• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.4
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• pp.121-131
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• 1991

This study is about the analysis of the application of non-metric camera to accident sites and aims to present an efficient, an economical and an accurate method of processing accident sites. This was accomplished by observation and accuracy analysis of an experimental model. It can be concluded that by applying the 3-D coordinate system and the bundle adjustment with additional parameters to non-metric cameras, it is possible to achieve an accuracy level of positional values which is similar to that achieved by conventional control surveying and by metric cameras. It was also found that the accuracy of absolute coordinates approached towards the accuracy of metric cameras with the increase of the film size and with the increase of the focal length of the non-metric camera.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.13 no.1
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• pp.95-106
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• 1995

The purpose of this study is to analyse the accuracy of non-metric photos by close-range photogrammetry. Close-range photogrammetry using non-metric photos is 'economical and convenient to handle, but it is insufficient of study on accuracy. To execute this study, first, the terrain model was made and then taken photographs of this model with metric and non-metric cameras. The Bundle adjustment and the Direct linear transformation methods are used for the analysis close-range photogrammetry. The results of the analysis showed that the Bundle adjustment method is a appropriate method for the analysis of the non-netric photo. Therefore, we concluded that the accuracy of the non-metric photo by close-range photogrammetry is applicability for the photogrammetry.

• Tunnel and Underground Space
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• v.29 no.4
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• pp.230-242
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• 2019

While various automatic rock fracture survey methods have been researched, the evaluation of the accuracy of these methods raises issues due to the absence of a metric which fully expresses the similarity between automatic and manual fracture maps. Therefore, this paper proposes a geometry similarity metric which is especially designed to determine the overall similarity of fracture maps and to evaluate the accuracy of rock fracture survey methods by a single number. The proposed metric, Scanline Intersection Similarity (SIS), is derived by conducting a large number of scanline surveys upon two fracture maps using Python code. By comparing the frequency of intersections over a large number of scanlines, SIS is able to express the overall similarity between two fracture maps. The proposed metric was compared with Intersection Over Union (IoU) which is a widely used evaluation metric in computer vision. Results showed that IoU is inappropriate for evaluating the geometry similarity of fracture maps because it is overly sensitive to minor geometry differences of thin elongated objects. The proposed metric, on the other hand, reflected macro-geometry differences rather than micro-geometry differences, showing good agreement with human perception. The metric was further applied to evaluate the accuracy of a deep learning-based automatic fracture surveying method which resulted as 0.674 (SIS). However, the proposed metric is currently limited to 2D fracture maps and requires comparison with rock joint parameters such as RQD.

• Korean Management Science Review
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• v.28 no.1
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• pp.117-127
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• 2011

In the Multi-Echelon maintenance environment, METRIC(Multi-Echelon Technique for Repairable Item Control) has been used in several different inventory level selection models, such as MOD-METRIC, Vari-METRIC, and Dyna- ETRIC. While this model's logic is easy to be implemented, a critical assumption of infinite maintenance capacity would deteriorate actual values, especially Expected Back Order(EBO)s for each item. To improve the accuracy of EBO, we develop two models using simulation and queueing theory that calculates EBO considering finite capacity. The result of our numerical example shows that the expected backorder from our model is much closer to the true value than the one from Vari-METRIC. The queueing model is preferable to the simulation model regarding the computational time.

• Smart Media Journal
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• v.10 no.4
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• pp.45-54
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• 2021

The skin is the body's first line of defense against external infection. When a skin disease strikes, the skin's protective role is compromised, necessitating quick diagnosis and treatment. Recently, as artificial intelligence has advanced, research for technical applications has been done in a variety of sectors, including dermatology, to reduce the rate of misdiagnosis and obtain quick treatment using artificial intelligence. Although previous studies have diagnosed skin diseases with low incidence, this paper proposes a method to classify common illnesses such as warts and corns using a convolutional neural network. The data set used consists of 3 classes and 2,515 images, but there is a problem of lack of training data and class imbalance. We analyzed the performance using a deep metric loss function and a cross-entropy loss function to train the model. When comparing that in terms of accuracy, recall, F1 score, and accuracy, the former performed better.

• Korean Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.140-144
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• 1998

We propose the area between a pair of non-self-intersecting 2-D parametric curves with same endpoints as an alternative distance metric between the curves. This metric is used when d curve is approximated with another in a simpler form to evaluate how good the approximation is. The traditional set-theoretic Hausdorff distance can he defined for any pair of curves but requires expensive calculations. Our proposed metric is not only intuitively appealing but also very easy to numerically compute. We present the numerical schemes and test it on some examples to show that our proposed metric converges in a few steps within a high accuracy.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.4
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• pp.200-207
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• 2004

This paper presents a simple and fast delay metric RC-class interconnects under saturated ramp inputs. The RC delay metric under saturated ramp inputs, called FDM(Fast Delay Metric), can estimate delay times at an arbitrary node using a simple closed-form expression and is extended from delay metric under step input easily As compared with similar techniques proposed in previous researches, it is shown that the FDM technique complexity for a similar accuracy. As the number of circuit nodes increases, there will be a significant difference in estimation times of RC delay between the previous techniques based on two circuit moments and the FDM which do not depend on circuit moments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.1
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• pp.13-24
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• 1988

The determination of three-dimensional positions on a circular or cylindrical surface covers a variety of applications. As an example, consider the monitoring of structures, which is an important topic in the broad category of deformation analysis. The use of convergent photography in determination of these positions has the many advantages over survey based procedures. This paper illustrates results from bundle adjustments derived from convergent photography of a cylindrical object, with both metric and non-metric cameras utilized in the test. In addition to standard error comparisons resulting from the error analysis provided by the bundle adjustments, object space coordinates resulting from metric and non-metric camera network geometries will also be compared.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.294-304
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• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.