• Journal of Digital Convergence
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• v.13 no.2
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• pp.215-225
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• 2015

This study was conducted to develop a severity-adjusted LOS(Length of Stay) model for knee replacement patients and identify factors that can influence the LOS by using the Korean National Hospital Discharge in-depth Injury Survey data. The comorbidity scoring systems and data-mining methods were used to design a severity-adjusted LOS model which covered 4,102 knee replacement patients. In this study, a decision tree model using CCS comorbidity scoring index was chosen for the final model that produced superior results. Factors such as presence of arthritis, patient sex and admission route etc. influenced patient length of stay. And there was a statistically significant difference between real LOS and adjusted LOS resulted from health-insurance type, bed size, and hospital location. Therefore the policy alternative on excessive medical utilization is needed to reduce variation in length of hospital stay in patients who undergo knee replacement.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.309-319
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• 2016

There is a need to develop a data quality management algorithm in order to improve the quality of health care data. In this study, we developed a data quality control algorithms associated diseases related to diabetes in patients with hypertension. To make a data quality algorithm, we extracted hypertension patients from 2011 and 2012 discharge damage survey data. As the result of developing Data quality management algorithm, significant factors in hypertension patients with diabetes are gender, age, Glomerular disorders in diabetes mellitus, Diabetic retinopathy, Diabetic polyneuropathy, Closed [percutaneous] [needle] biopsy of kidney. Depending on the decision tree results, we defined Outlier which was probability values associated with a patient having diabetes corporal with hypertension or more than 80%, or not more than 20%, and found six groups with extreme values for diabetes accompanying hypertension patients. Thus there is a need to check the actual data contained in the Outlier(extreme value) groups to improve the quality of the data.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.395-400
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• 2019

Contemporary society, the era of the 4th industrial revolution, has been enjoying a boom in the cosmetics industry due to the launch of cosmetics through new technology development due to the interest in the beauty industry. However, the cosmetics industry is booming, but dry eye syndrome, skin trouble, Hormonal disturbances, and many other side effects. In order to prove the universal use value of pine needles as a natural cosmetic material that can complement the side effects of existing chemical ingredients cosmetics harmful to human body, and to propose a roll model of desirable natural cosmetics, DPPH radical scavenging activity, ABTS assay, and flavonoid content were studied. The results showed that both the DPPH radical and ABTS radical scavenging activity increased in both the $70^{\circ}C$ and $100^{\circ}C$ pine water hot water extracts. The extracts from the pine tree extracts at $70^{\circ}C$ and $100^{\circ}C$ showed $32.0{\pm}2.5mg\;QE/ml$ and 116.8 A total flavonoid of ${\pm}5.5mg\;QE/ml$ was detected. Therefore, it is considered that the pine needle hydrothermal extract having a large amount of antioxidant which is advantageous to human body is highly applicable as a natural cosmetic material harmless to the human body.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Journal of the Korean association of regional geographers
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• v.15 no.3
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• pp.421-433
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• 2009

The impervious ratio was frequently employed as a fundamental attribute will be used as a proxy of the total environmental burden in the urban area since it may contribute as much or more on a cumulative basis to the overall environmental condition. This research proposes a comparative evaluation framework in a more objective and Quantitative way for an impervious ratio in the university campus, using the Google Earth. Two university campuses (Kyungpook National University: KNU, Hong Kong University: HKUJ were selected as survey objectives in order to evaluate the potential of Google Earth in monitoring impervious conditions in the campus. The 61cm resolution of Quickbird data combined with digital map realistically identified the major type of impervious surface such as road, building and parking lots in the study area by large scale spatial precision. The impervious zones with persistently high road density and parking space were specifically identified over the KNU campus while the HKC campus was intensively covered by tree, resulting in almost twice (31%). as compared to KNU (18.4%), The methods of characterizing impervious surface used in this study are easily replicable using data that are primarily publicly available, and therefore the collection of impervious coverage data via Google Earth is, therefore, proposed as a practical alternative.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1100-1108
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• 2015

A camera-based real-time hand posture and gesture recognition system is proposed for controlling various devices inside automobiles. It uses an imaging system composed of a camera with a proper filter and an infrared lighting device to acquire images of hand-motion sequences. Several steps of pre-processing algorithms are applied, followed by a background normalization process before segmenting the hand from the background. The hand posture is determined by first separating the fingers from the main body of the hand and then by finding the relative position of the fingers from the center of the hand. The beginning and ending of the hand motion from the sequence of the acquired images are detected using pre-defined motion rules to start the hand gesture recognition. A set of carefully designed features is computed and extracted from the raw sequence and is fed into a decision tree-like decision rule for determining the hand gesture. Many experiments are performed to verify the system. In this paper, we show the performance results from tests on the 550 sequences of hand motion images collected from five different individuals to cover the variations among many users of the system in a real-time environment. Among them, 539 sequences are correctly recognized, showing a recognition rate of 98%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.1-14
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• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1436-1457
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• 2018

Secure routing services in Wireless Sensor Networks (WSNs) are essential, especially in mission critical fields such as the military and in medical applications. Additionally, they play a vital role in the current and future Internet of Things (IoT) services. Lightness and efficiency of a routing protocol are not the only requirements that guarantee success; security assurance also needs to be enforced. This paper proposes a Secure-Fuzzy Energy Aware Routing Protocol (S-FEAR) for WSNs. S-FEAR applies a security model to an existing energy efficient FEAR protocol. As part of this research, the S-FEAR protocol has been analyzed in terms of the communication and processing costs associated with building and applying this model, regardless of the security techniques used. Moreover, the Qualnet network simulator was used to implement both FEAR and S-FEAR after carefully selecting the following security techniques to achieve both authentication and data integrity: the Cipher Block Chaining-Message Authentication Code (CBC-MAC) and the Elliptic Curve Digital Signature Algorithm (ECDSA). The performance of both protocols was assessed in terms of complexity and energy consumption. The results reveal that achieving authentication and data integrity successfully excluded all attackers from the network topology regardless of the percentage of attackers. Consequently, the constructed topology is secure and thus, safe data transmission over the network is ensured. Simulation results show that using CBC-MAC for example, costs 0.00064% of network energy while ECDSA costs about 0.0091%. On the other hand, attacks cost the network about 4.7 times the cost of applying these techniques.

• Imaging Science in Dentistry
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• v.30 no.1
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• pp.55-62
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• 2000

Purpose: This study was performed to develop the electronic textbook (CD-rom title) about preclinical practice of oral and maxillofacial radiology, using multimedia technology with interactive environment. Materials and Methods: After comparing the three authoring methods of multimedia, i.e. programming language, multimedia authoring tool and web authoring tool, we determined the web authoring tool as an authoring method of our electronic textbook. Intel Pentium II 350 MHz IBM-compatible personal computer with 128 Megabyte RAM, Umax Powerlook flatbed scanner with transparency unit, Olympus Camedia l400L digital camera, ESS 1686 sound card, Sony 8 mm Handycam, PC Vision 97 pro capture board, Namo web editor 3.0, Photoshop 3.0, ThumbNailer, RealPlayer 7 basic and RealProducer G2 were used for creating the text document, diagram, figure, X-ray image, video and sound files. We made use of javascripts for tree menu structure, moving text bar, link button and spread list menu and image map etc. After creating all files and hyperlinking them, we burned out the CD-rom title with all of the above multimedia data, Netscape communicator and plug in program as a prototype. Results and Conclusions : We developed the dental radiology electronic textbook which has 9 chapters and consists of 155 text documents, 26 figures, 150 X-ray image files, 20 video files, 20 sound files and 50 questions with answers. We expect that this CD-rom title can be used at the intranet and internet environments and continuous updates will be performed easily.