• International conference on construction engineering and project management
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• 2020.12a
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.32-38
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• 2000

One of the most popular algorithm in adaptive signal processing is the least mean square(LMS) algorithm. The majority of these papers examine the LMS algorithm with a constant step size. The choice of the step size reflects a tradeoff between misadjustment and the speed of adaptation. Subsequent works have discussed the issue of optimization of the step size or methods of varying the step size to improve performance. However there is as yet no detailed analysis of a variable step size algorithm that is capable of giving both the adaptation speed and the convergence. In this paper we propose a new variable step size algorithm where the step size adjustment is controlled by the gradient of error square. The proposed algorithm is performed in the Walsh-Hadamard domain in real-valued orthogonal transform because of fast convergence. The simulation results using the new algorithm for noise canceller system is described. They are compared to the results obtained by other algorithms. It is shown that the proposed algorithm produces good results compared with conventional algorithms.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.3
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• pp.256-265
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• 2017

Seafood is attracting attention as a future food industry. In recent years, the demand for fishery equipment of mechanization, automation, and unmanned was increased due to the environment affected by seafood processing, stricter regulations on safety, decline and aging of fishery worker. Ark shell (Scapharca subcrenata) was being produced in many steps in the production process. The process has been made such as collection-landing-washing-first sort (goods/non-goods)-transports-second sort (size). It was undergone first and second steps by delivering to the consumer. Here, the first step is to sort goods to collection and the second step is to sort by size. The fishery workers need ten people in first step and six people in second step. The workload of one hour per kg is 4,247 kg/h in first step and 2,213 kg/h in second step. In addition, the goods ratio by work process was 79% in first step and 98% in the second step. In this process, a lot of fishery worker and working time is needed. Therefore, this study developed elemental techniques for an automated size sorting system considering the working process problem, time and situation for washing and sorting of ark shell.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.590-599
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• 2017

In South Korea, sewage pipeline exploration devices have been developed using high resolution digital cameras of 2 mega-pixels or more. On the other hand, most devices are less than 300 kilo-pixels. Moreover, because 100 kilo-pixels devices are used widely, the environment for image processing is very poor. In this study, very low resolution ($240{\times}320$ = 76,800 pixels) images were adapted when it is difficult to detect cracks. Considering that the images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique was studied using digital image processing technology for low resolution images of sewage pipelines. The authors developed a program to automatically detect cracks as 6 steps based on the MATLAB functions. In this study, the second step covers an algorithm developed to find the optimal threshold value, and the fifth step deals with an algorithm to determine cracks. In step 2, Otsu's threshold for images with a white caption was higher than that for an image without caption. Therefore, the optimal threshold was found by decreasing the Otsu threshold by 0.01 from the beginning. Step 5 presents an algorithm that detects cracks by judging that the length is 10 mm (40 pixels) or more and the width is 1 mm (4 pixels) or more. As a result, the crack detection performance was good despite the very low-resolution images.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.97-111
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• 2008

Commercial data mining tools basically support binary attributes only in mining association rules, that is, they can mine binary association rules only. In general, however. transaction databases contain not only binary attributes but also quantitative attributes. Thus, in this paper we propose a systematic approach to mine quantitative association rules---association rules which contain quantitative attributes---using commercial mining tools. To achieve this goal, we first propose an overall working framework that mines quantitative association rules based on commercial mining tools. The proposed framework consists of two steps: 1) a pre-processing step which converts quantitative attributes into binary attributes and 2) a post-processing step which reconverts binary association rules into quantitative association rules. As the pre-processing step, we present the concept of domain partition, and based on the domain partition, we formally redefine the previous bipartition and multi-partition techniques, which are mean-based or median-based techniques for bipartition, and are equi-width or equi-depth techniques for multi-partition. These previous partition techniques, however, have the problem of not considering distribution characteristics of attribute values. To solve this problem, in this paper we propose an intuitive partition technique, named standard deviation minimization. In our standard deviation minimization, adjacent attributes are included in the same partition if the change of their standard deviations is small, but they are divided into different partitions if the change is large. We also propose the post-processing step that integrates binary association rules and reconverts them into the corresponding quantitative rules. Through extensive experiments, we argue that our framework works correctly, and we show that our standard deviation minimization is superior to other partition techniques. According to these results, we believe that our framework is practically applicable for naive users to mine quantitative association rules using commercial data mining tools.

• Food Science and Preservation
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• v.18 no.6
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• pp.898-906
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• 2011

Physicochemical and physiological characteristics of red garlic were investigated in each of the four steps of its processing, and were compared with those of fresh and black garlic. With the progress in processing, the lightness value of the external and internal colors of the red garlic significantly decreased. The hardness was highest in the fresh garlic and lowest in the black garlic. During processing, the red garlic tended to become hard. The crudeprotein and ash contents were highest in the red garlic (step 4), but its moisture content was the lowest. The crudelipid content of the red garlic was lower than that of the fresh garlic. The pH of the red garlic showed little difference from that of the fresh garlic, but the black garlic was significantly, acidified. The acidity and pH contradicted each other : the black garlic had the highest acidity, and the acidity of the red garlic was within the low range. The fresh garlic had the lowest in reducing-sugar content, but such content was significantly increased in the red garlic and black garlic. Six kinds of organic acid were detected in the fresh garlic, and the same contents were also quantified in the red garlic until third step. In step 4, malic acid was not detected in the red garlic. Acetic and citric acid were only in the black garlic. S-allyl cysteine content of the red garlic was $18.05{\pm}0.53$ mg/100 g, similar to that of the black garlic ($19.43{\pm}0.50$ mg/100 g).

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.403-408
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• 2003

A general meaning of caricaturing is that a representation, especially pictorial or literary, in which the subject's distinctive features or peculiarities are deliberately exaggerated to produce a comic or grotesque effect. In other words, a caricature is defined as a rough sketch(dessin) which is made by detecting features from human face and exaggerating or warping those. There have been developed many methods which can make a caricature image from human face using computer. In this paper, we propose a new caricaturing system. The system uses a real-time image or supplied image as an input image and deals with it on four processing steps and then creates a caricatured image finally. The four Processing steps are like that. The first step is detecting a face from input image. The second step is extracting special coordinate values as facial geometric information. The third step is deforming the face image using local warping method and the coordinate values acquired in the second step. In fourth step, the system transforms the deformed image into the better improved edge image using a fuzzy Sobel method and then creates a caricatured image finally. In this paper , we can realize a caricaturing system which is simpler than any other exiting systems in ways that create a caricatured image and does not need complex algorithms using many image processing methods like image recognition, transformation and edge detection.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.706-711
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• 2010

In the applications of a human speech interface, reducing the error rate in recognition is the one of the main research issues. Many previous studies attempted to correct errors using post-processing, which is dependent on a manually constructed corpus and correction patterns. We propose an automatically learnable post-processing method that is independent of the characteristics of both the domain and the speech recognizer. We divide the entire post-processing task into two steps: error detection and error correction. We consider the error detection step as a classification problem for which we apply the conditional random fields (CRFs) classifier. Furthermore, we apply transformation-based learning (TBL) to the error correction step. Our experimental results indicate that the proposed method corrects a speech recognizer's insertion, deletion, and substitution errors by 25.85%, 3.57%, and 7.42%, respectively.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.383-396
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• 2018

Various parallel processing R packages are used for fast processing and the analysis of big data. Parallel processing is used when the work can be decomposed into tasks that are non-interdependent. In some cases, each task decomposed for parallel processing can also be decomposed into non-interdependent subtasks. We have to choose whether to parallelize the decomposed tasks in the first step or to parallelize the subtasks in the second step when facing nested parallelism situations. This choice has a significant impact on the speed of computation; consequently, it is important to understand the nature of the work and decide where to do the parallel processing. In this paper, we provide an idea of how to apply parallel computing effectively to problems by illustrating how to select a parallelism point for the bandwidth selection of nonparametric regression.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.39-47
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• 2021

Reduction effect of the spread of odorant and fine dust through windbreak trees can be predicted through numerical analysis. However, there is a disadvantage that a large space and destructive experiments must be carried out each time to calculate the aerodynamic coefficient of the tree. In order to overcome these shortcomings, In this study, we aimed to estimate the aerodynamic coefficient (C₀, C₁, C₂) by using image processing. Thuja occidentalis, which can be used as windbreak were used as the material. The leaf area index was estimated from the leaf area ratio using image processing with leaf weight, and the optical porosity was calculated through image processing of photos taken from the side while removing the leaves step-by-step. Correlation analysis was conducted with the aerodynamic coefficient of Thuja occidentalis calculated from the wind tunnel test and leaf area index and optical porosity calculated from the image analysis. The aerodynamic coefficient showed positive and negative correlations with the leaf area index and optical porosity, respectively. The results showed that the possibility of estimating the aerodynamic coefficient using image processing.