• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.45-52
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• 2020

Since the opening of the national petition site, it has attracted much attention. In this paper, we perform topic analysis of the national petition site and propose a prediction model for answerable petitions based on deep learning. First, 1,500 petitions are collected, topics are extracted based on the petitions' contents. Main subjects are defined using K-means clustering algorithm, and detailed subjects are defined using topic modeling of petitions belonging to the main subjects. Also, long short-term memory (LSTM) is used for prediction of answerable petitions. Not only title and contents but also categories, length of text, and ratio of part of speech such as noun, adjective, adverb, verb are also used for the proposed model. Our experimental results show that the type 2 model using other features such as ratio of part of speech, length of text, and categories outperforms the type 1 model without other features.

• Archives of Plastic Surgery
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• v.47 no.4
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• pp.333-339
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• 2020

Background The purpose of this study was to compare the anatomical features of the internal mammary vessels (IMVs) at the second and third intercostal spaces (ICSs) with regard to their use as recipient vessels in deep inferior epigastric artery perforator (DIEP) flap breast reconstruction. Methods A total of 38 consecutive DIEP breast reconstructions in 36 patients were performed using IMVs as recipient vessels between March 2017 and August 2018. The intraoperative findings and postoperative complications were analyzed. Anatomical analyses were performed using intraoperative measurements and computed tomography (CT) angiographic images. Results CT angiographic analysis revealed the mean diameter of the deep inferior epigastric artery to be 2.42±0.27 mm, while that of the deep inferior epigastric vein was 2.91±0.30 mm. A larger mean vessel diameter was observed at the second than at the third ICS for both the internal mammary artery (2.26±0.32 mm vs. 1.99±0.33 mm, respectively; P=0.001) and the internal mammary vein (IMv) (2.52±0.46 mm vs. 2.05±0.42 mm, respectively; P<0.001). Similarly, the second ICS was wider than the third (18.08±3.72 mm vs. 12.32±2.96 mm, respectively; P<0.001) and the distance from the medial sternal border to the medial IMv was greater (9.49±2.28 mm vs. 7.18±2.13 mm, respectively; P<0.001). Bifurcations of the IMv were found in 18.4% of cases at the second ICS and in 63.2% of cases at the third ICS. Conclusions The IMVs at the second ICS had more favorable anatomic features for use as recipient vessels in DIEP flap breast reconstruction than those at the third ICS.

• Journal of Trauma and Injury
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• v.25 no.4
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• pp.109-114
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• 2012

Purpose: Deep vein thrombosis and pulmonary thromboembolism are major causes of death after severe multiple trauma. Although various means of prevention have been presented and utilized, still, there are no standard guidelines for anticoagulation of multiple trauma because of some contraindications. Methods: A retrospective study of adult major trauma patients whose injury severity scores (ISSs) were over 16 and who had visited one university hospital in Daegu city was performed. We compared some features of patients diagnosed DVT or PTE with those of patients without DVT by computed tomography or ultra sonography. Those features included accompanying various kinds of intracranial hemorrhages, possibility of ambulation, emergent operation, early transfusion, and suspicious symptoms. Results: The mean age of the 58 subjects included in this study was $50.9{\pm}17.2years$, the mean ISS was $22.7{\pm}6.0$, and the mean hospital stay was $55.2{\pm}37.9days$. Ten(17.2%) patients had emergent surgery, and 44(75.9%) experienced delayed surgery. Early transfusion was needed in 34(58.6%) patients. Among the 18 patients diagnosed with DVT, accompanying intracranial hemorrhages were noted in 8(44.4%) patients; one of the 8 also had PTE. Among the same 18 patients, early transfusions were required in 11(61.1%) patients; one of the 11 also had PTE. Conclusion: The risk of DVT is increased in cases of severe multiple trauma, and many difficulties in applying anticoagulants are experienced. Though we need additional studies to decide proper prophylaxis for DVT and PTE, if the patient's general condition permits, a screening test for DVT as soon as possible could be an effective method to reduce the possibility of a bad outcome.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1134-1142
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• 2019

Detection of double compression is one of the most efficient ways of remarking the validity of videos. Many methods have been introduced to detect HEVC double compression with different coding parameters. However, HEVC double compression detection under the same coding environments is still a challenging task in video forensic. In this paper, we introduce a novel method based on the frame partitioning information in intra prediction mode for detecting double compression in with the same coding environments. We propose to extract statistical feature and Deep Convolution Neural Network (DCNN) feature from the difference of partitioning picture including Coding Unit (CU) and Transform Unit (TU) information. Finally, a softmax layer is integrated to perform the classification of the videos into single and double compression by combing the statistical and the DCNN features. Experimental results show the effectiveness of the statistical and the DCNN features with an average accuracy of 87.5% for WVGA and 84.1% for HD dataset.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.37-44
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• 2019

With the advance of digital IT technology, the performance of the printing and scanning devices is improved and their price becomes cheaper. As a result, the public can easily access these devices for crimes such as forgery of official and private documents. Therefore, if we can identify which printing device is used to print the documents, it would help to narrow the investigation and identify suspects. In this paper, we propose a deep learning model for printer identification. A convolutional neural network model based on local features which is widely used for identification in recent is presented. Then, another model including a step to calculate global features and hence improving the convergence speed and accuracy is presented. Using 8 printer models, the performance of the presented models was compared with previous feature-based identification methods. Experimental results show that the presented model using local feature and global feature achieved 97.23% and 99.98% accuracy respectively, which is much better than other previous methods in accuracy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.91-98
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• 2022

In this study, Pukyong National University's floating population survey and analysis were conducted using Raspberry Pie, an open source hardware, and object detection algorithms based on deep learning technology. After collecting images using Raspberry Pie, the person detection of the collected images using YOLO3's IMAGEAI and YOLOv5 models was performed, and Haar-like features and HOG models were used for accuracy comparison analysis. As a result of the analysis, the smallest floating population was observed due to the school anniversary. In general, the floating population at the entrance was larger than the floating population at the exit, and both the entrance and exit were found to be greatly affected by the school's anniversary and events.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.