• 한국조경학회지
• /
• 제28권1호
• /
• pp.129-135
• /
• 2000

In 1997, Yongsan-gu Office of Seoul held a design competition for 'Hyochang Park as a holy Grounds'. Although various shrines and monuments were located in it, Hyochang Park had lost its sense of pace as a 'holy grounds' mostly by its neighborhood-park-like atmosphere at its entrance area. Specific requirement for this competition was designing a 'symbolic object' to make this park more 'holy grounds' looking. However, it was very regretable that Yongsan-gu Office did emphasized on the 'object' rather than on the space or place in this design competition. Three points were emphasized in the winning scheme proposed by the author: where the object be located in the park, how the object be connected with the rest of the park, and how the object harmonized with some of traditional looking architectures and shrines. The purpose of this paper is to articulate the concept of the winning entry in detail and to describe how the concept actualized in reality.

• 전자통신동향분석
• /
• 제33권4호
• /
• pp.23-32
• /
• 2018

Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 하계종합학술대회 논문집(2)
• /
• pp.289-292
• /
• 2002

In this paper, an efficient architecture for the finite field multiplier is proposed. This architecture is faster and smaller than any other LFSR architectures. The traditional LFSR architecture needs t x m registers for achieving the t times speed. But, we designed He multiplier using a novel fast architecture without increasing the number of registers. The proposed multiplier is verified with a VHDL description using SYNOPSYS simulator. The measured results show that the proposed multiplier is 2 times faster than the serial LFSR multiplier. The proposed multiplier is expected to become even more advantageous in the smart card cryptography processors.

• 대한기계학회논문집A
• /
• 제33권8호
• /
• pp.753-759
• /
• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권9호
• /
• pp.4641-4657
• /
• 2017

Named Data Networking (NDN) has emerged and become one of the most promising architectures for future Internet. However, like traditional IP-based networking paradigm, NDN may not evade some typical network threats such as malicious data aggregates (MDA), which may lead to bandwidth exhaustion, traffic congestion and router overload. This paper firstly analyzes the damage effect of MDA using realistic simulations in large-scale network topology, showing that it is not just theoretical, and then designs a fine-grained MDA mitigation mechanism (MDAM) based on the cooperation between routers via alert messages. Simulations results show that MDAM can significantly reduce the Pending Interest Table overload in involved routers, and bring in normal data-returning rate and data-retrieval delay.

• 인터넷정보학회논문지
• /
• 제18권6호
• /
• pp.47-53
• /
• 2017

Recently, huge datasets have been generating rapidly in a variety of fields. Then, there is an urgent need for technologies that will allow efficient and effective processing of huge datasets. Therefore the problems of partitioning a huge dataset effectively and alleviating the processing overhead of the partitioned data efficiently have been a critical factor for scalability and performance in distributed database system. In our work we utilized multicore servers to provide scalable service to our distributed system. The partitioning of database over multicore servers have emerged from a need for new architectural design of distributed database system from scalability and performance concerns in today's data deluge. The system allows uniform access through a web service interface to concurrently distributed databases over multicore servers, using SQMD (Single Query Multiple Database) mechanism based on publish/subscribe paradigm. We will present performance results with the distributed database system built on multicore server, which is time intensive with traditional architectures. We will also discuss future works.

• Journal of Information Technology Applications and Management
• /
• 제21권2호
• /
• pp.1-14
• /
• 2014

For years, businesses have realized that story can persuade customers, familiarize people with the brand or the product, inspire team members and also mean big economic value. Storytelling, however, is regarded as such area depending only on the story director or writer's creativity and thus minimum efforts have been given to information technology development for supporting business storytelling process. This study aims to review existing information technologies for both traditional and relatively new storytelling genres, and regards their implications when applied to business storytelling. As the result of the qualitative study, three kinds of business storytelling supporting system architectures-brainstorming, writing manin story, participatory storytelling-are suggested and discussions on further works are briefly mentioned.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.29.1-29
• /
• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• Journal of Information Processing Systems
• /
• 제17권6호
• /
• pp.1157-1169
• /
• 2021

Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

• ETRI Journal
• /
• 제44권2호
• /
• pp.313-326
• /
• 2022

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost-benefit analysis, we propose a novel approach to overcome the performance constraints of agent-based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.