• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.417-424
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• 2007

Mesh networks using WLAN technology have been paid attention as a key wireless access technology. However, many technical issues still exist for its successful deployment. One of those issues is the routing problem that addresses the path setup through a WLAN mesh network for the data exchanges between a station and a wired network. Since the characteristics of a WLAN mesh network can be very dynamic, the use of single routing protocol would not fit for all environments whether it is reactive or proactive. Therefore, it is required to develop an adaptive routing protocol that modifies itself according to the changes in the network parameters. As a logical first step for the development, an analytical model considering all the dynamic features of a WLAN mesh network is required to evaluate the performance of a reactive and a proactive routing scheme. In this paper, we propose an analytical model that makes us scrutinize the impact of the network and station parameters on the performance of each routing protocol. Our model includes the size of a mesh network, the density of stations, mobility of stations. and the duration of network topology change. We applied our model to the AODV that is a representative reactive routing protocol and DSDV that is a representative proactive routing protocol to analyze the tradeoff between AODV and DSDV in dynamic network environments. Our model is expected to help developing an adaptive routing protocol for a WLAN mesh network.

• The Korean Journal of Food And Nutrition
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• v.34 no.5
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• pp.449-457
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• 2021

This study analyzed the nutritional composition (proximate composition, total dietary fiber, calories, minerals, fatty acids, and amino acids) of 10 noodle products (tteok ramyun, jjamppong ramyun, kimchi ramyun, instant udon, cup ramyun, jajangmyun, bibimmyun, cream spaghetti, ssalguksu, and milmyun), which account for 85% of the cumulative intake of one or more key nutrients, using data from the 7^th Korea National Health and Nutrition Examination Survey. The moisture contents of bibimmyun, jajangmyun, and cream spaghetti were lower than those of the other noodle products, whereas the crude fat, crude protein, carbohydrate, and calorie contents were the highest. Cream spaghetti had the highest mineral, fatty acid, and amino acid contents, followed by bibimmyun and jajangmyun. Ssalguksu had the lowest contents of most nutrients. These data could be used to populate a food composition database, which can provide consumers with the nutritional information about frequently consumed noodle products.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.151-155
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• 2021

The application of environmental DNA in the domestic ecosystem is also accelerating, but the processing and analysis of the produced data is limited, and doubts are raised about the reliability of the analyzed and produced biological taxa identification data, and the sample medium (target sample, water, air, sediment, Gastric contents, feces, etc.) and quantification and improvement of analysis methods are also needed. Therefore, in order to secure the reliability and accuracy of biodiversity research using the environmental DNA of the domestic ecosystem, it is a process of actively using the database accumulated through ecological taxonomy and undergoing verification procedures, and experts verifying the resolution of the data increased by gene sequence analysis. This is absolutely necessary. Environmental DNA research cannot be solved only by applying molecular biology technology, and interdisciplinary research cooperation such as ecology-taxa identification-genetics-informatics is important to secure the reliability of the produced data, and researchers dealing with various media can approach it together. It is an area in desperate need of an information sharing platform that can do this, and the speed of development will proceed rapidly, and the accumulated data is expected to grow as big data within a few years.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.349-359
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• 2022

The purpose of this study is to add and revalidate items of learning cognition and learning emotion factors for online use of the K-LSS for junior college. It is important for self-reflection and improvement of academic achievement to specifically explore and analyze the sub-factors of learning cognition, learning behavior, and learning emotion for each item that can affect the learning strategy of junior college students. The added items are two items for diagnosing the concentration of attention in the learning information processing process of the learning cognitive factor and two questions about the interpersonal anxiety factor for diagnosing the level of anxiety about others in the learning emotional factor. The study area was conducted in 5 areas nationwide, and the subjects of the study were 923 junior college students excluding 327 respondents who answered insincerity. The K-LSS_r scale is a learning strategy diagnosis scale of 52 questions composed of three sub-elements of learning cognition (18 questions), learning emotion (15 questions), and learning behavior (19 questions), and reliability for generalization in this study. As a result of the verification, Cronbach's α coefficient of the entire scale was .896, and Cronbach's α coefficient of the three factors ranged from .876 to .910. The half-segment reliability coefficient of the scale was .858 in total, and the half-segment reliability coefficients of the three factors ranged from .792 to .843. The test-retest reliability verification result for 3 weeks for 350 Junior college Students in 5 regions was .884, and the validity test for generalization also confirmed that the recruitment validity is significant.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.297-304
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• 2021

Commercial obfuscation tools (protectors) aim to create difficulties in analyzing the operation process of software by applying obfuscation techniques and Anti-reversing techniques that delay and interrupt the analysis of programs in software reverse engineering process. In particular, in case of virtualization detection and anti-debugging functions, the analysis tool exits the normal execution flow and terminates the program. In this paper, we analyze Anti-reversing techniques of executables with Debugger Detection and Viralization Tools Detection options through VMProtect 3.5.0, one of the commercial obfuscation tools (protector), and address bypass methods using Pin. In addition, we predicted the location of the applied obfuscation technique by finding out a specific program termination routine through API analysis since there is a problem that the program is terminated by the Anti-VM technology and the Anti-DBI technology and drew up the algorithm flowchart for bypassing the Anti-reversing techniques. Considering compatibility problems and changes in techniques from differences in versions of the software used in experiment, it was confirmed that the bypass was successful by writing the pin automation bypass code in the latest version of the software (VMProtect, Windows, Pin) and conducting the experiment. By improving the proposed analysis method, it is possible to analyze the Anti-reversing method of the obfuscation tool for which the method is not presented so far and find a bypass method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.