• Korean Journal of Remote Sensing
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• v.11 no.2
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• pp.59-73
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• 1995

The study is focused on the analysis of geomorphological environment changes of alluvial bar in lower Kum river using satellite-based multitemporal/multisensor data. Landsat datas for environment changes analysis consists of Landset MSS(2 scenes) and Landset TM(7 scenes) acquired from 1979 to 1994. This study is to develop the analysis techniques for the environment change detection of using ratio, classification, false color composite etc, of Landsat data especially useful to the geomorphological study of tidal flats and river channels. The results of this study can be summarized as follows : 1. The lower Kum River alluvial bar have had rapid geomorphological changes after the construction of the temporary dam to block the river flowing in 1983. The most alluvial bar located in the river has both bankway growth, especially the allurival bar in the Lower Kum River had grown between 1983 to 1990. 2. After construction of the estuarine barrage, no remarkable geomorphological changes have been found in Kum River area but the growth and formation of new underwater bar has continued. The enormous materials was needed for the growth and formations of new underwater barrier oslands and bar would be supplied from the sea bottom and river sediment to diminish of stream velocity after construction of the estuarine barrage.

• Smart Structures and Systems
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• v.24 no.6
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• pp.803-812
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• 2019

Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.1-10
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• 2019

In this paper, we propose the track initiation algorithm based on the weighted score for TWS radar tracking. This algorithm utilizes radar velocity information to calculate the probabilistic track score and applies the Non-Maximum-Suppression(NMS) to confirm the targets to track. This approach is understood as a modification of a conventional track initiation algorithm in a probabilistic manner. Also, we additionally apply the weighted Hough transform to compensate a measurement error, and it helps to improve the track detection probability. We designed the simulator in order to demonstrate the performance of the proposed track initiation algorithm. The simulation result show that the proposed algorithm, which reduces about 40 % of a false track probability, is better than the conventional algorithm.

• Biomedical Science Letters
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• v.25 no.1
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• pp.15-22
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• 2019

The diagnosis of atopic dermatitis (AD) includes a test that checks allergen-mediated skin reactions and a method of measuring the total IgE and allergen-specific IgE in blood. However, these test methods are performed directly on the patient, which cause some pain or discomfort. In addition, the skin response test or IgE may result in false negative in about 20% of patients. In the present study, to identify specific biomarkers, HaCaT cells were used as a human keratinocyte that make up the skin, were treated IL-4 and IL-13 for 24 hours to induce a situation similar to keratinocytes in AD patients. In the HaCaT cells, pro-inflammatory cytokine such as IL-5, IL-6, and MCP-1 were increased by IL-4 and IL-13 and skin barrier proteins was reduced by IL-4 and L-13. This results showed that a situation similar to the stratum corneum of an actual patient is induced in HaCaT cells. And then the secretions of Kallikrein (KLK) 5 and KLK7 protease were checked by enzyme-linked immunosorbent assay (ELISA). It was specifically increased by IL-4 and IL-13. This showed that AD-related protease can be detected at the protein level using keratinocytes that can be taken in a non-invasive manner and suggested the possibility of applying it to AD diagnosis.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1069-1079
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• 2019

In this study, we developed a $PM_{10}$ forecasting model using DNN and Membership Function, and improved the forecasting performance. The model predicts the $PM_{10}$ concentrations of the next 3 days in the Seoul area by using the weather and air quality observation data and forecast data. The best model(RM14)'s accuracy (82%, 76%, 69%) and false alarm rate(FAR:14%,33%,44%) are good. Probability of detection (POD: 79%, 50%, 53%), however, are not good performance. These are due to the lack of training data for high concentration $PM_{10}$ compared to low concentration. In addition, the model dose not reflect seasonal factors closely related to the generation of high concentration $PM_{10}$. To improve this, we propose Julian date membership function as inputs of the $PM_{10}$ forecasting model. The function express a given date in 12 factors to reflect seasonal characteristics closely related to high concentration $PM_{10}$. As a result, the accuracy (79%, 70%, 66%) and FAR (24%, 48%, 46%) are slightly reduced in performance, but the POD (79%, 75%, 71%) are up to 25% improved compared with those of the RM14 model. Hence, this shows that the proposed Julian forecast model is effective for high concentration $PM_{10}$ forecasts.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.1-10
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• 2021

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

• Parasites, Hosts and Diseases
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• v.60 no.4
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• pp.295-299
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• 2022

Malaria elimination and control require prompt and accurate diagnosis for treatment plan. Since microscopy and rapid diagnostic test (RDT) are not sensitive particularly for diagnosing low parasitemia, highly sensitive diagnostic tools are required for accurate treatment. Molecular diagnosis of malaria is commonly carried out by nested polymerase chain reaction (PCR) targeting 18S rRNA gene, while this technique involves long turnaround time and multiple steps leading to false positive results. To overcome these drawbacks, we compared highly sensitive cytochrome oxidase gene-based single-step multiplex reaction with 18S rRNA nested PCR. Cytochrome oxidase (cox) genes of P. falciparum (cox-III) and P. vivax (cox-I) were compared with 18S rRNA gene nested PCR and microscopy. Cox gene multiplex PCR was found to be highly specific and sensitive, enhancing the detection limit of mixed infections. Cox gene multiplex PCR showed a sensitivity of 100% and a specificity of 97%. This approach can be used as an alternative diagnostic method as it offers higher diagnostic performance and is amenable to high throughput scaling up for a larger sample size at low cost.