• Journal of Korean society of Dental Hygiene
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• 제7권4호
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• pp.365-379
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• 2007

The objective of the current study is to provide basic data necessary for the development of systematic program that is required for the systematic occupational work training of dental hygienists who newly employed at dental hospitals or clinics. The results of the surveys were listed as in below. The survey was conducted for 175 experienced dental hygienists who are in charge of occupational work training in 6 regions(Seoul, Kyunggi, Busan, Ulsan, Kwangju, Chungnam, Kyungnam) of the country where the occupational work training for new dental hygienists is systematically operated. 1. The recognition of experienced dental hygienists for the importance of occupational work training revealed that image training was the most importantly recognized by dental hygienists in Seoul Kyunggi regions(pE.01). In case of Busan region, periodontic training and conservative dentistry training were the most importantly recognized, and customer service training was mostly highly recognized in Ulsan region(pE.01). In case of Kwangjuregion, dental health insurance claim training was recognized as most important subject, and Patient consultation training was the most importantly recognized in Chungnam region. In case of Kyungnam region. Oral surgery was recognized as the most important training subject. 2. Regard on the importance of the range of occupational work training, the experienced dental hygienists with less than 2 years of experience were found to recognize the training of greeting and naming most importantly, the dental hygienists with 2~3 years of experience most importantly recognized oral surgery, and the dental hygienists with 4~5 year of experience were found to recognize conservative training most importantly. In case of dental hygienists having 6~9 year of experience recognized periodontic and conservation trainings as the most important subjects, and the dental hygienist having more than 10 years of experience were found to recognize conservative and image trainings mostly importantly.

• Journal of the Korean Institute of Intelligent Systems
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• 제25권1호
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• pp.97-104
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• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.21-26
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• 2006

This paper focuses on modeling and predicting differential GPS corrections transmitted by marine radio-beacon systems using artificial neural networks. Various neural network structures with various training algorithms were examined, including Linear, Radial Biases, and Feedforward. Matlab Neural Network toolbox is used for this purpose. Data sets used in building the model are the transmitted pseudorange corrections and broadcast navigation message. Model design is passed through several stages, namely data collection, preprocessing, model building, and finally model validation. It is found that feedforward neural network with automated regularization is the most suitable for our data. In training the neural network, different approaches are used to take advantage of the pseudorange corrections history while taking into account the required time for prediction and storage limitations. Three data structures are considered in training the neural network, namely all round, compound, and average. Of the various data structures examined, it is found that the average data structure is the most suitable. It is shown that the developed model is capable of predicting the differential correction with an accuracy level comparable to that of beacon-transmitted real-time DGPS correction.

• Proceedings of the Korean Institute of Building Construction Conference
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.57-58
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• 2022

Concrete mix design is used as essential data for the quality of concrete, analysis of structures, and stable use of sustainable structures. However, since most of the formulation design is established based on the experience of experts, there is a lack of data to base it on. are suffering Accordingly, in this study, the purpose of this study is to build a predictive model to use the concrete mixing factor as basic data for calculation using the DNN technique. As for the data set for DNN model learning, OPC and ternary concrete data were collected according to the presence or absence of admixture, respectively, and the model was separated for OPC and ternary concrete, and training was carried out. In addition, by varying the number of hidden layers of the DNN model, the prediction performance was evaluated according to the model structure. The higher the number of hidden layers in the model, the higher the predictive performance for the prediction of the mixing elements except for the compressive strength factor set as the output value, and the ternary concrete model showed higher performance than the OPC. This is expected because the data set used when training the model also affected the training.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제20권9호
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• pp.18-23
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• 2006

This paper presents an operator training simulator for power system restoration against massive blackout. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using power flow calculation methodology, and PSS/E data is used to set up the initial state for easy setting. The proposed simulator consists of three major components-a power flow(PF), a data conversion(CONV), and, a GUI module. The PF module calculates power flow, and then checks over-voltages of buses and overloads of lines. The CONV module composes a Y-Bus array and a database at each restoration action. The initial Y-Bus array is composed from PSS/E data. A user friendly GUI module is developed including a graphic editor and a built-in operation manual. The maximum processing time for one step operation is 15 seconds, which is adequate for training purpose.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.256-262
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• 2004

Conventionally, profile non-uniformity has been characterized by relying on approximated profile with angle or anisotropy. In this study, a new non-uniformity model for etch profile is presented by applying a discrete wavelet to the image obtained from a scanning electron microscopy (SEM). Prediction models for wavelet-transformed data are then constructed using a back-propagation neural network. The proposed method was applied to the data collected from the etching of tungsten material. Additionally, 7 experiments were conducted to obtain test data. Model performance was evaluated in terms of the average prediction accuracy (APA) and the best prediction accuracy (BPA). To take into account randomness in initial weights, two hundred models were generated for a given set of training factors. Behaviors of the APA and BPA were investigated as a function of training factors, including training tolerance, hidden neuron, initial weight distribution, and two slopes for bipolar sig-moid and linear function. For all variations in training factors, the APA was not consistent with the BPA. The prediction accuracy was optimized using three approaches, the best model based approach, the average model based approach and the combined model based approach. Despite the largest APA of the first approach, its BPA was smallest compared to the other two approaches.

• Korean Journal of Applied Biomechanics
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• 제15권4호
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• pp.75-83
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• 2005

The purpose of this study was to determine the kinematic characteristics of the ankle joint and RPM(repetition per minutes) during the supra maximal training in cycling. For this study, 8 national representative cyclists, distance cyclists in track and road, were selected. During the super-maximum pedalling, kinematic data were collected using a six-camera(240Hz) Qualisys system. the room coordinate system was right-handed and fixed in the back of a roller for cycle, with right-handed orthogonal segment coordinate systems defined for the leg and foot. Lateral kinematic data were recorded at least for 3 minutes while the participants pedal on a roller. Two-dimensional Cartesian coordinates for each marker were determined at the time of recording using a nonlinear transformation technique. Coordinate data were low-pass filtered using a fourth-order Butterworth recursive filter with cutoff frequency of 15Hz. Variables analyzed in this study were compared using a one factor(time) ANOVA with repeated measures. The results of investigation suggest that the number of rotating pedal was decreased with time phase during the super-maximum pedaling. Maximum angle of the ankle joint showed little in change with time phase compared with minimum angle of that.

• The Transactions of the Korean Institute of Electrical Engineers D
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• 제52권5호
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• pp.290-294
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• 2003

Using a backpropagation neural network (BPNN), a high power semiconductor device was empirically modeled. The device modeled is a n-LDMOSFET and its electrical characteristics were measured with a HP4156A and a Tektronix curve tracer 370A. The drain-source current $(I_{DS})$ was measured over the drain-source voltage $(V_{DS})$ ranging between 1 V to 200 V at each gate-source voltage $(V_{GS}).$ For each $V_{GS},$ the BPNN was trained with 100 training data, and the trained model was tested with another 100 test data not pertaining to the training data. The prediction accuracy of each $V_{GS}$ model was optimized as a function of training factors, including training tolerance, number of hidden neurons, initial weight distribution, and two gradients of activation functions. Predictions from optimized models were highly consistent with actual measurements.

• The Journal of Korean Academic Society of Nursing Education
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• 제25권4호
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• pp.436-447
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• 2019

Purpose: The purpose of this study is to identify the educational effect among nursing students who performed the patient role in women's health nursing simulations. Methods: In this exploratory qualitative study, a sample of 31 third- and fourth-grade nursing students who participated in scenario-based patient roles in clinical performance examination of the women's health nursing simulation practice training. Data were collected through focus group interviews. Qualitative data were analyzed using qualitative content analysis. Results: Three themes emerged from the data analysis. Participants experienced an enhancement of patient-centered nursing competence, deep learning immersion and display of self-regulated learning. The difficulty of performing the patient role contributed additional effects such as the difficulty to perform the patient role in the psychological training environment. Conclusion: It is recommended to utilize nursing students as patients in simulation practice training. On the other hand, the psychological training environment can cause difficulties in performing patient roles, a burden on the role of the patient, and involves the interruption of the role.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.629-635
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• 2021

This study seeks to examine and analyze the influence of institutional strengthening factors, and capacity building - communication, resources, and training - on the performance of defense policy implementation. This study conducted a quantitative analysis related to the implementation of the institutional strengthening policy. The data used are primary data with a research instrument in the form of a questionnaire. The population in this study were all people in the city of Bandung, Indonesia. The sample of this study consisted of 200 respondents consisting of civilians and soldiers who served in the city of Bandung. Data analysis uses the Structural Equation Model (SEM) measurement model. The results of this study reveals that institutional strengthening (X1) influences positively and significantly capacity building's communication (Y1), resources (Y2), and training (Y3). On the other hand, the performance of defense policy implementation (Y4) is positively and significantly affected by capacity building's communication (Y1), resources (Y2), and training (Y3). The interaction between institutions, consumption support, role of the healthcare sector, and effectiveness are the most important indicators reflecting capacity building (communication, resources, training) and the performance of defense policy implementation. Essentially, this study analyzes the performance of defense policy implementation based on capacity building.