• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.441-451
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• 2022

In the era of bigdata, scalability is a crucial issue in learning models. Among many others, the Alternating Direction of Multipliers (ADMM, Boyd et al., 2011) algorithm has gained great popularity in solving large-scale problems efficiently. In this article, we propose applying the ADMM algorithm to solve the least square problem penalized by the pairwise-difference penalty, frequently used to identify group structures among coefficients. ADMM algorithm enables us to solve the high-dimensional problem efficiently in a unified fashion and thus allows us to employ several different types of penalty functions such as LASSO, Elastic Net, SCAD, and MCP for the penalized problem. Additionally, the ADMM algorithm naturally extends the algorithm to distributed computation and real-time updates, both desirable when dealing with large amounts of data.

• Journal of Information Technology Applications and Management
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• v.29 no.4
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• pp.17-33
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• 2022

This study measures the internal and external competitiveness of 35 OECD countries in the insurance industry. We analyze whether variables related to the Fourth Industrial Revolution affect international competitiveness by applying a nonlinear autoregressive distributed lag model. As a result, the competitiveness of life insurance foreign companies in internal is showing positive responses in high income inequality countries. In addition, insurance companies in countries with low income inequality have shown high performance in external. The non-life insurance industry is less sensitive to shocks than life insurance. This is because non-life insurance is a more dangerous industry than life insurance and there are many restrictions on policies and regulations. The reason is that non-life insurance is a more dangerous industry than life insurance and there are many restrictions on policies and regulations.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.1-8
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• 2023

This paper presents a design method for a wideband bandpass filter (BPF) which compensates for frequency dependency based on the image admittance and image phase. In the proposed method, new compensation methods for the admittance and phase are integrated with the conventional method. The proposed method improves the frequency shift and reduces the unwanted bandwidth when designing more than 20% of the Fractional Bandwidth (FBW), whereas the conventional method exhibits frequency degradation at only 10% FBW. The proposed design theory was verified by applying it to both lumped elements and distributed lines through circuit simulation and measurements without an optimization process. The measurement results demonstrate improvements in the frequency shift and target bandwidth. In the future, an accurate design method based on frequency dependence can be implemented for the next-generation broadband communication system applications.

• Annual Conference of KIPS
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• 2010.11a
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• pp.256-259
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• 2010

본 논문에서는 오픈 아키텍처 기반의 이기종 분산 시스템에서 애플리케이션 관리 및 모니터링 시스템을 구현한다. 전투관리 시스템이나 공정관리 시스템 등과 같은 통합관리 시스템은 다양한 이기종 분산 시스템으로 구성되며 이러한 분산 환경에서 다양한 애플리케이션들이 동작하게 되는데 이들을 통제 감시하는 방법이 필요하다. 본 논문에서는 추상적인 정보모델을 이용하여 이기종 시스템들을 표현하고 이를 통해 상호의존성을 관리함으로써 다양한 애플리케이션을 효과적으로 관리하고 모니터링 할 수 있는 방안을 제시한다.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.281-284
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• 2023

Real-time control communication network system is important for developing defense robots because it affects environmental interaction, performance, and safety. We propose a real-time control communication network using the Xenomai real-time operating system and the open-source EtherCAT master library, SOEM. EtherCAT is an Ethernet-based industrial communication method. It has low latency and many functions such as cable redundancy and distributed clock synchronization. We use Xenomai RTOS and Intel NUC to develop the system. Experimental tests demonstrate the Real-time EtherCAT master implementation, and communication with CiA301-based slave devices. The jitter measurement was conducted to validate the real-time performance of the system. The proposed system shows possibility for real-time robotics applications in various defense robots.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.8-24
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• 2004

OO RT programming is a technology expected to flourish in this quarter of the 21st century. Currently, its youthfulness is indicated by the insufficient availability of the support middleware and the associated API, let alone language compilers. The middleware providing fault-tolerant execution support is in its infancy. The advances in OO RT distributed programming will also enable large-scale RT simulations. The research community dealing with this technology area is expected to grow continuously for foreseeable future and consequent accelerations of the technology advances will in turn accelerate the development of many new types of sophisticated RT DC applications as well as realization of advanced types of ubiquitous computing societies.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.135-147
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• 2023

Skipped median is more robust than median when outliers are not symmetrically distributed. In this work, we propose a novel algorithm to estimate the skipped median. The idea of skipped median and the new algorithm are extended to regression problem, which is called least clipped absolute deviation (LCAD). Since our proposed algorithm for nonconvex LCAD optimization makes use of convex least absolute deviation (LAD) procedure as a subroutine, regularizations developed for LAD can be directly applied, without modification, to LCAD as well. Numerical studies demonstrate that skipped median and LCAD are useful and outperform their counterparts, median and LAD, when outliers intervene asymmetrically. Some extensions of the idea for skipped median and LCAD are discussed.

• Electronics and Telecommunications Trends
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• v.37 no.6
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• pp.32-42
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• 2022

Recently, decentralized approaches to artificial intelligence (AI) development, such as federated learning are drawing attention as AI development's cost and time inefficiency increase due to explosive data growth and rapid environmental changes. Collaborative AI technology that dynamically organizes collaborative groups between different agents to share data, knowledge, and experience and uses distributed resources to derive enhanced knowledge and analysis models through collaborative learning to solve given problems is an alternative to centralized AI. This article investigates and analyzes recent technologies and applications applicable to the research of multi-agent collaboration of AI bots, which can provide collaborative AI functionality autonomously.

• Journal of Ceramic Processing Research
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• v.13 no.spc1
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• pp.110-113
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• 2012

Ge-S and Li-Ge-S powders were synthesized via solution-based process in order to employ chalcogenide-based solid electrolyte for use in Li secondary batteries. GeCl4 and thioacetamide in combination result in Ge-S powders of which major crystalline phase becomes GeS2 where the tetragonal and orthorhombic phases coexist after heat treatment. A chemical treatment using NaOH brings about the reduction of chlorine in the powders obtained. However, the heat treatment at 300 ℃ is more effective in minimizing the chlorine content. When lithium chloride is used as the precursor of Li ions, the LiCl powders are agglomerated with an inhomogeneous distribution. When Li2S is used, the Li-Ge-S powders are distributed more uniformly and the orthorhombic GeS2 phase dominates in the powders.

• Annual Conference of KIPS
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• 2021.11a
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• pp.128-130
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• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.