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Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the attack graph, which is created based on the vulnerability database, using machine learning and deep learning. Second, it generates the attack graph using network topology and system information with a machine learning model that is trained with the attack graph generated from the vulnerability database. We construct the dataset for attack graph generation with topological and system information. The attack graph generation problem is recast as a multi-output learning and binary classification problem. It shows attack path detection accuracy of 89.52% in the multi-output learning approach and 80.68% in the binary classification approach using the in-house dataset, respectively.

There is no doubt that CNN has made remarkable technological developments as the core technology of computer vision, but the pooling technique used for CNN has its own issues. This study set out to solve the issues of the pooling technique by proposing conditional min pooling and a restructured convolutional neural network that improved the pooling structure to ensure efficient use of the conditional min pooling. Some Caltech 101 and crawling data were used to test the performance of the conditional min pooling and restructured convolutional neural network. The pooling performance test based on Caltech 101 increased in accuracy by 0.16~0.52% and decreased in loss by 19.98~28.71% compared with the old pooling technique. The restructured convolutional neural network did not have a big improvement in performance compared to the old algorithm, but it provided significant outcomes with similar performance results to the algorithm. This paper presents the results that the loss rate was reduced rather than the accuracy rate, and this result was achieved without the improvement of convolution.

The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis, FOV and frame immersion were set through typification, the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed.

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13&ndash, 14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

The purpose of this study was to investigate the protective effects of beet root (Beta vulgaris var. rubra) in lipopolysaccharide (LPS) and alcohol induced liver damage. Beta vulgaris ethanol extract (BVEE) showed good antioxidant activity in the contents of polyphenol and flavonoid compounds, and the electron-donating ability and ABTS(+) radical scavenging activity. As for anti-inflammatory effect in RAW 264.7 cells, inhibition rate of nitric oxide production was increased in dose dependent manner. In hepatotoxicity model induced by LPS and alcohol in rat, BVEE significantly decreased serum AST, ALT and γ-GTP concentrations in a dose-dependent manner. The histopathological changes after H&E staining showed that fat accumulation and inflammatory cell infiltration were decreased by BVEE. The collagen fibers around the central lobule observed by Masson’s trichrome staining were also decreased by BVEE. In addition, as for the immunohistochemical staining and Transmission electron microscopy, BVEE improved morphological characteristics of damaged liver lesion. The increased mRNA expressions of NF-κB, MAPK1, MAPK3, CYP2E1, and α-SMA were significantly decreased in BVEE treated group. These results indicated that BVEE would have protective effects in hepatotoxicity by altering various indicators related to the liver damage induced by LPS or alcohol.

해부학 교육은 보건의학계열 학생들에게 가장 기본이 되는 과목이며 중요하다. 해부학 교육의 질적 개선 및 다양성을 위해서 임상병리학과, 물리치료학과, 작업치료학과 학생 130명을 대상으로 설문을 통해 기존의 해부학 교육과 카데바실습(cadaver dissection)의 만족도, 이해도, 인식을 조사하였다. 분석 결과, 해부학 ...

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

This paper set out to revise and improve existing autonomous driving models using reinforcement learning, thus proposing a reinforced autonomous driving prediction model. The paper conducted training for a reinforcement learning model using DQN, a reinforcement learning algorithm. The main aim of this paper was to reduce the time spent on training and improve self-driving performance. Rewards for reinforcement learning agents were developed to mimic human driving behavior as much as possible. High rewards were given for greater distance travelled within lanes and higher speed. Negative rewards were given when a vehicle crossed into other lanes or had a collision. Performance evaluation was carried out in urban environments without pedestrians. The performance test results show that the model with the collision prevention model exhibited faster performance improvement within the same time compared to when the model was not applied. However, vulnerabilities to factors such as pedestrians and vehicles approaching from the side were not addressed, and the lack of stability in the definition of compensation functions and limitations with respect to the excessive use of memory were shown.

요 약석탄과 석유를 대신할 새로운 대체 에너지의 연구 및 개발은 지난 수년간 계속해서 연구되고 있다. 최근엔 유한 에너지 가격 상승으로 인하여 무공해 신재생 에너지의 관심이 증가하고 있다. 특히 기존에 활발히 연구되고 상용화된 신재생 에너지 분야 중 태양에너지는 태양광 에너지에 주된 연구가 이루어져 있다. 그러나 태양광 에너지의 효율성은 이미 포화상태에 도달하였다. 이에 본 논문에서는 태양광이 아닌 태양열 에너지를 활용할 수 있는 태양광 추적형 파라볼릭 집열장치를 제안한다. 제안하는 태양열 집열장치는 효과적으로 태양열을 집열하기 위하여 파라볼릭 형태로 제안하고, 계절 및 시기에 따른 태양의 위치변화를 감지할 수 있도록 5축 센서 방식의 태양광 추적형 트래킹 기술을 결합한 태양열 집열장치의 프로토타입을 제안한다. 또한 집열장치의 효율적인 관리를 위하여 관리자 인터페이스를 설계 및 구현한다.ABSTRACT

This study set out to invent an Information and Communication Technologies (ICT)-based smart Acer mono sap collection electric device to make efficient use of the labor force by reducing inefficient activities of old manual work to record sap exudation and state information. Based on the assumption that environmental information would have close connections with Acer mono sap exudation to reinforce the competitive edge of production in forest products, the study analyzed correlations between Acer mono sap exudation and environmental information and predicted Acer mono exudation. A smart collection of electric devices would gather data about Acer mono sap exudation per hour on outdoor temperature, humidity, conductivity, and wind direction and velocity, and was installed in four areas in the Republic of Korea, including Sancheong, Gwangyang, Geoje, and Inje. Collected data were used to analyze correlations between environmental information and Acer mono sap exudation using four different algorithms, including linear regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and random forest, to predict Acer mono sap exudation. Remarkable outcomes were obtained across all the algorithms except for linear regression, demonstrating close connections between environmental information and Acer mono sap exudation. The random forest model, which showed the most outstanding performance, was used to make a mobile app capable of providing predicted Acer mono sap exudation and collected environmental information.

The original version of this article unfortunately contained a mistake. The sequence of the author names was incorrect. The corrected sequence is given above.

Purpose: This study analyzed the names of optical shops currently used in Korea and is to investigate the optical shop names that customers prefer. Methods: We used the 351 names of optical shop all over the country as a sample, and surveyed 296 people aged between 20s~30s in Seoul and Gyeonggi provinces to collect the data. Results: The most used names are currently English names (54.4%) and names of with 2 syllables (30.5%) and 3 syllables (32.5%). For more than 4 syllables, the names included Eye (26.0%) and Glasses (24.3%) are being used a lot. For the name awareness, Korean names (63.0%) was the highest and followed by 1syllable (34.5%) and 5 syllables (20.9%). Conclusions: For the improvement of optical shop awareness, Korean name with 1 to 3 syllables and words related glasses are recommended.

There is a shortage of mobile application systems readily applicable to the field of crop cultivation in relation to diseases and insect pests directly connected to the quality of crops. Most of system have been devoted to diseases and insect pests that would offer full predictions and basic information about diseases and insect pests currently. But for lack of the instant diagnostic functions seriously and the field of crop cultivation, we design and implement a crop cultivation field-oriented smart diseases and insect pests information retrieval system based on mobile for u-Farm. The proposed system had such advantages as providing information about diseases and insect pests in the field of crop cultivation and allowing the users to check the information with their smart-phones real-time based on the Lucene, a search library useful for the specialized analysis of images, and JSON data structure. And it was designed based on object-oriented modeling to increase its expandability and reusability. It was capable of search based on such image characteristic information as colors as well as the meta-information of crops and meta-information-based texts. The system was full of great merits including the implementation of u-Farm, the real-time check, and management of crop yields and diseases and insect pests by both the farmers and cultivation field managers.

Standard protocols are required for the accurate measurement of global soil respiration rates. This research aimed to determine the appropriate sampling frequency for pine forest soil respiration rates by quantifying the optimal number of measurement chambers and Q10 values. We used an automatic open/closed chamber system with 16 chambers to obtain a high level of reliability about soil respiration rates. On average, 57 and 14 chambers were required to obtain 10% and 20% margins of error at the 95% confidence level, while 38 and 9 chambers were required for these 2 error margins at the 90% confidence level, respectively. Thus, our data were in the range of the 20% margin of error at the 95% confidence level. ANOVA statistical tests also separated the minimum required measurement interval into two groups using Q10 values based on actual measured temperatures: (1) short-term sampling intervals of 1 and 3 days and (2) long-term intervals of 7, 14, 20, and 30 days. Our results indicate that 3 days is the opti...

Purpose: This study aimed to analyze the research trends of Ophthalmic Optic by targeting the papers published in the Journal of Korean Ophthalmic Optics Society and serve it as a basis for future researches. Methods: This study was conducted through a statistical analysis of the general characteristics, researcher characteristics, research characteristics and the research trends from total 990 papers, from the first issue to the last issue in 2014, published in the Journal of Korean Ophthalmic Optics Society. Results: Most common number of the authors of the published papers were two or three, respectively 26.8%. Most of the lead authors belong to the schools in Jeolla-do province (31.3%). Most of the educational system of which lead authors belong to is four-year-course system (43.0%). Most common research methods were optometric tests (40.9%). The most common subjects of researches are about the field of optometry (29.8%). The study shows that there were significant differences in the research methods depending on the school systems of the universities to which the lead authors belong (p=0.000). There also were significant differences in the school systems of the universities to which the lead authors belong, and research methods depending on the research subjects (p=0.000). There were meaningful differences according to the period divisions (1996 to 2004 and 2005 to 2014), i.e. the number of authors has been growing, and it is the same case for the number of the authors who belong to four-year-course universities (p=0.000). The number of papers on optometry and contact lenses have increase. The benefits of the research funds have also increased (p