• Journal of information and communication convergence engineering
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• 제22권3호
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• pp.249-255
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• 2024

Advancements in deep learning have enhanced vision-based aggregate analysis. However, further development and studies have encountered challenges, particularly in acquiring large-scale datasets. Data collection is costly and time-consuming, posing a significant challenge in acquiring large datasets required for training neural networks. To address this issue, this study introduces a simulation that efficiently generates the necessary data and labels for training neural networks. We utilized a genetic algorithm (GA) to create optimized lists of aggregates based on the specified values of weight and particle size distribution for the aggregate sample. This enabled sample data collection without conducting sieving tests. Our evaluation of the proposed simulation and GA methodology revealed errors of 1.3% and 2.7 g for aggregate size distribution and weight, respectively. Furthermore, we assessed a segmentation model trained with data from the simulation, achieving a promising preliminary F1 score of 78.18 on the actual aggregate image.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2004년도 학술대회지
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• pp.15-19
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• 2004

The production of waste concrete is increased continuously by urban renewal, reconstruction, remodeling, and so on. So the positive use of recycled aggregate concrete is needed. Research for recycled aggregate concrete that use recycled aggregate from the mid-80s to solve environmental problem of aggregate insufficiency and waste concrete is consisting vigorously. However, specifications and mix design about waste concrete's use are evading use of recycled aggregate concrete yet in spot being not taken a triangular position. Therefore. it analyze existing research data for recycled aggregate concrete collection to develop strength estimate program in this research. Recycled aggregate concrete's strength estimate program if specifications and mix design about recycled aggregate concrete are taken a triangular position to foundation recycled aggregate concrete's practical use to increase judge.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.551-554
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• 2003

Aggregate operator which belongs to query operations are important in specialized systems such as geographic information system(GIS) and spatial database system. Most of data describing objects in the real world are characterized by space and time attributes. Till now, however, works on aggregate operations have only dealt with spatial or temporal aspect of object. The current demand of aggregate operations relates to spatiotemporal data which are contained both spatial and temporal data concurrently. Therefore, work on spatiotemporal operations is focused on database area. In this paper, we propose spatiotemporal aggregate functions that operate on spatiotemporal data. Above all, we support spatiotemporal aggregate functions on the basis of three dimensional spatiotemporal models that are defined with the linear one dimensional temporal domain. The proposed algorithms are evaluated through some implementation results. We are sure that the achievement of our work is useful and efficient.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권2호
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• pp.73-80
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• 2014

맵리듀스는 빅데이터 분석 및 처리에 널리 사용되는 프로그래밍 모델이다. 빅데이터 분석을 위해 흔히 사용되는 질의 중 하나는 집계 질의(aggregate query)이다. 본 논문에서는 여러 사용자가 동시에 여러 집계 질의를 계속해서 요청하는 경우, 맵리듀스를 사용하여 이들 질의를 효율적으로 처리하는 방법을 제안한다. 제안 방법은 각 집계 질의를 개별적으로 처리하지 않고, 여러 집계 질의를 묶어 하나의 최적화된 맵리듀스 잡(job)으로 만들어 일괄 처리한다. 그 결과로 제안 방법은 단순 방법에 비해 시간당 처리하는 질의 수를 크게 증가시킨다. 성능 평가를 통해, 제안 방법은 단순 방법에 비해 질의 처리 속도를 크게 향상시킴을 보인다.

• Spatial Information Research
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• 제19권3호
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• pp.95-106
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• 2011

최근 무선 센서 네트워크에서 센서로부터 원하는 데이타를 가져오는 네트워크 내 집계 질의처리 기법에 대한 연구가 활발히 진행되고 있다. 기존의 대표적인 네트워크 내 집계 질의 처리 기법들은 집계 질의 처리를 위해 라우팅 알고리즘과 데이타 구조를 제안하고 있다. 그러나 이러한 기법들은 센서 노드들의 에너지 소모가 크고, 질의 처리 결과 정확도가 떨어지고, 또한 질의 처리 시간이 오래 걸리는 문제점들을 가지고 있다. 본 논문에서는 이러한 기존 집계 질의 처리 기법들의 문제점을 해결하고 무선 센서 네트워크에서 보다 효율적인 집계 질의 처리를 위해 BPA(Bucket-based Parallel Aggregation)를 제시하였다. BPA는 질의 영역을 센서 노드 분포에 따라 쿼드 트리로 구성하여 집계 질의를 병렬로 처리하고, 각 센서 노드로 하여금 데이타를 이중 전송하게 함으로써 전송 오류로 인한 데이타 손실을 줄인다. 또한, BPA는 집계 질의 처리시 버켓 기반의 데이타 구조를 이용하고 이러한 버켓 데이타 구조를 버켓내 데이타 개수에 따라 적응적으로 분할 및 합병한다. 특히 버켓내 데이타 크기를 줄이기 위해 데이타를 압축하고 데이타 전송 횟수를 줄이기 위해 필터링을 수행한다. 마지막으로 센서 데이타를 이용한 다양한 실험을 통해 본 논문에서 제안하는 BPA의 우수성을 입증하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 가을학술발표대회논문집
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• pp.209-210
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• 2023

In this study, the accuracy of the assembly rate of fine aggregate and the cleavage rate of coarse aggregate was analyzed using the constructed learning data. As a result, it was possible to predict the distribution of assembly rate for fine aggregate through a simple sample collection image, showing an accuracy of 96%. The classification of the aggregates could be confirmed by analyzing the fracture shape of the gravel, showing an accuracy of 97%.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1995년도 봄 학술발표회 논문집
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• pp.177-182
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• 1995

Large-scaling recycling of demolished concrete will contribute not only to the solution of a growing waste disposal problem. it will also help to conserve natural resoures of sand and gravel and to secure future supply of reasonly priced aggregates for builiding and other construction purposes within large urban areas. because recycled aggregate particles consist of substaintial amount of relatively soft cement paste component, it is less resistant to mechanical actions. With this view in mind, to obtain a reference data for the development of recycling system and to a basic data the guideline of recycled aggregate concrete construction and mix design, this study deals with the comparative analysis of the workability and engineering properties of recycled aggregate concrete according to the factors, such as blending ratio of recycled aggregate with the natural aggregate, addition of flyash, water cement ration.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1995년도 가을 학술발표회 논문집
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• pp.256-261
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• 1995

Large-scaling recycling of demolished concrete will concrete will contribute not only to the solution of a growing waste disposal problem, it will also help to consweve natural resoures of sand and gravel and to secure future supply of reasonly priced aggregates for building and other construction purposes within large urban areas. Because recycled aggregate particles consits of substantial amount of relatively soft cement paste component, it is less resistant to mechanical actions. With this view in mind, to obtain a reference data for the development of recycling system and to a basic data the guiedline of recycled aggregate concrete construction and engineering properties of recycled aggregate concrete according to the factors, such as blending ratio of recyced aggregete with the natural aggregate, addition to the factors, such as blending ratio of recycled aggregete with the natural aggregate, addition of flyash, water coment ratio.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.721-723
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• 2003

Aggregation is an operation that returns a result value through a computational process on the data which satisfy a certain condition. Recently many applications use aggregation to analyze spatiotemporal data. Although spatiotemporal data change its states over time, previous aggregation works have only dealt with spatial or temporal aspect of object. In this paper we propose spatiotemporal aggregate functions that operate on spatiotemporal data. The proposed algorithms are evaluated through some implementation results. The experiment results show that the proposed aggregate functions are applicable to spatiotemporal data efficiently.