• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.

• Annual Conference of KIPS
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• 2008.11a
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• pp.175-178
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• 2008

MPEG-4 Part 2 에서 표준으로 채택된 FGS (fine granularity scalability) 코딩 기법은 전송 선로의 상태가 급변하는 경우에도 주어진 대역폭에서 최적의 화질 향상을 얻을 수 있도록 설계된 망 상태에 적응적인 스케일러블 비디오 코딩 기법이다 [1][2][9]. 본 논문에서는 기존의 FGS 향상 계층에서 영상의 잔여 신호를 다시 한 번 bit-plane 코딩을 해줌으로써 화질 확장성을 더 높인 Advanced FGS 코딩 구조를 제안하였다. 본 논문의 실험에서는 기존의 MPEG-4 VM (verification model)에서 사용된 FGS 코딩과의 비교를 통해 Advanced FGS 구조의 화질 확장성을 평가하였다. 비교는 두 부호화 기술의 PSNR 값의 분석으로 이루어졌고, 결과를 통해 Advanced FGS 구조가 고화질을 가지며, 화질 확장성이 더 높은 구조임을 알 수 있었다.

• Annual Conference of KIPS
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• 2024.10a
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• pp.216-219
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• 2024

Binary Code Similarity Detection (BCSD) plays a critical role in software security applications like vulnerability detection and malware analysis. This review surveys both traditional and machine-learning-based approaches to BCSD. Traditional methods, such as control flow graph matching and symbolic execution, have demonstrated effectiveness but suffer from scalability issues, particularly with obfuscated code. Modern machine learning techniques, including graph neural networks and deep learning models, offer improved adaptability across architectures and scalability. Despite these advancements, challenges remain in cross-platform detection, handling obfuscation, and deploying BCSD tools in real-world security scenarios. The review highlights recent innovations and outlines potential future directions for enhancing the robustness and efficiency of BCSD systems.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.460-465
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• 2024

MongoDB, a document-based database, is suitable for distributed management environments of large-scale databases due to its high scalability, performance, and flexibility. Recently, as MongoDB has been widely used as a new database, many studies have been conducted including data modeling for MongoDB and studies on applying MongoDB to various applications. In this paper, we propose a data modeling method for implementing Seoul public transportation data with MongoDB. Seoul public transportation data is public data provided by the Korea Public Data Portal. In this study, we analyze the target data and find design patterns such as polymorphic pattern, subset pattern, computed pattern, and extended reference pattern in the data. Then, we present data modeling with these patterns. We also show examples of implementation of Seoul public transportation database in MongoDB. The proposed modeling method can improve database performance by leveraging the flexibility and scalability that are characteristics of MongoDB.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.205-206
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• 2014

• Proceedings of the Korea Database Society Conference
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• 2001.11a
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• pp.323-338
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• 2001

o 사용자의 인터페이스(User Interface)고려 - 다운로드 없는 채팅 - 커넥션 오리엔티드 커뮤니케이션 도구 제공 - 기술력을 바탕으로 한 서비스 - 막대한 Scalability를 견뎌낼 수 있는 기술/운영적인 도전 (중략)

• Proceedings of the Korean Nuclear Society Conference
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• 2005.10a
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• pp.448-449
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• 2005

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.577-582
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• 2011

EMR(Electronic Medical Record) is an emerging technology that is highly-blended between non-IT and IT area. One of methodology to link non-IT and IT area is to construct databases. Nowadays, it supports before and after-treatment for patients and should satisfy all stakeholders such as practitioners, nurses, researchers, administrators and financial department and so on. In accordance with the database maintenance, DAS (Data as Service) model is one solution for outsourcing. However, there are some scalability and strategy issues when we need to plan to use DAS model properly. We constructed three kinds of databases such as plain-text, MS built-in encryption which is in-house model and custom AES (Advanced Encryption Standard) - DAS model scaling from 5K to 2560K records. To perform custom AES-DAS better, we also devised Bucket Index using Bloom Filter. The simulation showed the response times arithmetically increased in the beginning but after a certain threshold, exponentially increased in the end. In conclusion, if the database model is close to in-house model, then vendor technology is a good way to perform and get query response times in a consistent manner. If the model is DAS model, it is easy to outsource the database, however, some technique like Bucket Index enhances its utilization. To get faster query response times, designing database such as consideration of the field type is also important. This study suggests cloud computing would be a next DAS model to satisfy the scalability and the security issues.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.171-178
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• 2007

In mobile ad hoc networks, an application scenario requires mostly group mobility behavior in the mix of group moving nodes and individually moving nodes. The nodes of those applications tend to belong to the movement group with similar movement behavior. Group mobility is one of the good methods to improve scalability, and reduces the protocol overhead. In this paper, we propose the multicast architecture which regards nodes that have equal group mobility in the heterogeneous group mobility network as the single entity with the multiple interfaces and composes multicast tree, The logical cooperative entity-based multicast architecture accommodates the scalability, the multicast tree simplification, and the protocol overhead reduction which arc obtained from the hierarchical multicast architecture, while it maintains the nat multicast architecture for the data transmission. It also prevents the concentration of the energy consumption dispersing data forwarding load into the several ingress/egress nodes. Results obtained through simulations show that logical cooperative entity based multicast protocol with multiple interfaces offers the protocol scalability and the efficient data transmission.