• 인터넷정보학회논문지
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• 제21권2호
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• pp.81-90
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• 2020

본 연구는 인공지능의 국방 분야 활용 시 데이터 위·변조 방지를 위한 블록체인 기술의 적용방안을 연구 하는데 목적이 있다. 인공지능은 빅 데이터를 다양한 기계학습 방법론을 적용하여 군집화하거나 분류하여 예측하는 기술이며 미국을 비롯한 군사 강대국은 기술의 완성단계에 이르렀다. 만약 데이터를 기반으로 하는 인공지능의 데이터 위·변조가 발생한다면 데이터의 처리과정이 완벽하더라도 잘못된 결과를 도출할 것이며 이는 가장 큰 적의 위험요소가 될 수 있고 데이터의 위·변조는 해킹이라는 형태로 너무나 쉽게 가능하다. 만약 무기화된 인공지능이 사용하는 데이터가 북한으로부터 해킹되어 조작되어 진다면 예상치 못한 곳의 공격이 발생할 수도 있다. 따라서 인공지능의 사용을 위해서는 데이터의 위·변조를 방지하는 기술이 반드시 필요하다. 데이터의 위·변조 방지는 해수함수로 암호화된 데이터를 연결된 컴퓨터에 분산 저장하여 한 대의 컴퓨터가 해킹되더라도 연결된 컴퓨터의 과반 이상이 동의하지 않는 한 데이터가 손상되지 않는 기술인 블록체인을 적용함으로써 문제를 해결할 수 있을 것으로 기대한다.

• 반도체디스플레이기술학회지
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• 제15권1호
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• pp.27-32
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• 2016

A new lane detection algorithm is proposed for the analysis of DNA fingerprints from a polymerase chain reaction (PCR) gel electrophoresis image. Although several research results have been previously reported, it is still challenging to extract lanes precisely from images having abrupt background brightness difference and bent lanes. We propose an edge based algorithm for calculating the average lane width and lane cycle. Our method adopts sub-pixel algorithm for extracting rising-edges and falling edges precisely and estimates the lane width and cycle by using k-means clustering algorithm. To handle the curved lanes, we partition the gel image into small portions, and track the lane centers in each partitioned image. 32 gel images including 534 lanes are used to evaluate the performance of our method. Experimental results show that our method is robust to images having background difference and bent lanes without any preprocessing.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Asian-Australasian Journal of Animal Sciences
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• 제31권7호
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• pp.1062-1071
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• 2018

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the 'transcriptome' has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern 'omics' and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid 'biomarker signature' can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional 'biomarker signatures' based on the physiological response triggered by illegal substances.

• 한국통신학회논문지
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• 제35권5B호
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• pp.801-808
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• 2010

무선 센서 네트워크의 가장 중요한 요구사항인 효율적인 에너지 사용을 위해 클러스터 구조를 가진 계층 기반 라우팅 프로토콜로 LEACH(Low Energy Adaptive Clustering Hierarchy)가 제안되었다. LEACH 프로토콜은 수많은 센서 노드들이 임의 개수의 클러스터를 구성하고, 각 클러스터에는 멤버 노드와 클러스터 헤드가 존재한다. 멤버 노드들은 데이터를 감지하여 자신이 소속된 클러스터 헤드에게 전송하고 클러스터 헤드는 멤버 노드에게 전송받은 데이터를 융합하여 Base Station(BS)에게 전송한다. LEACH 프로토콜에서는 클러스터 헤드가 균등하게 분포되는 것을 보장하지 않고, 융합된 데이터를 BS에게 직접 전송하기 때문에 에너지 소모가 크다는 제한사항을 가지고 있다. 본 연구에서는 이러한 제한사항을 개선하기 위해, 클러스터 헤드간 체인을 형성해 멀리 떨어진 BS가 아니라 가장 가까운 인접 클러스터 헤드에게 데이터를 전송하고, 최종적으로 BS와 가장 가까운 클러스터 헤드가 데이터를 융합해 전송하는 LECEEP를 제안한다. 시뮬레이션 결과 LECEEP가 LEACH 프로토콜과 비교하여 시간 경과에 따른 전체 네트워크의 에너지 소모 및 생존 노드 수 측면에서 우수함을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제16권2호
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• pp.225-233
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• 2011

주식 가격이나 경제 지표, 사회적 현상의 추세나 변화 등은 통상 시간에 따라 변화하기 때문에 시계열 자료로 구분된다. 시계열 자료는 시간 축에 대해 변화하는 자료의 표현 가치뿐 아니라 그 변화 추세나 향후 방향성까지 제시할 수 있다는 점에서 이에 대한 방법론에 대해 많은 연구와 노력이 지속되어 왔다. 본 논문에서는 전통적으로 예측 모형을 구축하여 예측하는 방법을 취하되 그 모형이 복잡하고 정교한 모델을 활용하여 예측 정확도를 높이려는 시도와는 달리 자료 클러스터링 방법과 자료 구간 선정을 통해 예측정확도를 높이려 시도하였다. 기본 모델은 마코프 모델이다. 구간별 유사 구간을 추출하여 모델링하는 구간별 모델링 방법과 클러스터링을 통한 그룹별 모델링을 통해 모델의 예측정확도를 개선하려 시도하였다. 실험을 통해 클러스터링을 거친 그룹별 마코프 모델이 정확도를 개선 시켰으나 예측율은 현저히 떨어지는 결과를 낳았다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권3호
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• pp.141-148
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• 2017

인간의 위치 이동 데이터를 모바일 기기에서 수집한 위치 정보를 이용해 얻을 수 있게 되면서, 위치 정보를 어떻게 이용할 수 있는지 그 활용 방안이 중요시 되고 있다. 이 연구에 앞서 위치 정보에 포함된 위치 정보와 시간 정보를 이용한 개인 이동성 모델 도출 연구가 선행되었다. 이동성 모델의 개념을 집단으로 확장하여 특정 집단에 속한 사람들의 개인 이동성 모델을 이용한 집단 이동성 모델을 도출하는 방법에 대해서 연구했고, 두 명의 개인 이동성 모델을 이용한 집단 이동성 모델과 그 모델을 표현하는 Markov 모델을 생성할 수 있었다. 본 논문에서는 세명 이상의 개별 이동 모델을 포함하는 사람의 이동성 모델을 생성하고 집단 모델 내 군집간의 확률 기반 Markov 모델을 도출하는 방법에 대해 소개한다. 또한 GPGPU 기법을 통해 생성 시간을 줄이는 기법을 이용하여 실용화를 고려하였다.

• Journal of Microbiology and Biotechnology
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• 제21권12호
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• pp.1336-1344
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• 2011

This study details and examines a novel ligation-mediated polymerase chain reaction (LM-PCR) method. Named the LM-PCR/Shifter, it relies on the use of a Class IIS restriction enzyme giving restriction fragments with different 4-base, 5' overhangs, this being the Shifter, and the ligation of appropriate oligonucleotide adapters. A sequence of 4-base, 5' overhangs of the adapter and a 4-base sequence of the 3' end of the primer(s) determine a subset of the genomic restriction fragments, which are amplified by PCR. The method permits the differentiation of bacterial species strains on the basis of the different DNA band patterns obtained after electrophoresis in polyacrylamide gels stained with ethidium bromide and visualized in UV light. The usefulness of the LM-PCR/Shifter method for genotyping is analyzed by a comparison with the restriction endonuclease analysis of chromosomal DNA by the pulsed-field gel electrophoresis (REA-PFGE) and PCR melting profile (PCR MP) methods for isolates of clinical origin. The clustering of the LM-PCR/Shifter fingerprinting data matched those of the REA-PFGE and PCR MP methods. We found that the LM-PCR/Shifter is rapid, and offers good discriminatory power and excellent reproducibility, making it a method that may be effectively applied in epidemiological studies.

• Journal of Veterinary Science
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• 제22권6호
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• pp.67.1-67.7
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• 2021

Background: Chlamydophila felis, formerly known as Chlamydia psittaci var. felis, is frequently associated with ocular, respiratory, and occasionally reproduction tract infections. Even though the infection is sometimes asymptomatic, it potentially results in a latent immunosuppressive infection. Objective: This study aimed to identify occurrences of feline chlamydophilosis, rarely reported in cats in Indonesia. Methods: The observation was conducted in three cats with clinical signs of Cp. felis infection, particularly relapsing conjunctivitis. The cats' histories were recorded based on owners' information. Conjunctival swabs were sampled for cytology examination and molecular assay detection. A phylogenetic tree was generated using MEGA-X software to reveal group clustering. A post-mortem examination was performed on the cat that died during an examination. Results: Cp. felis was detected in both cytological examination and polymerase chain reaction assay. The phylogenetic tree demonstrated that the Cp. felis isolated in this study clustered with several other isolates from the other countries. Cp. felis can be isolated from cats with different clinical manifestations and levels of severity. The chronic fatal infection demonstrated interstitial broncho-pneumonia under histopathological examination. Conclusions: Molecular assay of Cp. felis is always recommended to obtain a definitive diagnosis of feline chlamydophilosis since the disease can have various clinical manifestations. Even though it may be subclinical and is often not fatal, an infected cat may be a carrier that could spread the pathogen in the surrounding environment. Serious disease management is suggested to avoid high costs associated with regularly relapsing disease.

• Asian-Australasian Journal of Animal Sciences
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• 제32권4호
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• pp.467-476
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• 2019

Objective: This research was conducted to study the genetic diversity in several Indonesian cattle breeds using microsatellite markers to classify the Indonesian cattle breeds. Methods: A total of 229 DNA samples from of 10 cattle breeds were used in this study. The polymerase chain reaction process was conducted using 12 labeled primers. The size of allele was generated using the multiplex DNA fragment analysis. The POPGEN and CERVUS programs were used to obtain the observed number of alleles, effective number of alleles, observed heterozygosity value, expected heterozygosity value, allele frequency, genetic differentiation, the global heterozygote deficit among breeds, and the heterozygote deficit within the breed, gene flow, Hardy-Weinberg equilibrium, and polymorphism information content values. The MEGA program was used to generate a dendrogram that illustrates the relationship among cattle population. Bayesian clustering assignments were analyzed using STRUCTURE program. The GENETIX program was used to perform the correspondence factorial analysis (CFA). The GENALEX program was used to perform the principal coordinates analysis (PCoA) and analysis of molecular variance. The principal component analysis (PCA) was performed using adegenet package of R program. Results: A total of 862 alleles were detected in this study. The INRA23 allele 205 is a specific allele candidate for the Sumba Ongole cattle, while the allele 219 is a specific allele candidate for Ongole Grade. This study revealed a very close genetic relationship between the Ongole Grade and Sumba Ongole cattle and between the Madura and Pasundan cattle. The results from the CFA, PCoA, and PCA analysis in this study provide scientific evidence regarding the genetic relationship between Banteng and Bali cattle. According to the genetic relationship, the Pesisir cattle were classified as Bos indicus cattle. Conclusion: All identified alleles in this study were able to classify the cattle population into three clusters i.e. Bos taurus cluster (Simmental Purebred, Simmental Crossbred, and Holstein Friesian cattle); Bos indicus cluster (Sumba Ongole, Ongole Grade, Madura, Pasundan, and Pesisir cattle); and Bos javanicus cluster (Banteng and Bali cattle).