• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.615-622
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• 2014

The Korean film market has rapidly achieved an international scale, and this has led to a need for decision-making based on analytical methods that are more precise and appropriate. In this modern era, a highly advanced information environment can provide an overwhelming amount of data that is generated in real time, and this data must be properly handled and analyzed in order to extract useful information. In particular, the preprocessing of large data, which is the most time-consuming step, should be done in a reasonable amount of time. In this paper, we investigated a big data preprocessing method for predicting movie box office success. We analyzed the movie data characteristics for specialized preprocessing methods, and used the Hadoop MapReduce framework. The experimental results showed that the preprocessing methods using big data techniques are more effective than existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7A
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• pp.706-714
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• 2010

In wireless sensor networks, real-time data delivery schemes typically achieve a desired delivery speed by performing one-hop lookahead. Recently, to reduce the deadline miss ratio against the desired delivery speed, a study has proposed a real-time routing protocol based on proactively performing two-hop lookahead. However, the study might cause heavy message exchange overhead and high computing complexity to carry out obtainment of two-hop neighborhood speed information in the entire sensor nodes whether data are delivered or not. Moreover, although multi-hop lookahead provides the least deadline miss ratio, due to the restriction from the overhead and the complexity, the recent study merely adopts the two-hop lookahead manner. In this paper, we propose a novel real-time routing protocol that adopts on-demand neighborhood multi-hop information obtainments only around data forwarding paths. Simulation results prove that the proposed routing protocol offers better performances with respect to deadline miss ratio, total communication costs, energy efficiency, and network lifetime.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.7
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• pp.23-31
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• 2012

Resource allocation, such as joint rate control and scheduling, is an important issue in cognitive radio networks. However, it is difficult to jointly consider the rate control and scheduling problem due to the stochastic behavior of channel availability in cognitive radio networks. In this paper, we propose an asymmetric joint rate control and scheduling technique under reliability constraints in cognitive radio networks. The joint rate control and scheduling problem is formulated as a convex optimization problem and substantially decomposed into several sub-problems using a dual decomposition method. An algorithm for secondary users to locally update their rate that maximizes the utility of the overall system is also proposed. The results of simulations revealed that the proposed algorithm converges to a globally optimal solution.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.355-361
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• 2008

As agricultural machinery has become larger and tillage practices have changed in recent decades, compaction as a result of wheel traffic and tillage has caused increasing concern. If strategies to manage compaction, such as deep tillage, could be applied only where needed, economic and environmental benefits would result. For such site-specific compaction management to occur, compacted areas within fields must be efficiently sensed and mapped. We previously developed an on-the-go soil strength profile sensor (SSPS) for this purpose. The SSPS measures within-field variability in soil strength at five soil depths up to 50 cm. Determining the variability structure of SSPS data is needed for site-specific field management since the variability structure determines the required intensity of data collection and is related to the delineation of compaction management zones. In this paper, soil bin data were analyzed by a spectral analysis technique to determine the variability structure of the SSPS data, and to investigate causes and implications of this variability. In the soil bin, we observed a repeating pattern due to soil fracture with an approximate 12- to 19-cm period, especially at the 10-cm depth, possibly due to cyclic development of soil fracture on this interval. These findings will facilitate interpretation of soil strength data and enhance application of the SSPS.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.986-995
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• 2012

In this paper we propose a real-time moving object tracking method on a smart phone camera. By considering the limit of non-learning approach on low-performance platforms, we use the sliding-window detection technique based on histogram features. We solve the problem of the time-consuming histogram computation on each sub-window by adapting the integral histogram. For additional speed and tracking performance, we propose a new adaptive bin method. From the experiments on our dataset, we achieved high speed performance demonstrating 34~63 frames per second.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.209-214
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• 2020

The equitable partitioning problem(EPP) is classified as [0/1] binary skill existence or nonexistence and integer skill levels such as [1,2,3,4,5]. There is well-known a polynomial-time optimal solution finding algorithm for binary skill EPP. On the other hand, tabu search a kind of metaheuristic has apply to integer skill level EPP is due to unknown polynomial-time algorithm for it and this problem is NP-hard. This paper suggests heuristic greedy algorithm with polynomial-time to find the optimal solution for integer skill level EPP. This algorithm descending sorts of skill level frequency for each field and decides the lower bound(LB) that more than the number of group, packing for each group bins first, than the students with less than LB allocates to each bin additionally. As a result of experimental data, this algorithm shows performance improvement than the result of tabu search.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.849-858
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• 2004

Most of supervised teaming algorithms could be applied after that continuous variables are transformed to categorical ones at the preprocessing stage in order to avoid the difficulty of processing continuous variables. This preprocessing stage is called global discretization, uses the class distribution list called bins. But, when data are large and the range of the variable to be discretized is very large, many sorting and merging should be performed to produce a single bin because most of global discretization methods need a single bin. Also, if new data are added, they have to perform discretization from scratch to construct categories influenced by the data because the existing methods perform discretization in batch mode. This paper proposes a method that extracts sample points and performs discretization from these sample points in order to solve these problems. Because the approach in this paper does not require merging for producing a single bin, it is efficient when large data are needed to be discretized. In this study, an experiment using real and synthetic datasets was made to compare the proposed method with an existing one.

• Spatial Information Research
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• v.21 no.5
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• pp.43-51
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• 2013

In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial big data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.1-8
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• 2011

In this paper, data transmission corrugated horn antenna for RF sensor satellite system and its radiation characteristic are presented. The proposed antenna is designed, fabricated and measured to obtain the maximum gain at ${\pm}60^{\circ}$ of its radiation patterns. The result of the proposed antenna is that the return loss is less than -15 dB and the gain is over 5 dBi at ${\pm}60^{\circ}$ of its radiation patterns. From these results, it is confirmed that the proposed can be used as data transmission corrugated horn antenna for RF sensor satellite system.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.89-90
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• 2016

인버터에 사용되는 전력용 반도체 소자의 정션 온도는 모듈의 보호 및 수명 예측에 중요한 영향을 미친다. 온도를 추정하기 위해 제조사에서 제공되는 데이터 시트로부터 피라미터를 찾아 입력하게 되는데, 온도, 전류 및 $R_g$ 저항 등의 요인에 의해 이 파라미터가 변경된다. 본 논문은 온도 추정 과정에서 사용되는 파라미터에 온도에 따라 변동되는 성분을 선형화하여 추가하여 현재 온도에 맞는 파라미터를 계산해 낼 수 있는 방법에 관한 것으로 데이터 시트로부터 미리 계산된 계수 값을 이용하여 수식적으로 온도 의존성을 반영할 수 있다.이를 이용하여 부하 전류, 스위칭 주파수, DC Link 전압 등의 변동에 따라 정션 온도를 실시간으로 추정하였으며, iGBT 의 상단에 온도 센서를 부착하여 추정결과를 검증하였다. 본 방법을 통해 파라미터의 온도 의존성을 수식적으로 반영할 수 있으므로 파라미터를 저장하는 인버터의 데이터 저장 공간을 최소화 할 수 있다.