• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.320-326
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• 2008

In this paper, we propose a new algorithm emulating the DNA characteristics for noise-tolerant pattern matching problem on digital system. The digital pattern matching becomes core technology in various fields, such as, robot vision, remote sensing, character recognition, and medical diagnosis in particular. As the properties of natural DNA strands allow hybridization with a certain portion of incompatible base pairs, DNA-inspired data structure and computation technique can be adopted to bio-signal pattern classification problems which often contain imprecise data patterns. The key feature of noise-tolerance of DNA computing comes from control of reaction temperature. Our hardware system mimics such property to diagnose cardiovascular disease and results superior classification performance over existing supervised learning pattern matching algorithms. The hardware design employing parallel architecture is also very efficient in time and area.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.29-43
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• 2014

The development of NGS technologies, such as scientific workflows, has reduced the time required for decoding DNA sequences. Although the automated technologies change the genome sequence analysis environment, limited computing resources still pose problems for the analysis. Most scientific workflow systems are pre-built platforms and are highly complex because a lot of the functions are implemented into one system platform. It is also difficult to apply components of pre-built systems to a new system in the cloud environment. Cloud computing technologies can be applied to the systems to reduce analysis time and enable simultaneous analysis of massive DNA sequence data. Web service techniques are also introduced for improving the interoperability between DNA sequence analysis systems. The workflow-based middleware, which supports Web services, DBMS, and cloud computing, is proposed in this paper for expecting to reduceanalysis time and aiding lightweight virtual instances. It uses DBMS for managing the pipeline status and supporting the creation of lightweight virtual instances in the cloud environment. Also, the RESTful Web services with simple URI and XML contents are applied for improving the interoperability. The performance test of the system needs to be conducted by comparing results other developed DNA analysis services at the stabilization stage.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.730-735
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• 2005

Though Knapsack Problem appears to be simple, it is a NP-hard problem that is not solved in polynomial time as combinational optimization problems. To solve this problem, GA(Genetic Algorithms) was used in the past. However, there were difficulties in real experiments because the conventional method didn't reflect the precise characteristics of DNA. In this paper we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to solve problems of Knapsack Problem. ACO was applied to (0,1) Knapsack Problem; as a result, it reduced experimental errors as compared with conventional methods, and found accurate solutions more rapidly.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.539-542
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• 2004

배낭 문제는 조합 최적화 문제로서, 다항 시간(polynomial time)에 풀리지 않는 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 DNA 컴퓨팅 기법과 GA 등을 사용하여 해결하였다. 하지만 기존의 방법들은 DNA의 정확한 특성을 고려하지 않아, 실제 실험과의 결과 차이가 발생하고 있다. 본 논문에서는 DNA 컴퓨팅 실험 과정에서 발생하는 DNA 조작 오류를 최소화하고, 보다 정확한 예측을 위해 함수 언어인 Haskell을 이용한 코드 최적화 DNA-Haskell을 제안한다. 코드 최적화 DNA-Haskell은 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 DNA 컴퓨팅 방법보다 실험적 오류를 최소화하였으며, 또한 적합한 해를 빠른 시간 내에 찾을 수 있었다.

• Review of Korea Contents Association
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• v.15 no.4
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• pp.18-21
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• 2017

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.476-478
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• 2003

1-Base의 non Watson-Crick 결합과, Dangling end(결합이 이루어진 두 개의 DNA strand 중 한쪽 끝이 다른 쪽 끝보다 짧은 경우)를 허용하는 nearest-neighbor model을 사용하여 DNA/DNA Hybridization 예측 시스템을 구현하였다. DNA 컴퓨팅을 기존의 실리콘 컴퓨터를 이용하여 접근하는 이러한 방법은 좀 더 효율적인 분자 알고리즘의 개발과 DNA 컴퓨팅에 사용될 수 있는 더욱 신뢰성 있는 DNA 시퀀스의 설계에 도움을 줄 수 있을 것이다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.73-75
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• 2001

최근 DNA 분자의 병렬성을 이용한 DNA 컴퓨팅 기법들이 활발히 개발되고 있다. 그러나, DNA 컴퓨팅은 실제 생체 분자인 DNA를 사용하기 때문에 생체분자의 화학적 성질에 의한 오류의 가능성을 항상 내포하고 있다. 이러한 문제를 극복하고자 오류의 가능성을 최소화시키는 방법들이 연구되고 있고, 특히 DNA 서열을 만들 때 오류의 가능성을 최소화시키는 방법들이 많이 연구되고 있다. 본 논문에는 현재 개발하고 있는 시스템인 NACST를 간단히 소개한 후, DNA 컴퓨팅에 사용할 DNA 서열을 생성하기 위해서 유전자 알고리즘을 사용하는 방법을 제안하며, 유전자 알고리즘을 이용하여 DNA 서열을 효율적으로 생성하기 위한 적합도 함수들에 대해서 구체적으로 살펴보았다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.838-840
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• 2003

특정 단백질과 특이적으로 결합하는 핵산인 앱타머 (aptamer) 의 존재는, DNA 기반 컴퓨팅과 단백질 기반 컴퓨팅 사이에서 가교 역할을 할 수 있다는 가능성을 고려할 때 주목할 만하다. 본고에서는 전통적인 DNA 기반 컴퓨팅 방법론의 확장으로서, 앱타머 구조 변환의 활용 방안을 제안하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.243-246
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• 2004

배낭 문제는 단순한 것 같지만 조합형 특성을 가진 NP-hard 문제이다 이 문제를 해결하기 위해 기존에는 GA(Genetic algorithms)를 이용하였으나 지역해에 빠질 수 있어 잘못된 해를 찾거나 찾지 못하는 문제점을 갖고 있다. 본 논문에서는 이러한 문제점들을 해결하기 위해 막대한 병렬성과 저장능력을 가진 DNA 컴퓨팅 기법에 DNA에 기반한 변형된 GA인 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optmization)를 제안한다. ACO는 배낭 문제 중 (0,1)-배낭 문제에 적용하였고, 그 결과 기존의 GA를 이용한 것 보다 초기 문제 표현에서 우수한 적합도를 생성했으며, 빠른 시간내에 우수한 해를 찾을 수 있었다.