• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.301-308
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• 2022

Algae is an indispensable primary producer in the ecosystem by supplying energy to consumers in the aquatic ecosystem, and is largely divided into green algae, blue-green algae, and diatoms. In the case of blue-green algae, the water temperature rises, which occurs in the summer and overgrows, which is the main cause of the algae bloom. Recently, the change in the occurrence time and frequency of the algae bloom is increasing due to climate change. Existing algae survey methods are performed by collecting water and measuring through sensors, and time, cost and manpower are limited. In order to overcome the limitations of these existing monitoring methods, research has been conducted to perform remote monitoring using spectroscopic devices such as multispectral and hyperspectral using satellite image, UAV, etc. In this study, we tried to confirm the possibility of species classification of remote monitoring through laboratory-scale experiments through algal culture and river water collection. In order to acquire hyperspectral images, a hyperspectral sensor capable of analyzing at 400-1000 nm was used. In order to extract the spectral characteristics of the collected river water for classification of algae species, filtration was performed using a GF/C filter to prepare a sample and images were collected. Radiation correction and base removal of the collected images were performed, and spectral information for each sample was extracted and analyzed through the process of extracting spectral information of algae to identify and compare and analyze the spectral characteristics of algae, and remote sensing based on hyperspectral images in rivers and lakes. We tried to review the applicability of monitoring.

• Journal of Microbiology
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• v.45 no.6
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• pp.541-546
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• 2007

Red algae are distributed globally, and the group contains several commercially important species. Griffithsia okiensis is one of the most extensively studied red algal species. In this study, we conducted expressed sequence tag (ESTs) analysis and synonymous codon usage analysis using cultured G. okiensis samples. A total of 1,104 cDNA clones were sequenced using a cDNA library made from samples collected from Dolsan Island, on the southern coast of Korea. The clustering analysis of these sequences allowed for the identification of 1,048 unigene clusters consisting of 36 consensus and 1,012 singleton sequences. BLASTX searches generated 532 significant hits (E-value <$10^{-4}$) and via further Gene Ontology analysis, we constructed a functional classification of 434 unigenes. Our codon usage analysis showed that unigene clusters with more than three ESTs had higher GC contents (76.5%) at the third position of the codons than the singletons. Also, the majority of the optimal codons of G. okiensis and Chondrus crispus belonging to Bangiophycidae were G-ending, whereas those of Porphyra yezoensis belonging to Florideophycidae were G-ending. An orthologous gene search for the P. yezoensis EST database resulted in the identification of 39 unigenes commonly expressed in two rhodophytes, which have putative functions for structural proteins, protein degradation, signal transduction, stress response, and physiological processes. Although experiments have been conducted on a limited scale, this study provides a material basis for the development of microarrays useful for gene expression studies, as well as useful information for the comparative genomic analysis of red algae.

• Journal of Oriental Neuropsychiatry
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• v.28 no.3
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• pp.217-230
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• 2017

Objectives: This study was conducted to review studies on somatization disorder in traditional Chinese medicine. Methods: We reviewed studies in the China National Knowledge Infrastructure (CNKI) to 2017. Keywords were 軀體化障碍, Somatization disorder, somatic symptom disorder. We included Randomized Controlled Trial (RCT), and excluded non-Randomized Controlled Trial (nRCT), non-related somatization disorder or traditional Chinese medicine, non-clinical trials, dissertations for degrees. Jadad scale and Cochrane Library's Risk of Bias (RoB) were used for assessment of the quality of studies. Results: Twelve studies were selected. The Chinese Classification of Mental Disorders-3 (CCMD-3) was most frequently used as diagnostic criteria for somatization disorder. As for outcome measurement, Hamilton Rating Scale for Depression (HAMD) was used most commonly. Meta-analysis of 10 studies revealed effective rate of Chinese Herbal Medicine groups (CHM) was significantly higher than Western Medicine groups (WM) (RR: 1.14, 95% CI: 1.02 to 1.27, p=0.02, $I^2=40%$). There was no significant difference in effective rate of CHM+WM and WM (RR: 1.12, 95% CI: 0.84 to 1.49, p=0.46, $I^2=83%$). And also, effective rate of Acupuncture group (Acu) revealed no significant difference compared to that of WM (RR: 1.17, 95% CI: 0.95 to 1.44, p=0.13, $I^2=84%$). For HAMD, there was significant difference in CHM vs, WM group and Acu vs. WM group. Quality of selected 12 RCTs was low. Conclusions: Therapies practiced in traditional Chinese medicine may be effective options for somatization disorder. treatment. For further clinical studies in Korean medicine, this study could be groundwork for development of diagnosis and treatment on somatization disorder.

• Korean Journal of Environmental Biology
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• v.29 no.4
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• pp.312-320
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• 2011

In order to estimate a better methodological factor to understand phytoplankton ecology between abundance and bio-volume of phytoplankton, each 1,160 phytoplankton data, including abundance, classification and chlorophyll $a$ concentration were collected in Korean coastal waters of Incheon (Yellow sea), Tongyeong (South sea), and Ulsan (East sea). Based on these data, phytoplankton bio-volume can be calculated through a geometric model. The correlation coefficient between abundance and chlorophyll $a$ concentration was higher than the coefficient between biovolume and chlorophyll $a$ concentration, because a small size phytoplankton has relatively dense chlorophyll contents compared with the proportion of chlorophyll in a large size phytoplankton. Thus, the interpretation using abundance to understand phytoplankton ecology in Korean coastal waters may be more effective than that using bio-volume.

• Asian Oncology Nursing
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• v.11 no.1
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• pp.74-82
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• 2011

Purpose: This study was performed to systematically review the recently published nursing intervention studies. Methods: The literature was identified through the Korean Education and Research Information Service (KERIS), the Korean Information Service System (KISS), and National Assembly Library websites. Key words such as breast cancer, nursing, and intervention were used. The factors analyzed are as follows: 1) the characteristics of studies and study populations, 2) the classification of interventions, 3) outcome indicators and their effects, and 4) effective interventions. Results: Thirty two studies were included. Seventeen studies used a single intervention such as aerobic dance, TaiChi, foot massage, aromatherapy, or a stress-reduction method. Fifteen studies used combined interventions, including education, exercise, counseling, support, yoga or meditation. The data on 47 outcome indicators and their effects were segregated into psycho/spiritual outcomes, stress coping, physical outcomes, cardiorespiratory function, symptom management, arm and shoulder functions, fatigue, and quality of life. Some interventions had positive effects on stress, fatigue, and functions of shoulder. Conclusion: Various interventions are available for breast cancer patients, and some have had positive effects. However, more studies are required to develop evidence-based practice guidelines for nursing interventions.

• The Journal of Korean Medicine
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• v.31 no.4
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• pp.49-62
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• 2010

Objectives: Current information & communication technology is advancing very rapidly and the ripple effects are spreading all over society traditional Korean medicine (TKM) is no exception. We draw up afuture plan and target system's architecture of KIOM's OASIS to follow the information change and reinforce the supporting infra for TKM research. Methods: First, we analyzed the information circumstances of western and eastern medicine, both overseas and domestic, especially investigating the detailed changes domestically. Second, we grasped the present conditions of OASIS and compared it with other information portals. Finally, we drew a future plan and system architecture from the analysis results. Results: First, the information status of western medicine is much more advanced than TKM's, and overseas information circumstances are likewise more developed than domestics. Second, we found that OASIS is performing the central research infra role well in TKM. Finally we designed an information system architecture which is composed of an infra layer, an application layer and a service layer. Conclusion: We must integrate information materials such as literature, research manpower, facilities and standards to make TKM's knowledge portal successful. In detail, we have to make TKM's information classification code, build up the electronic TKM library and offer complementary and alternative medicine (CAM) trends.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6535-6541
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• 2014

Background: To evaluate risk factors for upper extremity lymphedema due to breast cancer surgery. Materials and Methods: Clinical studies published on PubMed, Ovid, EMbase, and Cochrane Library from January 1996 to December 2012 were selected. Results: Twenty-five studies were identified, including 12,104 patients. Six risk factors related to the incidence of lymphedema after breast cancer treatment were detected: axillary lymph node dissection (OR=3.73, 95%CI 1.16 to 11.96), postoperative complications (OR=2.64, 95%CI 1.10 to 6.30), hypertension (OR=1.83, 95%CI 1.38 to 2.42), high body mass index (OR=1.80, 95%CI 1.30 to 2.49), chemotherapy (OR=1.38, 95%CI 1.07 to 1.79) and radiotherapy (OR=1.35, 95%CI 1.10 to 1.66). We found significant protective factors for lymphedema: pathologic T classification (OR=0.57, 95%CI 0.36 to 0.91) and stage (OR=0.60, 95%CI 0.39 to 0.93), while some factors, like age, number of positive lymph nodes, number of lymph node dissection, demonstrated no obvious correlation. Conclusions: Axillary lymph node dissection, postoperative complications, hypertension, body mass index, chemotherapy, radiotherapy are risk factors for lymphedema after breast cancer treatment. Attention should be paid to patients with risk factors to prevent the occurrence of lymphedema.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.54-62
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• 2009

To avoid the redundant investment on the project selection process, it is necessary to check whether the submitted research topics have been proposed or carried out at other institutions before. This is possible through the search engines adopted by the keyword matching algorithm which is based on boolean techniques in national-sized research results database. Even though the accuracy and speed of information retrieval have been improved, they still have fundamental limits caused by keyword matching. This paper examines implemented TFIDF-based algorithm, and shows an experiment in search engine to retrieve and give the order of priority for similar and redundant documents compared with research proposals, In addition to generic TFIDF algorithm, feature weighting and K-Nearest Neighbors classification methods are implemented in this algorithm. The documents are extracted from NDSL(National Digital Science Library) web directory service to test the algorithm.