• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.211-226
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• 2017

The chain-ladder method, for which run-off data is employed is popularly used in the rate-adjustment and loss-reserving practices of non-life-insurance and health-insurance companies. The method is applicable when the underlying assumption of a consistent development pattern is in regards to a cumulative loss payment after the occurrence of an insurance event. In this study, a modified chain-ladder algorithm is proposed for when the assumption is considered to be only partially appropriate for the given run-off data. The concept of a structural-change point in the run-off data and its reflection in the estimation of unpaid loss amounts are discussed with numerical illustrations. Experience data from private health insurance coverage in Korea were analyzed based on the suggested method. The performance in estimation of loss reserve was also compared with traditional approaches. We present evidence in this paper that shows that a reflection of a structural-change point in the chain-ladder method can improve the risk management of the relevant insurance products. The suggested method is expected to be utilized easily in actuarial practice as the algorithm is straightforward.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.277-278
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• 2023

Smart contract는 Off-chain 데이터를 On-chain에서 조회해보지 못하는 "Blockchain Oracle Problem"을 갖고 있다. Oracle Problem을 적용할 때 실제 사용자 측면에서 어떠한 비용이 발생하는지 연구가 부족하다고 느껴, Chainlink 오라클 아키텍처 기반 시스템의 On-chain 비용을 측정할 수 있는 식을 제안한다. 그 결과 각 아키텍처마다 gas fee, oracle fee, latency의 차이를 확인한다.

• Journal of Animal Science and Technology
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• v.50 no.1
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• pp.111-120
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• 2008

The average milk fat content in goat milk was 3.88% on yearly basis. The milk fat content of 3.8% during summer season was lower than 4.2% during winter season. Total solid content increased in proportion to milk fat. When goat milk was stored at 4℃ for 24 hr, short-chain FFA(C4:0~C10:0) and medium- and long-chain FFA(C12:0~C18:1) increased about 106% and 203%, respectively. Induced lipolysis of goat milk by homogenization increased short-chain FFA and medium- and long-chain FFA by 22% and 199%. When goat milk was treated with calf lipase, there was increase of short-chain FFA by 9 times greater than increase of medium- and long-chain FFA by 5.6 times. Treatment with lipases from Candida rugosa and Pseudomonas fluorescens resulted in increase of medium- and long-chain FFA by 34 and 162 times, respectively, which was greater than increase of short-chain FFA by 6 and 14 times, respectively. Lipolysis in goat milk stored at 4℃ for 24 hr was correlated with LPL activity in goat milk(r=0.5635). Off-flavor of goat milk was correlated with LPL activity(r=0.5777). Milk fat content was negatively correlated with LPL activity(r=－0.4627). Palmitic acid content in goat milk was correlated with off-flavor(r=0.7226).

• Macromolecular Research
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• v.10 no.6
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• pp.297-303
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• 2002

We have investigated the microstructural properties of a weakly charged polyelectrolyte modeled with both Hookean spring and Debye-Huckel potential, by employing a novel hybrid scheme of molecular dynamics (MD) and Monte Carlo (MC) simulations. Although the off-lattice pivot step facilitates the earlier computations stage, it gives rise to oscillations and hinders the stable equilibrium state. In order to overcome this problem, we adopt the MC off-lattice pivot step in early stage only, and then switch the computation to a pure MD step. The result shows that the computational speed-up compared to the previous method is entirely above 10 to 50, without loss of the accuracy. We examined the conformations of polyelectrolyte in solvents in terms of the end-to-end distance, radius of gyration, and structure factor with variations of the screening effects of solvent and the monomer charges. The emphasis can favorably be given on the elongation behavior of a polyelectrolyte chain, with observing the simultaneous snapshots.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.551-562
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• 2008

The loss reserve is defined as a provision for an insurer's liability for claims or an insurer's estimate of the amount an individual claim will ultimately cost. For the estimation of the loss reserve, the data which make up the claims in general is represented as run-off triangle. The chain ladder method has known as the most representative one in the estimation of loss reserves based on such run-off triangular data. However, this fails to capture change point in trend. In order to test of structural changes of development factors, we will present the test statistics and procedures. A real data analysis will also be provided.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.33-41
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• 2018

Most important thing when designing and implementing a supply chain network is to consider various problems which may occur in real world situation. In this paper, we propose a supply chain network considering two problems (environmental factor and transportation types) under real world situation. CO2 emission amount as environmental factor is considered since it is usually generated from production and transportation processes. Normal delivery, direct delivery and direct shipment as transportation types are also considered since many customers ask various transportation types for delivery or shipment of their products under on-line or off-line purchase environment. The proposed supply chain network considering environmental factor and transportation types is represented in a mathematical formulation and implemented using hybrid genetic algorithm (HGA) approach. In numerical experiments, several scales of supply chain networks are presented and implemented using HGA approach. The performance of the HGA approach is compared with those of some conventional approaches under various measures of performance. Finally, it is proved that the performance of the HGA approach is superior to those of the others.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.715-729
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• 2021

Bitcoin is one of the cryptocurrencies, which is decentralized and transparent. However, due to its anonymity, it is currently being used for the purpose of transferring funds for illegal transactions in darknet markets. To solve this problem, clustering heuristic based on the characteristics of a Bitcoin transaction has been proposed. However, we found that the previous heuristis suffer from high false negative rates. In this study, we propose a novel heuristic for bitcoin clustering using off-chain data. Specifically, we collected and analyzed user review data from Silk Road 4 as off-chain data. As a result, 31.68% of the review data matched the actual Bitcoin transaction, and false negatives were reduced by 91.7% in the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1676-1686
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• 2021

In this paper, we propose a general cross-chain solution based on the idea of modularity, abstraction, and layering, which decoupling the cross-chain function from the consensus algorithm and specific application logic, and utilize a Merkle proof to ensure the validity and legality of cross-chain operations. Since the underlying implementations of homogeneous and heterogeneous blockchains are different, we treat them separately. For homogeneous blockchains, we suggest a TCP-like cross-chain transport protocol (CCTP). While for heterogeneous blockchains, we present a method to construct the relay chain to realize the cross-chain function. The proposed scheme can enable the correct, effective, reliable, orderly, and timely transmission of cross-chain data. However, the essential difference between the operations within a single blockchain and the interoperability between different blockchains is that the trust domain is different. Cross-chain interoperation itself breaks the completeness of the blockchain, therefore, some efficiency and safety must sacrifice to trade-off.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.37-50
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• 2022

Modern supply chains include multiple activities from collecting raw materials to transferring final products. These activities involve many parties who share a huge amount of valuable data, which makes managing supply chain systems a challenging task. Current supply chain management (SCM) systems adopt digital technologies such as the Internet of Things (IoT) and blockchain for optimization purposes. Although these technologies can significantly enhance SCM systems, they have their own limitations that directly affect SCM systems. Security, performance, and scalability are essential components of SCM systems. Yet, confidentiality and scalability are one of blockchain's main limitations. Moreover, IoT devices are lightweight and have limited power and storage. These limitations should be considered when developing blockchain-based IoT-SCM systems. In this paper, the requirements of efficient supply chain systems are analyzed and the role of both IoT and blockchain technologies in providing each requirement are discussed. The limitations of blockchain and the challenges of IoT integration are investigated. The limitations of current literature in the same field are identified, and a secure and scalable blockchain-based IoT-SCM system is proposed. The proposed solution employs a Hyperledger fabric blockchain platform and tackles confidentiality by implementing private data collection to achieve confidentiality without decreasing performance. Moreover, the proposed framework integrates IoT data to stream live data without consuming its limited resources and implements a dualstorge model to support supply chain scalability. The proposed framework is evaluated in terms of security, throughput, and latency. The results demonstrate that the proposed framework maintains confidentiality, integrity, and availability of on-chain and off-chain supply chain data. It achieved better performance through 31.2% and 18% increases in read operation throughput and write operation throughput, respectively. Furthermore, it decreased the write operation latency by 83.3%.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.54-60
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• 2020

Online cybercrime has various causes. The criminal guilty language, Criminal lingo is active in the shaded area with the bilateral aspect of the word on cyber. It has been continuously producing massive risk factors in cyberspace. Criminals are shared and disseminated online. It has been linked with fake news and aids to suicide that has recently become an issue. Thus the criminal lingo has become a real danger factor on cyber interface. Recently, Criminal lingo is shared and distributed as cyber hazard information. It is transformed that damaging to the youth and ordinary people through the internet and social networks. In order to take action, it is necessary to construct an expert system based on AI to implement a smart management architecture with block-chain technology. In this paper, we study technically a new smart management architecture which uses artificial intelligence based decision algorithm and block-chain tracking technology to prevent the spread of criminal lingo factors in the evolving cyber world. In addition, through the off-line regular patrol program of police units, we proposed the conversion of online regular patrol program for "cyber harem area".