conclusions are as follows. First, CEO compassionate behavior at the SMEs was a significant variable that directly and indirectly affected learning organization activities and organizational performance. Therefore, the CEO of an SME can create a positive organizational atmosphere through compassionate behaviors in the organization. Second, the forgiving behavior of the CEO did not have direct or indirect effects on learning organization activities and organizational performance. However, the reason for a CEO to continue his or her forgiving behavior is because it strengthens employee resilience, commitment, and self-efficacy to protect the organization from negative influences such as layoffs, risks, and wrongdoings. The action of forgiveness does not have direct or indirect effects. However, the CEO shall continue such behavior to strengthen members' physiological resilience, commitment, and self - effectiveness, and to protect the organization from risks including layoff and external negative factors.

• 기본간호학회지
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• 제12권3호
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• pp.278-283
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• 2005

Purpose: The purpose of this study was to compare changes in learning objectives in Fundamentals of Nursing which were established between 2000 and 2004. Method: 2000, 2004 learning objectives were analyzed with frequencies and percents. Results: There was an increase in the total number of learning objectives used in 2004(n=534) over 2000(n=527). In 2004 compared to 2000, there was an increase in learning objectives related to nursing process, need of oxygenation, need of nutrition, need of temperature regulation, need of activity and exercise, need of comfort, medication, preoperative care. According to Bloom's taxonomy, learning objectives established in 2004, mainly consisted of three domains, 35.5% for comprehension, 23.6% for synthesis, 20.4% for knowledge Changes in learning objectives established in 2004 compared to 2000 decreases in the comprehension domain and increases in the synthesis domain. Conclusion: The learning objectives established in 2004 showed remarkable change when compared to those established in 2000. But the learning objective domains in Bloom's taxonomy were distributed unevenly. For better learning objectives in Fundamentals of Nursing, constant revision will be needed.

• 인터넷정보학회논문지
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• 제23권3호
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• pp.97-110
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• 2022

연합학습은 학습데이터의 전송없이 모델의 학습을 수행할 수 있는 학습방법이다. IoT 혹은 헬스케어 분야는 사용자의 개인정보를 다루는 만큼 정보유출에 민감하여 시스템 디자인에 많은 주의를 기울여야 하지만 연합학습을 사용하는 경우 데이터가 수집되는 디바이스에서 데이터가 이동하지 않기 때문에 개인정보 유출에 자유로운 학습방법으로 각광받고 있다. 이에 따라 많은 연합학습 구현체가 개발되었으나 연합학습을 사용하는 시스템의 개발과 운영을 위한 시스템 설계에 관한 구체적인 연구가 부족하다. 본 연구에서는 연합학습을 실제 프로젝트에 적용하여 IoT 디바이스에 배포하고자 할 때 연합학습의 수명주기, 코드 버전 관리, model serving, 디바이스 모니터링에 대한 대책이 필요함을 보이고 이러한 점을 보완해주는 개발환경에 대한 설계를 제안하고자 한다. 본 논문에서 제안하는 시스템은 중단 없는 model-serving을 고려하였고 소스코드 및 모델 버전 관리와 디바이스 상태 모니터링, 서버-클라이언트 학습 스케쥴 관리기능을 포함한다.

• 대한치과기공학회지
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• 제43권4호
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• pp.186-193
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• 2021

Purpose: The purpose of the study was to determine the general characteristics of students in dental technology departments; the correlations among their immersion in learning, class satisfaction, and academic achievement; factors influencing online learning experience; ways to improve students' class satisfaction; and basic data for designing effective online courses. Methods: A total of 300 questionnaires were produced and distributed to dental technology students from September 29 through October 8, 2020. The outcome was analyzed using IBM SPSS Statistics ver. 25.0. A significance level of α=0.05 was used for reliable verification. Results: Immersion in learning, class satisfaction, and academic achievement were relatively high among students who studied on a regular basis, and class satisfaction and academic achievement were relatively high among students who studied with almost no interruption. Concerning the correlations between academic achievement, immersion in learning, and class satisfaction in online learning, the correlation between academic achievement and class satisfaction was the highest at r=0.862. Class satisfaction was the largest factor that influenced academic achievement, and the higher students' immersion in learning and class satisfaction were, the higher their academic achievement was. Conclusion: The research is a case study that investigated the general characteristics of dental technology department students and the correlations among their immersion in learning, class satisfaction, and academic achievement. The study outcome could be used in determining factors that influence online learning and designing effective online courses that improve learner satisfaction.