• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.415-418
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• 2003

Fuzzy modelling has the approximation property far the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method incorporating the human expert's knowledge or experience in fuzzy rules and it is not easy to End the membership function of fuzzy rule to minimize the output error as well. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the neural network based fuzzy modelling methods that can be used with various type of fuzzy rules. But in this model, it is the problem to End the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning techniques with the measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.473-476
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• 1989

This paper proposes the recognition of Korean character by using the given pre-segmented area. For higher processing speed, we introduce the techniques for pruning the unused segments. And, new thinning algorithm is used for finding skeleton of each segment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.1-8
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• 2024

On-device AI technology, which can operate AI models at the edge devices to support real-time processing and privacy enhancement, is attracting attention. As intelligent IoT is applied to various industries, services utilizing the on-device AI technology are increasing significantly. However, general deep learning models require a lot of computational resources for inference and learning. Therefore, various lightweighting methods such as quantization and pruning have been suggested to operate deep learning models in embedded edge devices. Among the lightweighting methods, we analyze how to lightweight and apply deep learning models to edge computing devices, focusing on pruning technology in this paper. In particular, we utilize dynamic and static pruning techniques to evaluate the inference speed, accuracy, and memory usage of a lightweight AI vision model. The content analyzed in this paper can be used for intelligent video control systems or video security systems in autonomous vehicles, where real-time processing are highly required. In addition, it is expected that the content can be used more effectively in various IoT services and industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1931-1953
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• 2017

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.169-172
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• 2005

Modem SAR interferometry (IFSAR) sensors delivering intensity images and corresponding digital terrain model (DTM) allow for a thorough surface lineament interpretation with the all-weather day-night applicability. In this paper, an automatic linear-feature detection algorithm for high-resolution SAR images acquired in Taiwan is proposed. Methodologies to extract linear features consist of several stages. First, the image denoising techniques are used to remove the speckle noise on the raw image. In this stage, the Lee filter has been chosen because of its superior performance. After denoising, the Coefficient of Variation Detector is performed on the result images for edge enhancements and detection. Dilation and erosion techniques are used to reconnect the fragmented lines. The Hough transform, which is a special case of a more general transform known as Radon transform, is a suitable method for line detection in our analysis. Finally, linear features are extracted from the binary edge image. The last stage contains many substeps such as edge thinning and curve pruning.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.94-102
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• 2023

The Florence Charter, established in 1981 by the ICOMOS-IFLA International Commission on Historic Gardens, considered the historic garden as a plant-dominated architectural composition, a perpetual balance between the artist and the artisan's desire to remain unaltered in perpetuity. Trees, the main component of the garden, require continuous management as they repeat their creation and calling according to the cycle of the seasons and accordingly, it is suggested that management to maintain the appearance of trees in Korean palaces was an indispensable element. Although it is an important matter to historically verify the tree management techniques of the palaces of the past, research has been difficult due to the absence of historical records and the disconnection of life due to the Japanese colonial period. In addition, according to the view that trees were not generally managed in the palace, research on palace tree management techniques has not been conducted so far. Therefore, this study aimed to examine whether or not the tree management of the palaces was performed in the past through expert interviews based on photographs taken in modern times. With the identification of tree species and pruning through in-depth interviews with experts using photographs taken in the modern period, the results are as follows. First, it was found that the shapes of trees and leaves could be identified through photographs and by observing the phenomena caused by planting and pruning, it was possible to estimate whether or not the tree management was implemented in the modern palace. Second, as a result of in-depth interviews with 8 experts in 4 fields, it was possible to determine the tree species and pruning status, purpose, and method. There was no significant difference in opinions between groups, and the evidence was clearly presented. Third, the type of management of trees in the palace was mainly found to be determination of the types of tree, removal of hazardous factors of trees, and management of lower vegetation and through the photographs of Jondeokjeong and Gwanwanjeong, it was confirmed that the trees were managed even before the Japanese colonial era, considering the time of filming. Based on the photographs taken, it was possible to estimate whether the trees were managed before the Japanese colonial period through expert interviews. However, it could not be clarified due to lack of historical materials as to whether it was carried out on its own according to the circumstances of the times or by the Japanese Empire. Still, in this study, evidence was obtained to refute the view that trees of the palace were not managed in the past through the collected data. Expert opinions supporting this view were collected to make the conclusion. In addition, based on the general theory of pruning, an empirical review of expert opinions was conducted to secure the reliability of the research results.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.583-599
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• 2022

Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.9
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• pp.782-791
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• 2013

This paper proposes the location estimation techniques of distributed targets with the multi-sensor data perceived through IR sensors of the military robots. In order to match up targets with measured azimuths, we apply the maximum likelihood (ML), depth-first, and breadth-first tree search algorithms, in which we use the measured azimuths and the number of pixels on IR screen for pruning branches and selecting candidates. After matching up targets with azimuths, we estimate the coordinate of each target by obtaining the intersection point of the azimuths with the least square error (LSE) algorithm. The experimental results show the probability of missing target, mean of the number of calculating nodes, and mean error of the estimated coordinates of the proposed algorithms.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.30-33
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• 2016

With the advent of big data, data mining is more increasingly utilized in various decision-making fields by extracting hidden and meaningful information from large amounts of data. Even as exponential increase of the request of unrevealing the hidden meaning behind data, it becomes more and more important to decide to select which data mining algorithm and how to use it. There are several mainly used data mining algorithms in biology and clinics highlighted; Logistic regression, Neural networks, Supportvector machine, and variety of statistical techniques. In this paper it is attempted to compare the classification performance of an exemplary algorithm J48 and REPTree of ML algorithms. It is confirmed that more accurate classification algorithm is provided by the performance comparison results. More accurate prediction is possible with the algorithm for the goal of experiment. Based on this, it is expected to be relatively difficult visually detailed classification and distinction.