• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.5-6
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• 2023

본 논문에서는 MIPS 아키텍쳐 기반 펌웨어에 대한 페이지 단위의 이미지 베이스 주소 탐색 방안을 제안한다. 이 방법은 MIPS 기반 임베디드 기기의 펌웨어를 대상으로, 대상 내의 분석 대상의 이미지 베이스 주소 계산 알고리즘을 효율적으로 개선하여 이미지 베이스 주소탐색 시간을 최소화하는 것을 목표로 한다. 이 방법은 펌웨어 내 문자열의 주소를 기준으로 세그먼트 시작 주소를 유추, 페이지 단위인 4KB 단위로의 이미지 베이스 주소 후보군을 계산하여 이미지 베이스 주소 후보군을 선별하는 것을 그 원리로 한다. 본 논문에 적용된 방법은 기존의 경험적 방법을 통한 펌웨어 베이스 탐색 방안에 비해 정확도면에서 우수함을 보인다.

• BMB Reports
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• v.57 no.1
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• pp.19-29
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• 2024

Mitochondrial DNA (mtDNA), a multicopy genome found in mitochondria, is crucial for oxidative phosphorylation. Mutations in mtDNA can lead to severe mitochondrial dysfunction in tissues and organs with high energy demand. MtDNA mutations are closely associated with mitochondrial and age-related disease. To better understand the functional role of mtDNA and work toward developing therapeutics, it is essential to advance technology that is capable of manipulating the mitochondrial genome. This review discusses ongoing efforts in mitochondrial genome editing with mtDNA nucleases and base editors, including the tools, delivery strategies, and applications. Future advances in mitochondrial genome editing to address challenges regarding their efficiency and specificity can achieve the promise of therapeutic genome editing.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.188-193
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• 2000

A more accurate life prediction for gas turbine blade takes into account the material behavior under the complex thermo-mechanical fatigue(TMF) cycles normally encountered in turbine operation. An experimental program has been carried out to address the thermo-mechanical fatigue life of the IN738LC nickel-base superalloy. In the first phase of the study, out-of-phase and in-phase TMF experiments have been performed on uncoated and coated materials. In the temperature range investigated. the deposition of NiCrAlY air plasma sprayed coating did not affect the fatigue resistance. In the second phase of the study, a physically-base life prediction model that takes into account of the contribution of different damage mechanisms has been applied. This model was able to reflect the temperature and strain rate dependences of isothermal cycling fatigue lives, and the strain-temperature history effect on the thermo-mechanical fatigue lives.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Asian Journal of Innovation and Policy
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• v.11 no.1
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• pp.87-109
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• 2022

The study attempts to examine the system-building activities of scientific institutions in developing the Molecular Diagnostic (MDs) Innovation System in India. Scientific Institutions are the precursor of any technological development with their capabilities in generating new ideas. MDs are advanced and accurate diagnostic technology with considerable scope to serve the diagnostic needs and requirements of the healthcare system. We adopted a System framework and analyzed the development of MDs in terms of the Technological Innovation System (TIS) functions, and the systematic challenges are assessed through the System Failure Framework (SFF). Based on the secondary and primary survey of prominent science base actors, the study finds that the role of government is crucial for facilitating technological development within a science base through the mobilization of resources. In India, the MDs technological development gained significant momentum over the last decade with the development of specialized human resources and dedicated research institutes. However, we do find that the innovative capabilities in attaining need-based TIS are sub-optimal owning to the specific diagnostic needs of highly burdened diseases in the society. The system analysis reveals that the TIS functions are underperforming because of the absence of a well-defined funding mechanism and goal-oriented targeted policy regime of the government. Since MDs have a transformative effect on the present healthcare system, we argue that the government has to address the system-based challenges and issues for developing a need-based technological innovation system for MDs in the country.

• Korean Journal of Organic Agriculture
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• v.21 no.4
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• pp.523-538
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• 2013

To help improve the current government practice of direct damage-compensation policies, resulting from the loss of profit, sustained by Hanwoo farmers, as a result of the recent Korea-U. S. Free Trade Agreement (FTA), this research aims to examine any problems or issues caused by said policies. To accomplish this task, we have established Hanwoo-SIMO model and estimated the damage of Hanwoo farmers, one without the implementation of the FTA and another with the FTA, to compare and contrast the two. We then analyzed the efficacy of the current government policies. According to our analysis, the current direct compensation policies for the loss of profit on the part of Hanwoo farmers are insufficient. To address this problem, we recommend the government enact a new direct damagecompensation law to address the following issues. First, as the base formula of damage-compensation, the government should use current price of the beef rather than the annually changing flexible price. Second, the flexible control index should remain fixed at 1.0 rate while the government prepares the adequate amount of the damage compensating direct payment resulting from the FTA. Third, the direct government compensation policy should extend beyond the current 15 years (2013-2026) as the profit loss is expected to increase after the midpoint of the FTA.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.275-284
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• 2008

The digital map needs efficiently updating. Because it is a base map at each local government and several geographic information systems and that is the key to enhancing to use spatial data. We suggest the linking method of building registers to the building layers of digital map, to update attribute data of the building layers. To conduct that, it is very important that each building in two data is linked by one-to-one matching. In this paper, we generate the strategy for renewing attribute data of the building layers based on identifier by using identifier of the new address system.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.99-106
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• 2019

Technology that cannot be excluded from 4th industry is self-driving sector. The self-driving sector can be seen as a key set of technologies in the fourth industry, especially in the DB sector is getting more and more popular as a business. The DB, which was previously produced and managed in two dimensions, is now evolving into three dimensions. Among the data obtained by Mobile Mapping System () to produce the HD MAP necessary for self-driving, Point Cloud, which is LiDAR data, is used as a DB because it contains accurate location information. However, at present, it is not widely used as a base data for 3D modeling in addition to HD MAP production. In this study, MMS Point Cloud was used to extract facilities around the road and to overlay the location to expand the usability of Point Cloud. Building utility poles and communication poles DB from Point Cloud and comparing road name address base and location, it is believed that the accuracy of the location of the facility DB extracted from Point Cloud is also higher than the basic road name address of the road, It is necessary to study the expansion of the facility field sufficiently.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.549-557
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• 2015

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: Velocity, voracity, volume, and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platformand the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4Gbyte of storage size (87%of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.