• 한국IT서비스학회지
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• 제20권2호
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• pp.91-109
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• 2021

Recently, social and economic environment has been rapidly changed. In particular, the development of IT technology accelerated the introduction of databases, communication networks, information processing and analyzing systems, making the use of such information and communication technology an essential factor for corporate management innovation. This change also affected the accounting areas. The purpose of this study is to document changes in accounting areas due to the adoption of IT technologies in the era of technology and information, to define the required accounting professions in this era, and to present the efficient educational methodologies for training such accounting experts. An accounting expert suitable for the era of technology and information means an accounting profession not only with basic accounting knowledge, competence, independency, reliability, communication skills, and flexible interpersonal skills, but also with IT skills, data utilization and analysis skills, the understanding big data and artificial intelligence, and blockchain-based accounting information systems. In order to educate future accounting experts, the accounting curriculum should be reorganized to strengthen the IT capabilities, and it should provide a wide variety of learning opportunities. It is also important to provide a practical level of education through industry and academic cooperation. Distance learning, web-based learning, discussion-type classes, TBL, PBL, and flipped-learnings will be suitable for accounting education methodologies to foster future accounting experts. This study is meaningful because it can motivate to consider accounting educational system and curriculum to enhance IT capabilities.

• 소성∙가공
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• 제31권4호
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• pp.173-178
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• 2022

The present study employed two different machine-learning approaches, the extreme gradient boosting (XGB) and light gradient boosting machine (LGBM), to predict a compressive deformation behavior of additively manufactured Ti-6Al-4V. Such approaches have rarely been verified in the field of metallurgy in contrast to artificial neural network and its variants. XGB and LGBM provided a good prediction for elongation to failure under an extrapolated condition of processing parameters. The predicting accuracy of these methods was better than that of response surface method. Furthermore, XGB and LGBM with optimum hyperparameters well predicted a deformation behavior of Ti-6Al-4V additively manufactured under the extrapolated condition. Although the predicting capability of two methods was comparable, LGBM was superior to XGB in light of six-fold higher rate of machine learning. It is also noted this work has verified the LGBM approach in solving the metallurgical problem for the first time.

• Structural Engineering and Mechanics
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• 제81권2호
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• pp.195-203
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• 2022

This paper presents the quantification and location damage detection of plane and space truss structures in a two-phase method to reduce the computations efforts significantly. In the first phase, a proposed damage indicator based on the residual force vector concept is used to get the suspected damaged members. In the second phase, using damage quantification as a variable, a teaching-learning based optimization algorithm (TLBO) is used to obtain the damage quantification value of the suspected members obtained in the first phase. TLBO is a relatively modern algorithm that has proved distinguished in solving optimization problems. For more verification of TLBO effeciency, the classical particle swarm optimization (PSO) is used in the second phase to make a comparison between TLBO and PSO algorithms. As it is clear, the first phase reduces the search space in the second phase, leading to considerable reduction in computations efforts. The method is applied on three examples, including plane and space trusses. Results have proved the capability of the proposed method to precisely detect the quantification and location of damage easily with low computational efforts, and the efficiency of TLBO in comparison to the classical PSO.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.313-315
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• 2021

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper, we aim to build an advanced approach, so-called Ensemble Knowledge Distillation (EKD), to significantly boost the classification accuracies, compared to traditional KD methods by distilling knowledge from a cumbersome teacher model into an ensemble of lightweight student models with parallel branches trained with ground truth labels. Therefore, learning features at different branches of the student models could enable the network to learn diverse patterns and improve the qualify of final predictions through an ensemble learning solution. Although we observed that experiments on the well-established ChestX-ray14 dataset showed the classification improvements of traditional KD compared to the base transfer learning approach, the EKD performance would be expected to potentially enhance classification accuracy and model generalization, especially in situations of the imbalanced dataset and the interdependency of 14 weakly annotated thorax diseases.

• Current Optics and Photonics
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• 제7권5호
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• pp.545-556
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• 2023

This paper introduces volume-sharing multi-aperture imaging (VMAI), a potential approach proposed for volume reduction in space-borne imagers, with the aim of achieving high-resolution ground spatial imagery using deep learning methods, with reduced volume compared to conventional approaches. As an intermediate step in the VMAI payload development, we present a phase-1 design targeting a 1-meter ground sampling distance (GSD) at 500 km altitude. Although its optical imaging capability does not surpass conventional approaches, it remains attractive for specific applications on small satellite platforms, particularly surveillance missions. The design integrates one wide-field and three narrow-field cameras with volume sharing and no optical interference. Capturing independent images from the four cameras, the payload emulates a large circular aperture to address diffraction and synthesizes high-resolution images using deep learning. Computational simulations validated the VMAI approach, while addressing challenges like lower signal-to-noise (SNR) values resulting from aperture segmentation. Future work will focus on further reducing the volume and refining SNR management.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• 유통과학연구
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• 제16권7호
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• pp.67-75
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• 2018

Purpose - In the 21st century, as information technology advances alongside the emergence of the 4th generation, industrial age, industrial environment has become individualized and customized. It is important to hire good quality employees for good service in the industry. The e-learning market is growing every year. Although e-learning companies are finding better quality employees in e-learning, it is not easy to find it. Companies also spend a lot of time and cost to find employee. On the employees side, they want to get a job freely when they want, but they cannot find their job easily. Furthermore, the labor market environment is changing fast. In the 4th generation, industrial age, employers require to find manpower whenever they need and want at little cost. So of their own accord, we have considered the necessity of management of human resources for employees and employers in e-learning. The purpose of this study is to propose a human cloud platform framework for enabling an efficient management of human resources in e-learning industry. Research design, data, and methodology - To pinpoint the items of a human cloud platform framework, the study was initiated according to the following process. First, items of competency relating to e-learning instructional designer was analyzed. Second, based on the items of information from this analysis, selection and validity verification took place with 5 e-learning specialists group. Third, the opinion of experts who were in charge of hiring in e-learning companies were collated with the questionnaire. Lastly, the human cloud platform framework was proposed based on opinion results. Results - The framework was comprised of 7 domains and 27 items in order to develop the human cloud platform for e-learning instructional designer. The analysis results showed that the most highly considered item were 'skill (4.60)' that employee already have the capability. Following this (in order) were 'project type (4.56)', 'work competency (4.56)', and 'strength area of instructional design (4.52)'. Conclusions - The 27 items in the human cloud platform framework were suggested in this study. Following this, we can consider to develop the human cloud platform for finding a job and hiring e-learning instructional designer easily. For successful platform operation, we need to consider reliability between employer and employee. In addition, we need quality assurance system based on operation has public confidence.

• 지능정보연구
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• 제15권4호
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• pp.123-140
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• 2009

퍼지신경망 모형은 인공신경망의 네트워크 구조 표현방법 및 학습알고리듬과 퍼지시스템의 추론방법을 통합한 모형으로 제어 및 예측분야에 성공적으로 적용되고 있다. 본 연구에서는 퍼지신경망 모형 중 우수한 예측정확도로 인해 최근 각광받고 있는 ANFIS (Adaptive Network-based Fuzzy Inference System) 모형에서 생성된 퍼지규칙의 해석용이성을 평가하였다. ANFIS모형은 인간 전문가와 상호작용하면서 규칙을 정제해 나갈 수 있다. 특히 인간전문가의 사전지식을 이용하여 초기 퍼지규칙을 만들고 난 후 모형을 학습하면 최적에 수렴하는 시간을 단축할 뿐 아니라, 전역 최적치 도달가능성이 높아진다고 보고되고 있다. 이러한 관점에서 볼 때 규칙의 해석용이성은 인간 전문가와의 상호작용을 위해 매우 중요한 이슈가 될 수 있다. 본 연구에서는 ANFIS모형과 의사결정나무 모형에서 생성된 규칙을 해석용이성 관점에서 비교하기 위한 측도를 제안하고 각 규칙들을 비교하였다. 본 연구에서 제안된 해석용이성 측도들은 규칙을 생성하는 다양한 기계학습 모형의 규칙생성 능력을 평가하는 기준으로도 활용될 수 있을 것이다.

• 대한지역사회영양학회지
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• 제21권6호
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• pp.497-508
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• 2016

Objectives: The purpose of this study was to evaluate the effect of nutrition education using materials based on social cognitive theory. Education topics focused on improving health-related and dietary self-awareness and behavior capability in adolescents. Methods: Participants were recruited from a middle school for girls; 67 students (educated group, n=34 and control group, n=33) participated. The education group received 12 lessons in club activity class. Self-administered surveys were conducted for each group before and after the nutrition education program. The questionnaires consisted of variables such as self-efficacy, outcome expectation, outcome expectancy, knowledge, and dietary practices based on the social cognitive theory. Education satisfaction was evaluated using a five-point Likert scale for two sections: a) teaching and learning and b) education results. The data were analyzed using a t-test and Chi Square-test (significance level: p < 0.05). Results: In the education group, post-education, there were significant differences in self-efficacy (p < 0.05), knowledge (p < 0.01), and dietary practices (p < 0.05), whereas outcome expectation and expectancy did not show any significant differences. None of the variables showed any significant differences in the control group. Educational satisfaction scores were $4.38{\pm}0.12$ (teaching and learning) and $4.14{\pm}0.15$ (education results). Conclusions: This study showed that improving adolescent's awareness and behavior capability has a positive effect on their dietary practices. Moreover, this study suggested that a theory-based determinant should be considered to improve dietary behavior among adolescents.

• 한국융합학회논문지
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• 제8권7호
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• pp.225-230
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• 2017

건축학 핵심 역량을 강화하기 위해서는 교과목을 실무 위주로 개편해야할 뿐 만 아니라 건축학 교육 인증에서 요구하는 다양한 역량들을 함양할 수 있도록 여러 가지 비교과 프로그램을 개발하고 학생들에게 제공하여야 한다. 본 논문에서는 D 대학에서 구축한 역량 기반 교과-비교과 융합교육시스템에 대해 살펴봄으로써 학생들의 자발적인 비교과 활동 참여율을 높이고 궁극적으로 대학에서 비교과 교육을 활성화하기 위한 방안을 제시하였다. 또한 건축학 전공 재학생과 지역의 건축 설계사 대표들을 대상으로 설문조사를 실시하여 비교과 활동이 건축학 교육과 학생의 진로에 미치는 영향을 살펴보았다. 대학의 융합교육시스템은 학생들이 비교과 활동에 적극 참여하게 하였고, 건축학 학습역량에도 크게 기여하는 것으로 나타났으며, 설문조사 결과 공모전 참여와 자격증 취득 프로그램과 같이 창의성 역량 함양 비교과 활동이 가장 효과적인 것으로 나타났다.