• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.58-61
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• 2018

In Korea, current medical technology is the highest level in the world. As a result, many doctors have specialized knowledge of various disorders or diseases, and are proceeding in a better way. With such high medical technology, it is possible to increase the probability of success of surgery to provide high reliability to patients. Rehabilitation is also a form of medical treatment that reduces the side effects that occur after surgery that is done for quick cure. However, the situation in this section is slightly different. There are moves to develop rehabilitation devices and operations, but most of them are now dependent on foreign technology. Rehabilitation therapy, which we commonly know, is dominated by behavior. However, it is also a kind of rehabilitation to find out how much the patient's symptoms are improved or recovered. In this paper, we have studied the development of a program by using the Deeplearning method in order to detect the problem of the food swallowing operation by the severity.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.584-590
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• 2016

This paper proposed "Priority Scheduling and MultiPath Routing Protocol (RRPS) for preventing communication disruption in the urban environment" to minimize the disconnection or disruption of V2I and V2V communication in the urban environment where communication is frequently disconnected according to density. The flow of the RRPS is explained as follows. RSU Request Priority Scheduling (RRPS) is used to apply the priority of the request message prior to reaching the end line by using the Start Line and End Line, which are the management areas of the RSU). This paper also proposed MPRP (Multi Path Routing Protocol) design to set up the multipath to the destination. As a result, the proposed RRPS improves the processing efficiency of V2I by applying priority scheduling to the message of the vehicle requesting the information in the RSU, and can prevent the communication disconnection. Thereby, it is improved the transmission success probability.

• Korean Management Science Review
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• v.27 no.2
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• pp.81-95
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• 2010

There is an increasing interest in contents such as movie, drama and game using transmedia storytelling. It includes Le Grand Chef, The War of Flower, Dae Jang Geum, The Matrix, Harry Potter and The Lord of the Rings. However, transmedia contents have not always been successful. To study the factors affecting possible outcomes of transmedia storytelling, we analyzed the intention on transmedia of potential consumers who have not been exposed to transmedia contents before. To this end, we investigated two different cases : first, potential customer intention to watch dramas which will be produced after launching comic-based movies; second, potential customer intention to see movies which will be made after broadcasting comic-based TV dramas. In each case, we analyzed the outcomes from potential customers by applying several variables including gender, exposure to the original and components of contents (plot, quality of act and music etc). Our study showed that potential customers prefer movies or TV dramas with quality of acting, directing, casting and storylines. Interestingly, the quality of acting is more important in dramas than in movies and casting is an appealing factor to potential customers in movies. In TV drama cases, potential customers have high watching intentions when they read the original content. Among them, male potential customer have low watching intentions on TV drama when they did not read the original content. However, female potential customers have high watching intentions on TV drama regardless of the previous exposure to the originals. In movie cases, female potential customers have higher intentions on seeing movies than male. These results suggest that one needs to consider several factors such as casting, acting and gender for generating transmedia contents with a high probability of success.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1337-1358
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• 2015

In this paper, we present an analytical and simulated study on the performance of adaptive vehicular ad hoc networks (VANET) priority based on Transmission Distance Reliability Range (TDRR) and data type. VANET topology changes rapidly due to its inherent nature of high mobility nodes and unpredictable environments. Therefore, nodes in VANET must be able to adapt to the ever changing environment and optimize parameters to enhance performance. However, there is a lack of adaptability in the current VANET scheme. Existing VANET IEEE802.11p's Enhanced Distributed Channel Access; EDCA assigns priority solely based on data type. In this paper, we propose a new priority scheme which utilizes Markov model to perform TDRR prediction and assign priorities based on the proposed Markov TDRR Prediction with Enhanced Priority VANET Scheme (MarPVS). Subsequently, we performed an analytical study on MarPVS performance modeling. In particular, considering five different priority levels defined in MarPVS, we derived the probability of successful transmission, the number of low priority messages in back off process and concurrent low priority transmission. Finally, the results are used to derive the average transmission delay for data types defined in MarPVS. Numerical results are provided along with simulation results which confirm the accuracy of the proposed analysis. Simulation results demonstrate that the proposed MarPVS results in lower transmission latency and higher packet success rate in comparison with the default IEEE802.11p scheme and greedy scheduler scheme.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.11-18
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• 2008

Korean morphological analyzer generally generates multiple candidates, and then selects the most likely one among multiple candidates. As the number of candidates increases, the chance that the correctly analyzed candidate is included in the candidate list also grows. This process, however, increases ambiguity and then deteriorates the performance. In this paper, we propose a new rule-based model that produces one best analysis. The analysis rules are automatically extracted from large amount of Part-of-Speech tagged corpus, and the proposed model does not require any manual construction cost of analysis rules, and has shown high success rate of analysis. Futhermore, the proposed model can reduce the ambiguities and computational complexities in the candidate selection phase because the model produces one analysis when it can successfully analyze the given word. By combining the conventional probability-based model. the model can also improve the performance of analysis when it does not produce a successful analysis.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1269-1276
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• 2015

The methodology of utilizing Intensity-Duration-flood Quantity (IDQ) curve for flood alert and warning was introduced and its performance was evaluated. For this purpose the lumped parameter model was calibrated and validated for gauged basin data set and the index precipitation equivalent to alert and warning flood was estimated. The index precipitation and IDQ curves associated by three different Antecedant Moisture Conditions (AMCs) are made provision for various possible flood scenarios. The test basin is Wonju-cheon basin ($94.4km^2$) located in Gangwon province, Korea. The IDQ curves corresponding to alert (50% of design flood level) and warning (70% of design flood level) level was estimated using the Clark unit hydrograph based lumped parameter model. The performance evaluation showed 0.704 of POD (Probability of Detection), 0.136 of FAR (False Alarm Ratio), and 0.633 of CSI (Critical Success Index), which is improved from the result of IDQ with single fixed AMC.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.1-7
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• 2020

This paper proposes a link adaptation method for Underwater Internet of Things (IoT), which reduces power consumption of sensor nodes and improves the throughput of network in underwater IoT network. Adaptive Modulation and Coding (AMC) technique is one of link adaptation methods. AMC uses the strong correlation between Signal Noise Rate (SNR) and Bit Error Rate (BER), but it is difficult to apply in underwater IoT as it is. Therefore, we propose the machine learning based AMC technique for underwater environments. The proposed Modulation Coding and Scheme (MCS) prediction model predicts transmission method to achieve target BER value in underwater channel environment. It is realistically difficult to apply the predicted transmission method in real underwater communication in reality. Thus, this paper uses the high accuracy BER prediction model to measure the performance of MCS prediction model. Consequently, the proposed AMC technique confirmed the applicability of machine learning by increase the probability of communication success.