• Korean journal of medical education
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• 제30권4호
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• pp.295-308
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• 2018

Purpose: Although medical curricula are now better structured for integration of biomedical sciences and clinical training, most teaching and learning activities still follow the older teacher-centric discipline-specific formats. A newer pedagogical approach, known as Collaborative Learning Cases (CLCs), was adopted in the medical school to facilitate integration and collaborative learning. Before incorporating CLCs into the curriculum of year 1 students, two pilot runs using the action research method was carried out to improve the design of CLCs. Methods: We employed the four-phase Kemmis and McTaggart's action research spiral in two cycles to improve the design of CLCs. A class of 300 first-year medical students (for both cycles), 11 tutors (first cycle), and 16 tutors (second cycle) were involved in this research. Data was collected using the 5-points Likert scale survey, open-ended questionnaire, and observation. Results: From the data collected, we learned that more effort was required to train the tutors to understand the principles of CLCs and their role in the CLCs sessions. Although action research enables the faculty to improve the design of CLCs, finding the right technology tools to support collaboration and enhance learning during the CLCs remains a challenge. Conclusion: The two cycles of action research was effective in helping us design a better learning environment during the CLCs by clarifying tutors' roles, improving group and time management, and meaningful use of technology.

• 대한건축학회논문집:계획계
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• 제36권5호
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• pp.93-103
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• 2020

The purpose of this study is to suggest a supervisory way to improve the efficiency of Building Supervision using Deep Learning, especially object detecting technology. Since the establishment of the Building Supervision system in Korea, it has been changed and improved many times systematically, but it is hard to find any improvement in terms of implementing methods. Therefore, the Supervision is until now the area where a lot of money, time and manpower are needed. This might give a room for superficial, formal and documentary supervision that could lead to faulty construction. This study suggests a way of Building Supervision which is more automatic and effective so that it can lead to save the time, effort and money. And the way is to detect the hoop-bars of a column and count the number of it automatically. For this study, we made a hoop-bar detecting network by transfor learnning of YOLOv2 network through MATLAB. Among many training experiments, relatively most accurate network was selected, and this network was able to detect rebar placement in building site pictures with the accuracy of 92.85% for similar images to those used in trainings, and 90% or more for new images at specific distance. It was also able to count the number of hoop-bars. The result showed the possibility of automatic Building Supervision and its efficiency improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.90-113
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• 2021

Electronic Records Management System (ERMS) is a computer program or set of applications that is utilized for keeping up to date records along with their storage. ERMS has been extensively utilized for enhancing the performance of academic institutions. The system assists in the planning and decision-making processes, which in turn enhances the competencies. However, although ERMS is significant in supporting the process of decision-making, the majority of organizations have failed to take an initiative to implement it, taking into account that are some implementing it without an appropriate framework, and thus resulted in the practice which does not meet the accepted standard. Therefore, this study identifies the factors influencing the adoption of ERMS among employees of HLI in Yemen and the role of such adoption in the decision-making process, using the Unified Theory of Acceptance and Use of Technology (UTAUT) along with Technology, Organization and Environment (TOE) as the underpinning theories. The study conducts a cross-sectional survey with a questionnaire as the technique for data collection, distributed to 364 participants in various Yemeni public Higher Learning Institutions (HLI). Using AMOS as a statistical method, the findings revealed there are significant and positive relationships between technology factors (effort expectancy, performance expectancy, IT infrastructure and security), organizational factors (top management support, financial support, training, and policy),environmental factors (competitiveness pressure, facilitating conditions and trust) and behavioral intention to adopt ERMS, which in return has a significant relationship with the process of decision-making in HLI. The study also presents a variety of theoretical and empirical contributions that enrich the body of knowledge in the field of technology adoption and the electronic record's domain.

• International Journal of Knowledge Content Development & Technology
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• 제11권1호
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• pp.49-64
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• 2021

This research paper delved into the availability and utilization of library portal services for research in university libraries in Nigeria. Two research questions and two null hypotheses were formulated to guide the study. A structured questionnaire was used for data collection. The study was carried out in Nnamdi Azikiwe Library, University of Nigeria Nsukka (UNN) and Felix Aghagbo Nwako Library, Nnamdi Azikiwe University Library (UNIZIK), Awka. A total of 70 professionals (librarians and system analysts) in these libraries supplied data for the study. Data collected was analyzed using mean and standard deviation. These were used to answer the research questions while the null hypotheses were tested using t-test statistic at 0.05 level of significance and 68 degree of freedom. Major findings showed that portal services available in the library showed that the areas of availability of library portal services in Nnamdi Azikiwe Library, U.N.N. includes Agriculture, Health Sciences, Engineering, Environmental Sciences and information about institution, Social Sciences, Arts and Humanities, Natural Sciences. In Festus Aghagbo Nwako Library, Awka, the areas of Portal Library Services include Career Development, Federal Government Programs, Environmental Sciences, Biological Sciences, Social Sciences, Arts and Humanities, Engineering, and Health Sciences. Findings on extent of utilization of portal services available in the libraries indicated that portal services are utilized to a low extent in the university libraries. Furthermore, librarians did not significantly differ in their opinions on the availability of library portal services and on the extent of utilization of library portal services in the libraries under study. It is therefore recommended that provisions should be made by the universities and library management to provide and update required portal services in addition to creating enabling environment for enhanced access and utilization of these services. The governments must make an effort to provide funds for policy implementation, necessary technology training for librarians and users, and develop general information infrastructure.

• 한국문헌정보학회지
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• 제55권3호
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• pp.171-196
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• 2021

이 연구의 목적은 일제강점기 피지배 계급으로 역사 속에서 소외되어 있었던 한국인 도서관 직원들의 현황과 역할에 대해서 조사하고 그들의 활동을 재평가하는 데 있다. 이를 위해 먼저, 한국인이 근무했던 도서관과 한국인 직원현황을 조사했다. 둘째, 그들이 도서관에서 종사했던 직책과 그 성격을 살펴보았다. 셋째, 한국인 도서관 근무자들이 받은 교육에 대한 참석 실태와 도서관 관련 잡지 투고 현황을 조사했다. 현황분석 결과, 일제강점기에 한국인 도서관 직원은 총 27개 도서관에 241명이 있었던 것으로 나타났으며, 도서관 강습회에 73명, 제29회 전국도서관대회에 22명이 참석하였고, 주요 도서관 잡지 기고자도 40여 명에 달했다. 이러한 결과를 통해 이들이 해방 후, 우리나라 근대도서관 이식과정에서 일정 역할을 한 것으로 평가할 수 있다.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 한국분무공학회지
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• 제28권1호
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• 한국비블리아학회지
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• 제18권1호
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• pp.117-146
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• 2007

본 연구에서는 2002년에 수립되어 2003년부터 2007년까지 5개년 계획으로 추진되어 온 학교도서관 활성화 사업의 지난 4년간의 성과를 종합 분석하고자 하며, 분석결과와 평가를 기반으로 향후 2단계 활성화 사업을 추진함에 있어 고려해야 할 사항을 제안하고 있다. 첫째, 3.000억이라는 엄청난 예산을 투입하여 신설되고 리모델링된 도서관이 제대로 운영되기 위해서 가장 중요한 것은 사서교사라는 것이다. 둘째. 학교도서관 활용수업 모형을 개발하되, 학년별. 학급별, 교과별, 단원별로 세분화 전문화시켜 개발해야 한다. 셋째, 학교도서관이 학업성취도에 미치는 영향을 실증적으로 증명해 냄으로써 학부모와 지역사회가 자발적으로 학교도서관 활성화에 나설 수 있도록 한다. 넷째, 다양한 이용자 연구를 통해 고객에 해당하는 이용자들의 현실적인 요구를 분석하고 이들의 요구를 해결할 수 있도록 해야 한다. 다섯째, 사서교사 양성 교육과정에 대한 연구를 통해 사서교사의 자질, 역할. 역량을 강화시킬 수 있는 사서교사 양성을 위한 교직이수 트랙을 개발해야 한다. 그러나 무엇보다 사서교사는 스스로의 역할을 강화시킬 필요가 있으며 고객인 학생과 교사들이 전문가로 인정할 수 있도록 노력해야 한다.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.23-29
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• 2023

가상현실 분야에서는 사용자 경험의 몰입도를 극대화하기 위해 햅틱 피드백을 활용하고 발전시키려는 지속적인 노력이 있었다. 그러나 대부분의 햅틱 피드백은 진동 모터 등 경제성을 고려한 액추에이터를 사용하는 문제로 인해 제한적인 촉각 경험만을 사용자에게 제공할 수 있었다. 복싱과 같은 스포츠 시뮬레이션이나 게임에서의 타격 경험의 경우, 실제 물체를 타격하는 감각과 진동 액추에이터로 렌더링되는 감각 사이의 괴리 때문에 한계가 분명하다. 본 연구에서는 이를 주목하여, 사용자가 손으로 가상의 물체를 타격할 때 가상 임팩트를 생성할 수 있는 햅틱 임팩트 시스템을 제안했다. 햅틱 인터페이스는 퀵 리턴 메커니즘을 사용하여 엔드이펙터가 사용자의 주먹에 햅틱 임팩트 피드백을 직접 전달하고 진동 촉감을 통해서 사용자의 손바닥에 가상 임팩트 감각을 전달할 수 있도록 하였다. 제안된 시스템은 인간 대상 실험을 통해 평가하였으며 실험 결과는 햅틱 임팩트 가상 임팩트의 인지 강도와 사실감에 유의한 영향을 미친다는 것을 나타낸다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.