• 한국공간정보시스템학회 논문지
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• 제11권2호
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• pp.89-98
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• 2009

데이터 스트림은 다양한 입력속도로 끊임없이 입력되고 데이터 스트림을 저장하는 메모리상의 저장공간은 유한하기 때문에 때때로 저장공간을 초과하는 데이터가 입력되는 경우가 발생한다. 이 문제를 해결하기 위해 초과되는 데이터의 일부를 버려 메모리 초과를 방지하는 부하제한 기법이 연구되었다. 기존의 부하제한 기법은 데이터의 편차에 따른 최적의 샘플링 비율을 갖는 랜덤 샘플링을 사용한다. 그러나 이 기법은 공간적 특성을 고려하지 않기 때문에 공간 질의에 사용되는 데이터와 사용되지 않는 데이터를 구분하지 않고 샘플링 한다. 그래서 공간 질의가 포함되는 u-GIS 환경에서는 질의 정확도가 감소하는 문제가 발생하였다. 본 논문에서는 공간 질의와 비공간 질의가 동시에 발생하는 u-GIS 환경에서 질의 정확도를 보다 향상 시키는 부하제한 기법을 연구하였다. 이 기법은 동시에 실행되는 공간 질의의 공간적 이용도에 따라 차등적으로 샘플링을 하여, 질의에 이용될 확률이 낮은 데이터를 샘플링을 한다. 제안된 부하제한 기법은 공간질의가 존재하는 경우 질의 정확도를 크게 향상 시켰고, 샘플링 중 공간 필터링 연산을 적용하여 질의처리 속도도 일부 향상 시켰다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1649-1665
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• 2021

In view of the low accuracy of the traditional FunkSVD algorithm, and in order to improve the computational efficiency of the algorithm, this paper proposes a parallel algorithm of improved FunkSVD based on Spark (SP-FD). Using RMSProp algorithm to improve the traditional FunkSVD algorithm. The improved FunkSVD algorithm can not only solve the problem of decreased accuracy caused by iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, thereby achieving the effect of improving the accuracy of the algorithm. And using the Spark big data computing framework to realize the parallelization of the improved algorithm, to use RDD for iterative calculation, and to store calculation data in the iterative process in distributed memory to speed up the iteration. The Cartesian product operation in the improved FunkSVD algorithm is divided into blocks to realize parallel calculation, thereby improving the calculation speed of the algorithm. Experiments on three standard data sets in terms of accuracy, execution time, and speedup show that the SP-FD algorithm not only improves the recommendation accuracy, shortens the calculation interval compared to the traditional FunkSVD and several other algorithms but also shows good parallel performance in a cluster environment with multiple nodes. The analysis of experimental results shows that the SP-FD algorithm improves the accuracy and parallel computing capability of the algorithm, which is better than the traditional FunkSVD algorithm.

• Smart Structures and Systems
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• 제16권4호
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• pp.641-665
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• 2015

In this paper, a simple and efficient phenomenological macroscopic one-dimensional model is proposed which is able to simulate main features of shape memory alloys (SMAs) particularly ferro-elasticity effect. The constitutive model is developed within the framework of thermodynamics of irreversible processes to simulate the one-dimensional behavior of SMAs under uniaxial simple tension-compression as well as pure torsion+/- loadings. Various functions including linear, cosine and exponential functions are introduced in a unified framework for the martensite transformation kinetics and an analytical description of constitutive equations is presented. The presented model can be used to reproduce primary aspects of SMAs including transformation/orientation of martensite phase, shape memory effect, pseudo-elasticity and in particular ferro-elasticity. Experimental results available in the open literature for uniaxial tension, torsion and bending tests are simulated to validate the present SMA model in capturing the main mechanical characteristics. Due to simplicity and accuracy, it is expected the present SMA model will be instrumental toward an accurate analysis of SMA components in various engineering structures particularly when the ferro-elasticity is obvious.

• 한국멀티미디어학회논문지
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• 제16권11호
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• pp.1261-1271
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• 2013

Zoom tracking involves the automatic adjustment of the focus motor in response to the zoom motor movements for the purpose of keeping an object of interest in focus, and is typically achieved by moving the zoom and focus motors in a zoom lens module so as to follow the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. Thus, one can simply implement zoom tracking by following the most closest trace curve after all the trace curve data are stored in memory. However, this approach is often prohibitive in practical implementation because of its large memory requirement. Many other zoom tracking methods such as GZT, AZT and etc. have been proposed to avoid large memory requirement but with a deteriorated performance. In this paper, we propose a new zoom tracking method called 'Approximate Feedback Zoom Tracking method (AFZT)' on DM36x-based IP network camera, which does not need large memory by approximating nearby trace curves, but generates better zoom tracking accuracy than GZT or AZT by utilizing focus value as feedback information. Experiments through real implementation shows the proposed zoom tracking method improves the tracking performance and works in real-time.

• 정보저장시스템학회논문집
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• 제7권2호
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• pp.53-59
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• 2011

We have developed a compact and high-power mode-locked fiber laser for multilayered optical memory. Fiber lasers have the potential to be compact and stable light sources that can replace bulk solid-state lasers. To generate high-power pulses, we used stretched-pulse mode locking. The average power and pulse width of the output pulse from the fiber laser that we developed were 109 mW and 2.1 ps, respectively. The dispersion of the output pulse was compensated with an external single-mode fiber of 2.5 m length. The pulse was compressed from 2.1 ps to 93 fs by dispersion compensation. The fiber laser we have developed is possible to use as a light source of multilayered optical memory. We also present a fiber confocal microscope as an alignment-free readout system of multilayered optical memories. The fiber confocal microscope does not require fine pinhole position alignment because the fiber core is used as the point light source and the pinhole, and both of which are always located at the conjugated point. The configuration reduces the required accuracy of pinhole position alignment. With these techniques we can present an all-fiber recording and readout system for multilayered memories.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.183-183
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• 2020

Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.

• 한국정보과학회논문지:시스템및이론
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• 제31권1_2호
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• pp.125-134
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• 2004

최근에 프로세서-메모리간 성능격차 문제를 완화하기 위하여 내장캐시의 접근실패율을 낮추고 메모리 대역폭을 확장하는 내장캐시 압축시스템이 제안되었다. 내장캐시 압축시스템은 데이타를 압축해 저장함으로써 내장캐시의 실질적 저장공간을 확장하고, 메모리 버스에서 데이타를 압축해 전송함으로써 실질적 메모리 대역폭을 확장한다. 본 논문에서는 이와 같은 내장캐시 압축시스템을 확장해 기존의 주 메모리 압축시스템과 병합해 설계한 이종 메모리 압축시스템을 제안한다. 주 메모리의 기억공간을 효율적으로 확장하고, 내장캐시의 접근실패율을 낮추고, 메모리 대역폭을 확장하고, 압축캐시의 복원시간을 줄이고, 설계 복잡도를 낮추기 위하여 몇 가지 새로운 기법들을 제시한다. 제안하는 시스템과 비교대상 시스템의 성능은 슈퍼스칼라 구조의 마이크로프로세서 시뮬레이터를 수정하여 실행기반 시뮬레이션을 통해 검증한다. 본 논문에서 사용한 실험방법은 기존의 트레이스기반 시뮬레이션과 비교해 보다 높은 정확도를 갖는다. 실험결과 주 메모리 확장에 따른 이득을 고려하지 않은 경우에 제안하는 시스템은 일반 메모리시스템에 비하여 수행시간을 내장캐시의 크기에 따라 최대 4-23%가량 단축한다. 제안하는 시스템의 데이타 메모리와 코드 메모리의 확장비율은 각각 57-120%와 27-36%이다.

• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.294-304
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• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권9호
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• pp.3515-3538
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• 2015

Data accuracy can be increased by detecting and removing the incorrect data generated in wireless sensor networks. By increasing the data accuracy, network lifetime can be increased parallel. Network lifetime or operational time is the time during which WSN is able to fulfill its tasks by using microcontroller with on-chip memory radio transceivers, albeit distributed sensor nodes send summary of their data to their cluster heads, which reduce energy consumption gradually. In this paper a powerful algorithm using proactive fuzzy system is proposed and it is a mixture of fuzzy logic with comparative correlation techniques that ensure high data accuracy by detecting incorrect data in distributed wireless sensor networks. This proposed system is implemented in two phases there, the first phase creates input space partitioning by using robust fuzzy c means clustering and the second phase detects incorrect data and removes it completely. Experimental result makes transparent of combined correlated fuzzy system (CCFS) which detects faulty readings with greater accuracy (99.21%) than the existing one (98.33%) along with low false alarm rate.

• ETRI Journal
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• 제40권4호
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• pp.421-434
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• 2018

The application of deep neural networks (DNNs) to connect the world with cyber physical systems (CPSs) has attracted much attention. However, DNNs require a large amount of memory and computational cost, which hinders their use in the relatively low-end smart devices that are widely used in CPSs. In this paper, we aim to determine whether DNNs can be efficiently deployed and operated in low-end smart devices. To do this, we develop a method to reduce the memory requirement of DNNs and increase the inference speed, while maintaining the performance (for example, accuracy) close to the original level. The parameters of DNNs are decomposed using a hybrid of canonical polyadic-singular value decomposition, approximated using a tensor power method, and fine-tuned by performing iterative one-shot hybrid fine-tuning to recover from a decreased accuracy. In this study, we evaluate our method on frequently used networks. We also present results from extensive experiments on the effects of several fine-tuning methods, the importance of iterative fine-tuning, and decomposition techniques. We demonstrate the effectiveness of the proposed method by deploying compressed networks in smartphones.