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A Proposal On Digital Signature For FAX Document Using DM Algorithm (FAX 문서에 대한 DM 합성 알고리즘을 이용한 디지털 서명의 제안)

  • 박일남;이대영
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.7 no.2
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    • pp.55-72
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    • 1997
  • This paper presents a digital signature scheme for facsimile document which directly embeds a signature onto the document. We use multiple reference lines which have been scanned just before and modify each distance of changing pels both on the reference line specified by key and on the coding line with a single bit of the signature data. The time to take in signature is reduced by spreading of signature. Non-repudiation in origin, the 3rd condition of digital signature is realized by proposed digital signature scheme. The transmitter embeds the signature secretly and transfers it, and the receiver makes a check of any forgery on the signature and the document. This scheme is compatible with the ITU-T.4(CCITT G3 or G4 facsimile standards). The total amount of data transmitted and the image quality are about the same to that of the original document, and thus a third party notices that no signature is embedded on the document.

Lane Model Extraction Based on Combination of Color and Edge Information from Car Black-box Images (차량용 블랙박스 영상으로부터 색상과 에지정보의 조합에 기반한 차선모델 추출)

  • Liang, Han;Seo, Suyoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.1
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    • pp.1-11
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    • 2021
  • This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

Prediction of Food Franchise Success and Failure Based on Machine Learning (머신러닝 기반 외식업 프랜차이즈 가맹점 성패 예측)

  • Ahn, Yelyn;Ryu, Sungmin;Lee, Hyunhee;Park, Minseo
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.4
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    • pp.347-353
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    • 2022
  • In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

Machine Learning-Based Malicious URL Detection Technique (머신러닝 기반 악성 URL 탐지 기법)

  • Han, Chae-rim;Yun, Su-hyun;Han, Myeong-jin;Lee, Il-Gu
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.3
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    • pp.555-564
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    • 2022
  • Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

Recovery of Ammonia Nitrogen using Gas-permeable Membranes (기체투과막을 이용한 암모니아성 질소 회수방안)

  • Lee, Sang-hun;Chae, Sang Yeop
    • Membrane Journal
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    • v.32 no.3
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    • pp.191-197
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    • 2022
  • Ammonia nitrogen can be effectively recovered from livestock manure waste, etc. by using the gas permeable membrane technology. In this case, ammonia gas in the waste passes through the pores in one-side of membrane, impregnated in waste, and then reach the opposite side of the membrane. The permeated ammonia gas molecules are captured and recovered by acid (such as sulfuric acid) in the solution existing on the opposite side of the membrane. In order to improve ammonia nitrogen removals in the inlet part, high pH should be maintained in the feed waste including ammonia nitrogen to recover, which requires the cost of the chemical. To resolve this issue, previous studies tested various methods, for example, utilization of cheap calcium hydroxide or aeration together with inhibition of unwanted nitrification. The gas permeable membranes used for the recovery of ammonia nitrogen may be characterized, not only by proper heat and chemical resistance, but also by hydrophobicity, allowing selective ammonia gas permeation through the hydrophobic membrane pores. Future research should consider the relevant pilot or upscale processes using on-site wastes with various properties, and identify the optimal design/operation conditions as well as economic feasibility improvement plans.

Towards Group-based Adaptive Streaming for MPEG Immersive Video (MPEG Immersive Video를 위한 그룹 기반 적응적 스트리밍)

  • Jong-Beom Jeong;Soonbin Lee;Jaeyeol Choi;Gwangsoon Lee;Sangwoon Kwak;Won-Sik Cheong;Bongho Lee;Eun-Seok Ryu
    • Journal of Broadcast Engineering
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    • v.28 no.2
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    • pp.194-212
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    • 2023
  • The MPEG immersive video (MIV) coding standard achieved high compression efficiency by removing inter-view redundancy and merging the residuals of immersive video which consists of multiple texture (color) and geometry (depth) pairs. Grouping of views that represent similar spaces enables quality improvement and implementation of selective streaming, but this has not been actively discussed recently. This paper introduces an implementation of group-based encoding into the recent version of MIV reference software, provides experimental results on optimal views and videos per group, and proposes a decision method for optimal number of videos for global immersive video representation by using portion of residual videos.

Efficient Tomography System of Electron Microscopy using Selective Filtering (선택적 Filtering을 이용한 효율적 전자현미경 Electron Tomography 시스템)

  • Jung, Won-Goo;Cho, Hye-Jin;Park, Seong Oak;Chae, Hee-Su;Je, A-Reum;Lee, Kyoung Hwan;Jung, Hyun Suk;Kweon, Hee-Seok
    • Annual Conference of KIPS
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    • 2009.11a
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    • pp.395-396
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    • 2009
  • Electron tomography를 이용한 3차원적 영상 시각화는 electron microscopy를 통해 하나의 실험 대상으로부터 연속된 이미지를 생산함으로써 이루어진다. 이미지 데이터 내부에는 대용량의 정보값을 포함하고 있어 3차원 구조물로의 변환이 가능하다. electron tomography 작업 과정 중 고해상도 원본 이미지에 pattern recognition 알고리즘이 적용된 필터링을 적용하면 실험에 필요한 데이터의 정보 손실을 최소화한 상태에서 electron tomography 시스템의 효율성을 높일 수 있다. 또한 tomographic econstruction이 진행되는 각 단계에 hanning windowing을 적용하면 불필요한 정보 값이나 노이즈 등을 효과적으로 제거할 수 있다. 윤곽선 데이터의 효과적 활용을 위하여 sobel 필터 처리를 할 경우 관찰하고자 하는 대상의 윤곽선 특징을 뚜렷하게 시각화 할 수 있었다. 본 연구를 통하여 데이터의 시각화 과정에서 실험의 신뢰성 확보를 위해 원본 이미지를 기반으로 하는 tomogram과 필터링을 적용한 tomogram을 비교하여 최종 결과물의 정확도를 높이고, electron tomography를 통한 결과물의 질적 향상을 유도할 수 있음을 확인하였다.

A Case Study on Text Analysis Using Meal Kit Product Review Data (밀키트 제품 리뷰 데이터를 이용한 텍스트 분석 사례 연구)

  • Choi, Hyeseon;Yeon, Kyupil
    • The Journal of the Korea Contents Association
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    • v.22 no.5
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    • pp.1-15
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    • 2022
  • In this study, text analysis was performed on the mealkit product review data to identify factors affecting the evaluation of the mealkit product. The data used for the analysis were collected by scraping 334,498 reviews of mealkit products in Naver shopping site. After preprocessing the text data, wordclouds and sentiment analyses based on word frequency and normalized TF-IDF were performed. Logistic regression model was applied to predict the polarity of reviews on mealkit products. From the logistic regression models derived for each product category, the main factors that caused positive and negative emotions were identified. As a result, it was verified that text analysis can be a useful tool that provides a basis for maximizing positive factors for a specific category, menu, and material and removing negative risk factors when developing a mealkit product.

Comparison of Machine Learning Techniques in Urban Weather Prediction using Air Quality Sensor Data (실외공기측정기 자료를 이용한 도심 기상 예측 기계학습 모형 비교)

  • Jong-Chan Park;Heon Jin Park
    • The Journal of Bigdata
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    • v.6 no.2
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    • pp.39-49
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    • 2021
  • Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

A Study on Interactive Talking Companion Doll Robot System Using Big Data for the Elderly Living Alone (빅데이터를 이용한 독거노인 돌봄 AI 대화형 말동무 아가야(AGAYA) 로봇 시스템에 관한 연구)

  • Song, Moon-Sun
    • The Journal of the Korea Contents Association
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    • v.22 no.5
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    • pp.305-318
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    • 2022
  • We focused on the care effectiveness of the interactive AI robots. developed an AI toy robot called 'Agaya' to contribute to personalization with more human-centered care. First, by applying P-TTS technology, you can maximize intimacy by autonomously selecting the voice of the person you want to hear. Second, it is possible to heal in your own way with good memory storage and bring back memory function. Third, by having five senses of the role of eyes, nose, mouth, ears, and hands, seeking better personalised services. Fourth, it attempted to develop technologies such as warm temperature maintenance, aroma, sterilization and fine dust removal, convenient charging method. These skills will expand the effective use of interactive robots by elderly people and contribute to building a positive image of the elderly who can plan the remaining old age productively and independently