• Korean Journal of Exercise Nutrition
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• v.25 no.4
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• pp.17-23
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• 2021

[Purpose] This study aimed to investigate the effect of education based on the health belief model on the physical activity of the staff of the University of Medical Sciences. [Methods] This semi-experimental study was conducted on 130 university staff aged 25-50 years from the Hamadan University of Medical Sciences. Inclusion criteria were having at least 1 year of work experience, lack of acute and chronic physical and mental illnesses, and not using drugs that affect physical activity. The samples were randomly divided into two groups. The experimental group received three training sessions based on the health belief model. Before and 2 months after training, the control and experimental groups were evaluated via the following questionnaires: (1) demographic information questionnaire, (2) Health Belief Model Questionnaire, and (3) International Physical Activity Questionnaire. Finally, data were analyzed statistically. [Results] The training process resulted in a significant increase in the mean scores of the health belief model constructs in the experimental group, but changes in the control group were not significant. Self-efficacy was the strongest predictor of physical activity. [Conclusion] The health belief model is a useful model for improving individuals' understanding of the benefits of physical activity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• Current Optics and Photonics
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• v.7 no.4
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• pp.449-456
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• 2023

The localized surface-plasmon resonance has drawn great attention, due to its unique optical properties. In this work a general theoretical description of the dipole mode is proposed, using the forced damped harmonic oscillator model of free charges in an ellipsoid. The restoring force and driving force are derived in the quasistatic approximation under general conditions. In this model, metal is regarded as composed of free charges and bound charges. The bound charges form the dielectric background which has a dielectric function. Those free charges undergo a collective motion in the dielectric background under the driving force. The response of free charges will not be included in the dielectric function like the Drude model. The extinction and scattering cross sections as well as the damping coefficient from our model are verified to be consistent with those based on the Drude model. We introduce size effects and modify the restoring and driving forces by adding the dynamic depolarization factor and the radiation damping term to the depolarization factor. This model provides an intuitive physical picture as well as a simple theoretical description of the dipole mode of the localized surface-plasmon resonance based on free-charge collective motion.

• Computers and Concrete
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• v.14 no.1
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• pp.73-90
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• 2014

The traditional destructive tests in damage detection require high cost, long consuming time, repairing of damaged members, etc. In addition to these, powerful equipments with advanced technology have motivated development of global vibration based damage detection methods. These methods base on observation of the changes in the structural dynamic properties and updating finite element models. The existence, location, severity and effect on the structural behavior of the damages can be identified by using these methods. The main idea in these methods is to minimize the differences between analytical and experimental natural frequencies. In this study, an application of damage detection using model updating method was presented on a one storey reinforced concrete (RC) building model. The model was designed to be 1/2 scale of a real building. The measurements on the model were performed by using ten uni-axial seismic accelerometers which were placed to the floor level. The presented damage identification procedure mainly consists of five steps: initial finite element modeling, testing of the undamaged model, finite element model calibration, testing of the damaged model, and damage detection with model updating. The elasticity modulus was selected as variable parameter for model calibration, while the inertia moment of section was selected for model updating. The first three modes were taken into consideration. The possible damaged members were estimated by considering the change ratio in the inertia moment. It was concluded that the finite element model calibration was required for structures to later evaluations such as damage, fatigue, etc. The presented model updating based procedure was very effective and useful for RC structures in the damage identification.

• Journal of the Korea Society for Simulation
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• v.21 no.1
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• pp.27-34
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• 2012

Proposed in the paper is an agent based and object-oriented methodology to create a virtual logistics support system model. The proposed virtual logistics support system model consists of three types of objects: the logistics force agent model(static model), the military supplies transport manager model(function model), the military supplies state manager model(dynamic model). A logistic force agent model consists of two agent: main function agent and function agent. To improve the reusability and composability of a logistics force agent model, the function agent is designed to adapt to different logistics force agent configuration. A military supplies transport manager is agent that get information about supply route, make decisions based on decision variables, which are maintained by the military supplies state manager, and transport military supplies. A military supplies state manager is requested military supplies from logistics force agent, provide decision variables such as the capacity, order of priority. For the implementation of the proposed virtual logistics force agent model, this paper employs Discrete Event Systems Specification(DEVS) formalism.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.363-369
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• 2010

Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.43-50
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• 2016

This paper introduces an appearance-based facial expression recognition method using ASM landmarks which is used to acquire a detailed face region. In particular, EHMM-based algorithm and SVM classifier with histogram feature are employed to appearance-based facial expression recognition, and performance evaluation of proposed method was performed with CK and JAFFE facial expression database. In addition, performance comparison was achieved through comparison with distance-based face normalization method and a geometric feature-based facial expression approach which employed geometrical features of ASM landmarks and SVM algorithm. As a result, the proposed method using ASM-based face normalization showed performance improvements of 6.39% and 7.98% compared to previous distance-based face normalization method for CK database and JAFFE database, respectively. Also, the proposed method showed higher performance compared to geometric feature-based facial expression approach, and we confirmed an effectiveness of proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1599-1607
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• 2022

The data mainly used for the model are as follows: the personal information, the information of insurance product, etc. With the data, we suggest three types of models: content-based filtering model, collaborative filtering model and classification models-based model. The content-based filtering model finds the cosine of the angle between the users and items, and recommends items based on the cosine similarity; however, before finding the cosine similarity, we divide into several groups by their features. Segmentation is executed by K-means clustering algorithm and manually operated algorithm. The collaborative filtering model uses interactions that users have with items. The classification models-based model uses decision tree and random forest classifier to recommend items. According to the results of the research, the contents-based filtering model provides the best result. Since the model recommends the item based on the demographic and user features, it indicates that demographic and user features are keys to offer more appropriate items.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.157-166
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• 2022

Structural supervision on the construction site has been performed based on visual inspection, which is highly labor-intensive and subjective. In this study, the remote technique was developed to improve the efficiency of the measurements on rebar spacing using a 360° camera and reconstructed 3D models. The proposed method was verified by measuring the spacings in reinforced concrete structure, where the twelve locations in the construction site (265 m²) were scanned within 20 seconds per location and a total of 15 minutes was taken. SLAM, consisting of SIFT, RANSAC, and General framework graph optimization algorithms, produces RGB-based 3D and 3D point cloud models, respectively. The minimum resolution of the 3D point cloud was 0.1mm while that of the RGB-based 3D model was 10 mm. Based on the results from both 3D models, the measurement error was from 10.8% to 0.3% in the 3D point cloud and from 28.4% to 3.1% in the RGB-based 3D model. The results demonstrate that the proposed method has great potential for remote structural supervision with respect to its accuracy and objectivity.