• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.8-14
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• 2012

Improving the speed of image processing is in great demand according to spread of high quality visual media or massive image applications such as 3D TV or movies, AR(Augmented reality). SIMD computer attached to a host computer can accelerate various image processing and massive data operations. MAMS is a multi-access memory system which is, along with multiple processing elements(PEs), adequate for establishing a high performance pipelined SIMD machine. MAMS supports simultaneous access to pq data elements within a horizontal, a vertical, or a block subarray with a constant interval in an arbitrary position in an $M{\times}N$ array of data elements, where the number of memory modules(MMs), m, is a prime number greater than pq. MAMS-PP4 is the first realization of the MAMS architecture, which consists of four PEs in a single chip and five MMs. This paper presents implementation of image processing algorithms and performance analysis for MAMS-PP16 which consists of 16 PEs with 17 MMs in an extension or the prior work, MAMS-PP4. The newly designed MAMS-PP16 has a 64 bit instruction format and application specific instruction set. The author develops a simulator of the MAMS-PP16 system, which implemented algorithms can be executed on. Performance analysis has done with this simulator executing implemented algorithms of processing images. The result of performance analysis verifies consistent response of MAMS-PP16 through the pyramid operation in image processing algorithms comparing with a Pentium-based serial processor. Executing the pyramid operation in MAMS-PP16 results in consistent response of processing time while randomly response time in a serial processor.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.483-492
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• 1993

미래의 차의 선택사항이 될 여행정보시스템을 설치하여 그 성능의 인간공학적 조사를 수행하였다. 이 시스템을 실제로 GM자동차 Oldsmobile Toronado형 100대에 설치하여 사용자가 얼마나 쉽게, 빨리 그리고 안락하게 갈 수 있고, 어떻게 하면 좀 더 이것을 인간공학적으로 향상시킬수 있는 가를 연구하였다. 100대중 23대는 대학교에서 연구를 하고 나머지는 AAA회사의 회원권을 가진 지역사용자들에게 AAA회사와 AVIS회사를 통하여 대여를 하여 컴퓨터차(TravTek시스템)의 성능과 효율성을 시험하였다. 그 내용을 살펴보면, 차에 부착된 컴퓨터의 컬러화면으로 사용자에게 도로를 보여주고, 컴퓨터가 최단거리를 제시하여 운전자에게 컴퓨터음성과 화면으로 길을 안내한다. 그 파급효과로 도로 체증현상을 막고, 기름의 낭비도 절약하고, 밤에도 안전하게 운행할 수 있게 할 뿐만아니라 처음보는 거리라고 하더라도 컴퓨터가 안내하면서 목적지까지 무사히 도착할 수 있게 하여 준다. 이러한 시스템을 설치한 차를 타고 여행할 때, 여행자가 과연 얼마나 안락하게 여행할 수 있으며, 도로의 체증현상을 줄이고, 사고를 예방하며, 차의 설계와 목적이 인간공학적으로 합당한 가를 알아보고자 하는 연구이다. 인간공학적 평가 인자들은 (1) 운전자의 수행도, (2) 사용자 선호도, (3) 사용자 인식, (4) 운행정보등이다. 그리고 컴퓨터음성을 사용하였을 때와 사용하지 않았을 때의 두가지 경우와 (1) 움직이는 컴퓨터지도를 사용하였을 때 (2) 단순화한 도로안내를 사용하였을 때, (3) 컴퓨터 지도를 사용하지 않았을 경우(종이지도사용)에 관해 위의 4가지 인간공학적 인자들을 평가하고자 한다. 이 연구는 아직도 진행중이라 발표하고자 하는 논문역시 현재까지의 연구결과를 토대로 발표하는 것이므로 완전한 결론을 내릴 수는 없고, 진행과 정의내용과 토의사항과 잠정적인 결론을 제시하고자 한다. 의거한 작업순서 결정을 위해 우선 BB의 상한을 구하는 연구를 행했다. 이를 위해 우선 단일작업장에서 야기될 수 있는 모든 상황을 고려한 최적 작업순서 결정규칙을 연구했으며, 이의 증명을 위해 이 규칙에 의거했을 때의 보완작업량이 최소가 된다는 것을 밝혔다. 보완작업 계산의 효율성을 제고하기 위해 과부하(violation)개념을 도입하였으며, 작업유형이 증가된 상황에서도 과부하 개념이 보완작업량을 충분히 반영할 수 있음을 밝혔다. 본 연구에서 제시한 최적 작업순서 규칙에 의거했을 때 야기될 수 있는 여러가지 경우의 과부하를 모두 계산했다. 앞에서 개발된 단일작업량의 최적 작업순서 결정규칙을 이용하여 다작업장의 문제를 실험했다. 이 문제는 규모가 매우 크므로 Branch & Bound를 이용하였으며, 각 가지에서 과부하량이 최적인 경우만을 고려하는 휴리스틱을 택하여 실험자료를 이용하여 여러 회 반복실험을 행했다. 그리고 본 연구의 성과를 측정하기 위해 휴리스틱 기법시 소요되는 평균 CPU time 범위에서, 랜덤 작업순서에 따른 작업할당을 반복실험하여 이중 가장 좋은 해와 비교했다. 그러나 앞으로 다작업장 문제를 다룰 때, 각 작업장 작업순서들의 상관관계를 고려하여 보다 개선된 해를 구하기 위한 연구가 요구된다. 또한, 준비작업비용을 발생시키는 작업장의 작업순서결정에 대해서도 연구를 행하여, 보완작업비용과 준비비용을 고려한 GMMAL 작업순서문제를 해결하기 위한 연구가 수행되어야 할 것이다.로 이루어 져야 할 것이다.태를 보다 효율적으로 증진시킬 수 있는 대안이 마련되어져야 한다고 사료된다.$\ulcorner$순응$\lrcorner$의 범위를 벗어나지 않는다. 그렇기 때문에도 $\ulcorner$순응$\lrcorner$과 $\ulcorner$표현$\lrcorner$

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.65-75
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• 2015

This paper analyzes the determinants of wage for the college and university graduates utilizing both individual-level and industry-level variables. We note that wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and industry-level (level-2) variables. Then, the assumption that individual wage is independent in the classical regression is violated. Therefore, this paper utilizes the hierarchical linear model (HLM). The major results are the followings. First, the multiple correspondence analysis including level-1 and 2 variables reveals that both level 1 and level 2 variables affects individual wages judging from the fact that the values of level 1 and level 2 variables differ across the different level of individual wage groups. Second, the decision tree analysis including level-1 and 2 variables shows that the most influential variable in wage determination is industry-level wage and the next is industry-level working hour, ages and sex in the decling order in. This suggests that the utilization of the HLM is appropriate since the characteristics of industry is important in determining the individual wage. Third, it is shown that the HLM model is the best compared to the other models which do not take level-1 and level-2 variables simultaneously into account.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.25-39
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• 2017

Over 5 million people participate in cycle racing betting and its revenue is more than 2 trillion won. This study predicts the ranking of cycle racing using various statistical analyses and identifies important variables which have influence on ranking. We propose competitive ranking prediction models using various classification and regression methods. Our model can predict rankings with low misclassification rates most of the time. We found that the ranking increases as the grade of a racer decreases and as overall scores increase. Inversely, we can observe that the ranking decreases when the grade of a racer increases, race number four is given, and the ranking of the last race of a racer decreases. We also found that prediction accuracy can be improved when we use centered data per race instead of raw data. However, the real profit from the future data was not high when we applied our prediction model because our model can predict only low-return events well.

• Journal of Soil and Groundwater Environment
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• v.11 no.5
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• pp.1-8
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• 2006

The objective of this study is to assess impacts of sorption heterogeneity on the transport of leachate leaked from unlined landfill sites and is accomplished by examining the results from a series of Monte-Carlo simulations. For random distribution coefficient ($K_{d}$) fields with four different levels of heterogeneity ranging from homogeneous to highly heterogeneous, the transport of leachate was investigated by linking a saturated flow model with a contaminant transport model. Impacts of a chemical heterogeneity were evaluated using point statistics values such as mean, standard deviation, and coefficient of variation of the concentration obtained at monitoring wells from 100 Monte-Carlo trials. Inspection of point statistics shows that the distribution of distribution coefficient in the landfill site proves to be an important parameter in controlling leachate concentrations. In comparison to homogeneous sorption, heterogeneous $K_{d^-}$ fields produce the variability in the leachate concentration for different realizations. The variability increases significantly as the variance in the $K_{d^-}$ field and the travel time between source and monitoring well increase. These outcomes indicate that use of a constant homogeneous $K_{d}$ value for predicting the transport of leachate can result in significant error, especially when variability in $K_{d}$ is high.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.397-408
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• 2009

In this study, a stochastic precipitation generation framework for simultaneous simulation of daily precipitation at multiple sites is presented. The precipitation occurrence at individual sites is generated using hybrid-order Markov chain model which allows higher-order dependence for dry sequences. The precipitation amounts are reproduced using Anscombe residuals and gamma distributions. Multisite spatial correlations in the precipitation occurrence and amount series are represented with spatially correlated random numbers. The proposed model is applied for a network of 17 locations in the middle of Korean peninsular. Evaluation statistics are reported by generating 50 realizations of the precipitation of length equal to the observed record. The analysis of results show that the model reproduces wet day number, wet and dry day spell, and mean and standard deviation of wet day amount fairly well. However, mean values of 50 realizations of generated precipitation series yield around 23% Root Mean Square Errors (RMSE) of the average value of observed maximum numbers of consecutive wet and dry days and 17% RMSE of the average value of observed annual maximum precipitations for return periods of 100 and 200 years. The provided model also reproduces spatial correlations in observed precipitation occurrence and amount series accurately.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.172-181
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• 2023

In this study, real-time damage evaluation of cable-stayed bridges was conducted for cable damage. ICP type acceleration sensors were used for real-time damage assessment of cable-stayed bridges, and Kinetic Energy Optimization Techniques (KEOT) were used to select the optimal conditions for the location and quantity of the sensors. When a structure vibrates by an external force, KEOT measures the value of the maximum deformation energy to determine the optimal measurement position and the quantity of sensors. The damage conditions in this study were limited to cable breakage, and cable damage was caused by dividing the cable-stayed bridge into four sections. Through FE structural analysis, a virtual model similar to the actual model was created in the real-time damage evaluation method of cable. After applying random oscillation waves to the generated virtual model and model structure, cable damage to the model structure was caused. The two data were compared by defining the response output from the virtual model as a corruption-free response and the response measured from the real model as a corruption-free data. The degree of damage was evaluated by applying the data of the damaged cable-stayed bridge to the Improved Mahalanobis Distance (IMD) theory from the data of the intact cable-stayed bridge. As a result of evaluating damage with IMD theory, it was identified as a useful damage evaluation technology that can properly find damage by section in real time and apply it to real-time monitoring.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.