• Korean Journal of Construction Engineering and Management
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• v.1 no.3 s.3
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• pp.75-80
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• 2000

In scheduling multi-unit projects, several alternatives can exist in network construction due to repetitiveness of their activities. Project duration is affected not only by the duration of each activity but also by the arrangement of repeating activities in such projects. This paper provides a network compression method that assigns predecessors to each activity to minimize its float time. Different to the previous efforts that utilized line of balance as the base scheduling-model, this research adopts precedence diagram arranged in two coordinates, the space axis and the resource one. This method seeks the most appropriate predecessors for each activity in each direction of the two coordinates for the purpose of minimizing the idle resource and space. This activity arrangement method was applied to a multi-unit apartment-construction project, to prove its capability of network compression. The result shows that the method successfully sought room for saving construction duration by changing the activity arrangement. The network compression method presented in this research can be utilized in multi-unit construction projects such as apartment complex projects.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.512-517
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• 2008

It is show that increasing of aged and handicapped people requires development of Ubiquitous computing technique to offer the specialized service for handicapped-people. For this, we need a development of Context Awareness and Service Reasoning Technique that the technique is supplied interaction between user and U-environment instead of the old unilateral relation. The old research of context awareness needed probabilistic presentation model like a Bayesian Network based on expert Systems for recognize given circumstance by a domain of uncertain real world. In this article, we define a domain of disorder activity assistant service application and context model based on ontology in diversified environment and minimized intervention of user and developer. By use this context model, we apply the structure learning of Bayesian Network and decide the service and activity to development of application service for handicapped people. Finally, we define the proper Conditional Probability Table of the structured Bayesian Network and if random situation is given to user, then present state variable of Activity and Service by given Causal relation of Bayesian Network based on Conditional Probability Table and it can be result of context awareness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.197-201
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• 2006

In the implementation of a smart home, activity recognition technology using simple sensors is very important. In this paper, we propose a new activity recognition method based on Bayesian network (BN). The structure of the BN is learned by K2 algorithm and is composed of sensor nodes, activity nodes and time node whose state is quantized with reasonable interval. In the proposed method, the BN has less complexity and provides better activity recognition rate than the previous method.

• Journal of Sensor Science and Technology
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• v.16 no.6
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• pp.467-474
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• 2007

The real-time monitoring about the activity of the human provides useful information about the activity quantity and ability. The present study implemented a small-size and low-power acceleration monitoring system for convenient monitoring of activity quantity and recognition of emergent situations such as falling during daily life. For the wireless transmission of acceleration sensor signal, we developed a wireless transmission system based on a wireless sensor network. In addition, we developed a program for storing and monitoring wirelessly transmitted signals on PC in real-time. The performance of the implemented system was evaluated by assessing the output characteristic of the system according to the change of posture, and parameters and acontext recognition algorithm were developed in order to monitor activity volume during daily life and to recognize emergent situations such as falling. In particular, recognition error in the sudden change of acceleration was minimized by the application of a falling correction algorithm

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.82-86
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• 2021

The article analyzes the psychological and pedagogical scientific literature on the problem of motivation of students' educational activity, compiled and implemented a diagnostic research program, studied the system of conditions for the development of motivation for educational activity of students, compiled and implemented a program for the development of motivation for educational activity of students, highlighted the features of motivation for educational activity of students and conducted a comparative study analysis.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.361-368
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• 2022

This study seeks to determine the research trend of late adolescents by utilizing big data. Also, seek for research trends related to activity participation, treatment, and mediation to provide academic implications. For this process, gathered 1.000 academic papers and used TF-IDF analysis method, and the topic modeling based on co-occurrence word network analysis method LDA (Latent Dirichlet Allocation) to analyze. In conclusion this study conducted analysis of activity participation, treatment, and mediation of late adolescents by TF-IDF analysis method, co-occurrence word network analysis method, and topic modeling analysis based on LDA(Latent Dirichlet Allocation). The results were proposed through visualization, and carries significance as this study analyzed activity, treatment, mediation factors of late adolescents, and provides new analysis methods to figure out the basic materials of activity participation trends, treatment, and mediation of late adolescents.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.234-240
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• 2022

This paper proposes a customized AI exercise recommendation service for balancing the relative amount of exercise according to the working environment by each occupation. WISDM database is collected by using acceleration and gyro sensors, and is a dataset that classifies physical activities into 18 categories. Our system recommends a adaptive exercise using the analyzed activity type after classifying 18 physical activities into 3 physical activities types such as whole body, upper body and lower body. 1 Dimensional convolutional neural network is used for classifying a physical activity in this paper. Proposed model is composed of a convolution blocks in which 1D convolution layers with a various sized kernel are connected in parallel. Convolution blocks can extract a detailed local features of input pattern effectively that can be extracted from deep neural network models, as applying multi 1D convolution layers to input pattern. To evaluate performance of the proposed neural network model, as a result of comparing the previous recurrent neural network, our method showed a remarkable 98.4% accuracy.

• Journal of Korean Public Health Nursing
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• v.29 no.3
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• pp.488-502
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• 2015

Purpose: This study was conducted to determine the neighborhood environmental factors associated with physical activity among adults living in rural communities. Methods: A cross-sectional descriptive survey was conducted with a convenience sample of 201 adults living in three Ri in Y-city, Gyeonggi-do. Data were collected from face-to-face interview by trained interviewers and were analyzed using a zero-inflated negative binominal regression model. Results: Participants reported engaged in moderate or vigorous physical activity was 76.1%; 10.5% of participants reported that they met moderate physical activity recommendations and 14.5% of participants reported that they met vigorous physical activity recommendations. Zero-inflated negative binominal regression analysis showed association of increasing days of physical activity with social cohesion (${\beta}=.130$, p=.005), social network (${\beta}=-.096$, p=.003), and safety for crime (${\beta}=-.151$, p=.036), and no days of physical activity was associated with no attainment of education and marginally associated with increasing BMI. Conclusion: Neighborhood environmental factors including social cohesion, social network, and crime for safety were significantly associated with physical activity of rural adults. Community health nurses should expand an approach for individual behavior change to incorporate rural adults' specific neighborhood environmental factors into physical activity interventions.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.612-613
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• 2015

As a construction project is recently becoming large-scaled and complex, construction process plan and management for successful performance of a construction project has become more important. Especially a reasonable estimation plan of activity duration is required because the activity duration is directly related to the determination of the entire project duration and budget. However, the activity duration is used to estimate by the experience of a construction manager and past construction records. Furthermore, the prediction of activity duration is more difficult because there is some uncertainty caused by various influencing factors in a construction project. This study suggests an estimation model of construction activity duration using neural network theory for a more systematic and objective estimation of each activity duration. Because suggested model estimates the activity duration by a reasonable schedule plan, it is expected to reduce the error between planning duration and actual duration in a construction project. And it can be a more systematic estimation method of activity duration comparing to the estimation method by experience of project manager.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.