• Journal of information and communication convergence engineering
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• v.22 no.3
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• v.y2004m10
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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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• v.8 no.2
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.73-80
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• 2014

MapReduce is a widely used programming model for analyzing and processing Big data. Aggregate queries are one of the most common types of queries used for analyzing Big data. In this paper, we propose an efficient method for processing an aggregate query stream, where many concurrent users continuously issue different aggregate queries on the same data. Instead of processing each aggregate query separately, the proposed method processes multiple aggregate queries together in a batch by a single, optimized MapReduce job. As a result, the number of queries processed per unit time increases significantly. Through various experiments, we show that the proposed method improves the performance significantly compared to a naive method.

• Spatial Information Research
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• v.19 no.3
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• pp.95-106
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• 2011

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation(BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a Quad-tree, and then processes aggregate queries in parallel for each cell region according to routing. And it sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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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%.

• Proceedings of the Korea Concrete Institute Conference
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• 1995.04a
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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.

• Proceedings of the Korea Concrete Institute Conference
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• 1995.10a
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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.

• Proceedings of the KSRS Conference
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• 2003.11a
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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.