• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1159-1174
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• 2014

As the use of smartphones and tablet PCs has exploded in recent years, there are many occasions where such devices are used for treating sensitive data such as financial transactions. Naturally, many types of attacks have evolved that target these devices. An attacker can capture a password by direct observation without using any skills in cracking. This is referred to as shoulder surfing and is one of the most effective methods. There has been only a crude definition of shoulder surfing. For example, the Common Evaluation Methodology(CEM) attack potential of Common Criteria (CC), an international standard, does not quantitatively express the strength of an authentication method against shoulder surfing. In this paper, we introduce a shoulder surfing risk calculation method supplements CC. Risk is calculated first by checking vulnerability conditions one by one and the method of the CC attack potential is applied for quantitative expression. We present a case study for security-enhanced QWERTY keyboard and numeric keypad input methods, and the commercially used mobile banking applications are analyzed for shoulder surfing risks.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.12
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• pp.295-300
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• 2018

The rapid development of IT, many conventional passive jobs have been automated. This automation increases the leisure time of many people and various services are being developed for them. In addition, with the advent of smart devices that are compact and portable, it is possible to use various internet services without any time and place discretion. Various studies based on virtualization are under way to efficiently store and process large data generated by many devices and services. Desktop Storage Virtualization (DSV), which integrates and provides users with on-premise-based distributed desktop resources during these studies, uses virtualization to consolidate unused resources within distributed, legacy desktops. This DSV is very important for providing high reliability to users. In addition, research on hierarchical structure and resource integration for efficient data distribution storage processing in a distributed desktop-based resource integration environment is underway. However, there is a lack of research on efficient operation in case of server failure in on-premise resource integration environment. In this paper, we propose Non-disruptive Server Management (NSM) which can actively cope with the failure of desktop server in distributed desktop storage environment based on on-premise. NSM is easy to add and remove desktops in a desktop-based integrated environment. In addition, an alternative server is actively performed in response to a failure occurrence.

• Knowledge Management Research
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• v.23 no.1
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• pp.47-67
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• 2022

Computer and Internet information technologies(ICTs) have changed the modern service industry and people's life style. In particular, the global spread of COVID-19 has attracted more attention to contact service types such as self-service technology. With the increase in labor costs and the enhancement of consumer self-awareness, more and more companies transfer part of their work to customers through their own service technology. This study seeks to answer the following questions. (1) Do technology difficulty and safety perception affect customer value recognition in the self-service technologies? (2) Does customer value recognition influence the intention to use such technologies continuously? This study conducted an empirical analysis with 327 samples to validate the influence of self-service characteristics(technology difficulty and safety perception) on customer value recognition and continuous utilization intentions. Also, it analyzes the moderating effects of age and frequency of use on the relationship between self-service characteristics and customer value recognition. The study results show that the technology difficulty does not affect the customer's perceived value recognition; and the higher the customer's value recognition, the higher the intention of continuous use.

• Korean Journal of Optics and Photonics
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• v.33 no.4
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• pp.146-158
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• 2022

The detector of a focal-plane-array mid-wave infrared (MWIR) camera has different response characteristics for each detector pixel, resulting in nonuniformity between detector pixels. In addition, image nonuniformity occurs due to heat generation inside the camera during operation. To solve this problem, in the process of camera manufacturing it is common to use a gain-and-offset table generated from a blackbody to correct the difference between detector pixels. One method of correcting nonuniformity due to internal heat generation during the operation of the camera generates a new offset value based on input frame images. This paper proposes a technique for dividing an input image into block image patches and generating offset values using only homogeneous patches, to correct the nonuniformity that occurs during camera operation. The proposed technique may not only generate a nonuniformity-correction offset that can prevent motion marks due to camera-gaze movement of the acquired image, but may also improve nonuniformity-correction performance with a small number of input images. Experimental results show that distortion such as flow marks does not occur, and good correction performance can be confirmed even with half the number of input images or fewer, compared to the traditional method.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.53-62
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• 2021

After the first to present projected moving pictures to audiences, the film industry has been reshaping along with technological advancements. Through the full-scale introduction of visual effects-oriented post-production and digital technologies in the film-making process, the film industry has not only undergone significant changes in the production, but is also embracing the cutting edge technologies broadly and expanding the scope of industry. Not long after the change to digital cinema, the concept of artificial intelligence, first known at the Dartmouth summer research project in 1956, before the digitalization of film, is expected to bring about a big transformation in the film industry once again. Large volume of clear digital data from digital film-making makes easy to apply recent artificial intelligence technologies represented by machine learning and deep learning. The use of artificial intelligence techniques is prominent around major visual effects studios due to automate many laborious, time-consuming tasks currently performed by artists. This study aims to predict how artificial intelligence technology will change the film industry in the future through analysis of visual effects production cases using artificial intelligence technology as a production tool and to discuss the industrial potential of artificial intelligence as visual effects technology.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.117-124
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• 2023

Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.

• Smart Media Journal
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• v.11 no.10
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• pp.89-96
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• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.