• 대한산업공학회지
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• 제34권4호
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• pp.460-469
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• 2008

Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.189-199
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• 2000

불연속면의 분석은 방향성 데이터로서 적은 표본을 가지고 암반의 전체를 추론해야 하는 문제를 갖고 있다. 실험실에서 분석된 표본은 시추공에 의하여 분석된 데이터이므로 그 크기가 제한되어 있어, 거대한 암석을 분석하는 것은 대단히 어렵다. 따라서 이런 표본을 이용하여 거대한 암석의 대표 방향성, 집락 정도, 불연속면의 간격, 암석의 크기를 결정하는 연장성에 관하여 가정된 분포 하에서 계산하고, 이를 이용하여 암석의 분포형태를 관찰할 수 있는 확률 절리망을 그리는 알고리즘을 연구한다. 그리고 실제 불연속면 데이터를 적용하여 이들을 구하고 확률절리망을 작성한다.

• 대한산업공학회지
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• 제39권6호
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• 한국정보통신학회논문지
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• 제12권6호
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• pp.1095-1100
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• 2008

이 논문에서는 곤충 발자국의 패턴을 인식하기 위해, 인식의 기본 단위인 세그먼트를 자동 추출하는 기법과 Trace transform을 이용하여 발자국 인식에 필요한 특징을 추출하는 기법을 제안하였다. Trace transform 방법을 이용하면 패턴의 이동, 회전, 반사에 불변하는 특징 값을 얻을 수 있다. 이러한 특징 값들은 곤충 발자국과 같이 다양한 변형이 존재하는 패턴을 인식하는 데에 적합하다. 특징 값을 도출하기 위한 첫 번째 단계로는 추출된 세그먼트에 대한 Trace transform을 통해 새로운 Trace 이미지를 생성시킨다. 그런 다음, 병렬로 표현되는 trace-line을 따라 특성 함수에 의해 특징들이 일차적으로 도출되고, 또 다시 도출된 특징들은 diametric, circus 단계의 함수를 거치면서 새로운 특징값으로 재구성된다. 2가지 서로 다른 곤충의 발자국 패턴을 이용하여 실험한 결과 곤충 발자국의 이동, 회전, 반사에 관계없이 인식에 적합한 특징 값들이 추출됨을 확인할 수 있었다.

• 대한원격탐사학회지
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• 제24권3호
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• pp.235-243
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• 2008

Object tracking is a common vision task to detect and trace objects between consecutive frames. It is also important for a variety of applications such as surveillance, video based traffic monitoring system, and so on. An efficient moving vehicle clustering and tracking algorithm suitable for traffic monitoring system is proposed in this paper. First, automatic background extraction method is used to get a reliable background as a reference. The moving blob(object) is then separated from the background by mean shift method. Second, the scale invariant feature based method extracts the salient features from the clustered foreground blob. It is robust to change the illumination, scale, and affine shape. The simulation results on various road situations demonstrate good performance achieved by proposed method.

• 한국대기환경학회지
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• 제16권5호
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• pp.469-476
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• 2000

This study was performed to understand the behavior and source characteristics of particulate trace metals in Daegu area. To do this, total of 84 samples had been collected from January to December 1999. TSP (total suspended particulate matter) and PM-10(particulate matter with aerodynamic diameters less 10${\mu}{\textrm}{m}$) were collected by filters on portable air sampler, and in TSP and PM-10 were analyzed by ICP(Inductively Coupled Plasma Spectrometer) after preliminary treatment. The results were follow as: first, annul means of TSP and PM-10 concentration were 123 and 69$\mu\textrm{g}$/㎤ respectively. The concentration of TSP adn PM-10 were highest in winter season compared to other seasons. Second, the concentration of Al, Fe, Mn were higher in TSP than in PM-10, indicating that these metals are generally associate with natural contributions. Third, a hierarchical clustering technique was used to group 9 metals. The results from the cluster analysis of TSP and PM-10 shows a similar clustering pattern : Fe, Al in a group and the rest of the metals such as Ni, Cr, As, Mn, Cd, Pb, Zn in the other group. One group of metal such as Fe, Al is associated with natural sources such as soil and dust. The other is closely related to urban anthropogenic sources such as fuel combustion, incineration, and refuse burning, Finally, using Al as a reference element, enrichment factors were used for identifying the major particulate contributors. The enrichment factors of Al. Fe<10 (standard value of enrichment factor) were considered to have a significant dust and soil source and termed nonenriched. Ni, Cr, As, Mn, Cd, Pb, Zn》10 is enriched and has a significant which is contributed by athropogenic sources.

• Geomechanics and Engineering
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• 제36권3호
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• 전자공학회논문지
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• 제51권10호
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• pp.137-144
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• 2014

전자전지원시스템은 실시간 전자 공격에 대처하기 위해 레이더 신호를 수집하고 분석한다. 레이더 펄스 클러스터링 시스템은 단일 소스에 방사되는 것으로 예상되는 레이더 신호를 분류한다. 본 논문에서는 도착방향, 주파수, 펄스 폭, 연속된 펄스의 도착시간의 차이 4가지 특징을 기반으로 한 클러스터링 알고리즘을 제안하였고 실험을 통하여 제안한 알고리즘이 이동방사체의 추적과 시간적으로 분리된 신호를 다른 군집으로 분리함을 보였다.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• 분석과학
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• 제33권2호
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.