• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.67-68
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• 2016

최근 빅 데이터를 이용한 시스템들이 여러 분야에서 활발히 이용되기 시작하면서 대표적인 빅데이터 저장 및 처리 플랫폼인 하둡(Hadoop)의 기술적 단점을 보완할 수 있는 다양한 분산 시스템 플랫폼이 등장하고 있다. 그 중 아파치 스파크(Apache Spark)는 하둡 플랫폼의 속도저하 단점을 보완하기 위해 인 메모리 처리를 지원하여 대용량 데이터를 효율적으로 처리하는 오픈 소스 분산 데이터 처리 플랫폼이다. 하지만, 아파치 스파크의 작업은 메모리에 의존적이므로 제한된 메모리 환경에서 전체 작업 성능은 급격히 낮아진다. 본 논문에서는 메모리 용량에 따른 아파치 스파크 성능 비교를 통해 아파치 스파크 동작을 위해 필요한 적정 메모리 용량을 확인한다.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.41-44
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• 1987

Recently Hopfield proposed a model for content-addressable memory, which has been shown to be capable of storing information in a distributed fashion and determining the nearest-neighbor. Its application is, however, inherently limited to the case that the number of l's in each stored vector is nearly the same as the number of O's in that vector. If not the case, the model has high probability of failure in finding the nearest-neighbor. In this work, a modification of the Hopfield's model, which works well irrespective of the number of l's (or O's) in each stored vector, is suggested.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.11
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• pp.1254-1264
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• 2008

This paper suggests an extended memory polynomial model that improves accuracy in modeling memory effects of RF power amplifiers(PAs), and verifies effectiveness of the suggested method. The extended memory polynomial model includes cross-terms that are products of input terms that have different delay values to improve the limited accuracy of basic memory polynomial model that includes the diagonal terms of Volterra kernels. The complexity of the memoryless model, memory polynomial model, and the suggested model are compared. The extended memory polynomial model is represented with a matrix equation, and the Volterra kernels are extracted using least square method. In addition, the structure of digital predistorter and digital signal processing(DSP) algorithm based on the suggested model and indirect learning method are proposed to implement a digital predistortion linearization. To verify the suggested model, the predicted output of the model is compared with the measured output for a 10W GaN HEMT RF PA and 30 W LDMOS RF PA using 2.3 GHz WiBro input signal, and adjacent-channel power ratio(ACPR) performance with the proposed digital predistortion is measured. The proposed model increases model accuracy for the PAs, and improves the linearization performance by reducing ACPR.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.1003-1012
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• 2005

The most significant difference of embedded systems from general purpose systems is that embedded systems are allowed to use only limited resources including battery and memory. Especially, the number of applications increases which deal with multimedia data. In those systems with high data computations, the delay of memory access is one of the major bottlenecks hurting the system performance. As a result, many researchers have investigated various techniques to reduce the memory access cost. Most programs generally have locality in memory references. Temporal locality of references means that a resource accessed at one point will be used again in the near future. Spatial locality of references is that likelihood of using a resource gets higher if resources near it were just accessed. The latest embedded processors usually adapt cache memory to exploit these two types of localities. Processors access faster cache memory than off-chip memory, reducing the latency. In this paper we will propose the enhanced dynamic allocation technique for structure-type data in order to eliminate unused memory space and to reduce both the cache miss rate and the application execution time. The proposed approach aggregates fields from multiple records dynamically allocated and consecutively remaps them on the memory space. Experiments on Olden benchmarks show $13.9\%$ L1 cache miss rate drop and $15.9\%$ L2 cache miss drop on average, compared to the previously proposed techniques. We also find execution time reduced by $10.9\%$ on average, compared to the previous work.

• Korean Journal of Cognitive Science
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• v.16 no.1
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• pp.1-15
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• 2005

Since human visual system has limited capacity for visual information processing, it should select goal-relevant information for further processing. There have been several studies that emphasized the possible involvement of memory in spatial shift of selective attention (Chun & Jiang, 1998, 1999; Klein, 1988; Klein & MacInnes, 1999). However, other studies suggested the inferiority of human visual memory in change detection(Rensink, O'Regan, & Clark, 1997; Simons & Levin, 1997) and in visual search(Hotowitz & Wolfe, 1998). The present study examined the involvement of memory in visual search; whether memory for the previously searched items guides selective attentional shift or not. We investigated how search works by comparing visual search performances in three different conditions; full exposure condition, partial exposure condition, and partial-to-full exposure condition. Revisiting searched items was allowed only in full exposure condition and not in either partial or partial-to-full exposure condition. The results showed that the efficiencies of attentional shift were nearly identical for all conditions. This finding implies that even in full exposure condition the participants scarcely re-examined the previously searched items. The results suggest that instant memory can be formed and used in visual search process. These results disagree with the earlier studies claiming thar visual search has no memory. We discussed the problems of the previous research paradigms and suggested some alternative accounts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.149-156
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• 2012

Support vector machines (SVMs) are well known for their pattern recognition capability, but proper care should be taken to alleviate their inherent implementation cost resulting from high computational intensity and memory requirement, especially in embedded systems where only limited resources are available. Since the memory requirement determined by the dimensionality and the number of support vectors is generally too high for a cache in embedded systems to accomodate, frequent accesses to the main memory occur inevitably whenever the cache is not able to provide requested data to the processor. These frequent accesses to the main memory result in overall performance degradation and increased energy consumption because a memory access typically takes longer and consumes more energy than a cache access or a register access. In this paper, we propose a technique that reduces the number of main memory accesses by optimizing the data access pattern of the SVM-based classifier in such a way that the temporal locality of the accesses increases, fully utilizing data loaded into the processor chip. With experiments, we confirm the enhancement made by the proposed technique in terms of the number of memory accesses, overall execution time, and energy consumption.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.253-256
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• 2007

The web server for the embedded Linux system(ELS), unlike the ones for the usual Linux or Windows, has some disadvantages such as small number of installable applications, low compatibility and limited extensibility. This fact raises some problems when data are transferred in real-time via the web server, which are mainly caused by a poor performance of the processor and small-sized memory. Conventional user interfaces adopted for the usual web servers are unsuitable for the ELS because they are platform-limited and their installations are done by the form of plug-ins. If the web server for the ELS has an Ajax engine that can be utilized without any installation procedure, the advantages of usability, accessibility and quick response time can be obtained. This paper presents the Ajax interface for the ELS web server. The efficiency of the proposed technology in the real-time remote monitoring is shown through an implementation.

• Journal of the Korean Magnetic Resonance Society
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• v.24 no.2
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• pp.59-65
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• 2020

Nitrogen-Vacancy (NV) center in diamond has been an emerging versatile tool for quantum sensing applications. Amongst various applications, nano-scale nuclear magnetic resonance (NMR) using a single or ensemble NV centers has demonstrated promising results, opening possibility of a single molecule NMR for its chemical structural studies or multi-nuclear spin spectroscopy for quantum information science. However, there is a key challenge, which limited the spectral resolution of NMR detection using NV centers; the interrogation duration for NV-NMR detection technique has been limited by the NV sensor spin lifetime (T₁ ~ 3ms), which is orders of magnitude shorter than the coherence times of nuclear spins in bulk liquid samples (T₂ ~ 1s) or intrinsic ¹³C nuclear spins in diamond. Recent studies have shown that quantum memory technique or synchronized readout detection technique can further narrow down the spectral linewidth of NMR signal. In this short review paper, we overview basic concepts of nanoscale NMR using NV centers, and introduce further developments in high spectral resolution NV NMR studies.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.627-628
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• 2008

This paper proposes an indoor air Quality monitoring system for measuring various indoor air qualities using IEEE 802.15.4 based wireless sensor network. For indoor air quality monitoring, sensor nodes include carbon monoxide sensor and dust sensor were used. The wireless network by deployed sensor nodes has limited energy, computing, communication capabilities and memory sizes. In this paper, the query process in indoor air quality monitoring was designed to improve the limited sensor node resources such as battery consumption and networking.