• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of the Korea Computer Industry Society
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• v.9 no.1
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• pp.21-28
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• 2008

In this paper, the edge parameters were drawn from speaking image around lip and effective automatic lip reading system to recognize the Korean 'a/e/i/o/u' 5 owels were constructed using the parameter. Speaking images around lip were divided into $5{\times}5$ pane. In each pane the number of digital edge element using Sobel operator were evaluated. The observational error between samples was corrected by using normalization method and the normalized value is used for parameter In the experiment to convince the strength of parameter, 50 normal persons were sampled. The images of 10 persons were analyzed and the images of another 40 persons were experimented for recognition. 500 data are gathered and analyzed. Based on this analysis, the neural net system is constructed and the recognition experiments are performed for 400 data. The neural net system gave the best recognition result of 91.1%.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• Journal of Korean Academy of Nursing
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• v.48 no.3
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• pp.266-278
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• 2018

Purpose: The aim of this study was to develop a substantive theory on self-management conducted by the adolescents with chronic kidney disease from their lived experience. Methods: Data was collected through in-depth interviews from May to December in 2015 with thirteen adolescents with chronic kidney disease. The data collected were analyzed on the basis of Strauss and Corbin's grounded theory. Results: The core of the category found in this study was "overcoming the unstable sense of self- control and integrating disease experience into their life". The causal conditions triggering the central phenomenon were "restriction in daily life" and "manifestation and aggravation of symptom". The central phenomenon in the experience of self-management within the adolescents with chronic kidney disease was "unstable sense of self control". The intervening condition for unstable self control were "micro system support" and "motivational resources". This study found that the adolescents with chronic kidney disease followed a series of strategies when they faced the central phenomenon, including; passive coping, reappraisal of illness, active coping, compliance with treatment, controlling physical activity, and adjusting school life. With these strategic approaches, the adolescents with chronic kidney disease could maintain their active lifestyles and achieve their health behaviors. The process of self-management by these adolescents passed through four phases; limited experience caused by diseases, effort for normalization, reorganizing their daily lives, and integration with daily lives and self-management. Conclusion: This Study explored the process and experience of self-management of adolescents with chronic kidney disease. These findings can be used for basis for developing substantive theory and nursing intervention strategy for adolescents with chronic kidney diseases.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5983-5986
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• 2015

Background: Serum 25-hydroxyvitamin D insufficiency is very common in Pakistan and is often related to inferior prognosis in some cancers but limited data exist for hematopoietic malignancies. This study was conducted to determine the vitamin D insufficiency in B-chronic lymphoid leukemia (CLL) cases at the time of presentation and its possible correlation with clinical staging, hematological parameters and biochemical markers. Materials and Methods: This descriptive cross sectional study was carried at Liaquat National Hospital from January 2011 to June 2013. Sixty patients with B-chronic lymphoid leukemia were enrolled. Complete blood count, vitamin D levels, serum urea, creatinine, uric acid and LDH levels were assessed. Data were compiled and analyzed using SPSS version 21. Results: Out of 60 patients, 42 (70%) were male and 18 (30%) were female. Mean age was $59.0{\pm}9.2years$. The frequency of vitamin D insufficiency was found to be 56.7%. Overall insufficiency was more frequently seen in male gender (40%). Vitamin D insufficiency demonstrated a positive association with low lactate dehydrogenase levels (P=0.005). No links were established with age, clinical stage, hematological and other biochemical markers. Conclusions: Vitamin D insufficiency is high compared with Western studies. Whether normalization of vitamin D insufficiency in deficient B-CLL patients could improve the clinical outcome or delay disease progression will require further studies.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.165-181
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• 2009

This paper presents an effective representation scheme for the shape analysis of the hippocampal structure and a stereoscopic-haptic environment to enhance sense of realism. The parametric model and the 3D skeleton represent various types of hippocampal shapes and they are stored in the Octree data structure. So they can be used for the interactive shape analysis. And the 3D skeleton-based pose normalization allows us to align a position and an orientation of the 3D hippocampal models constructed from multimodal medical imaging data. We also have trained Support Vector Machine (SVM) for classifying between the normal controls and epileptic patients. Results suggest that the presented representation scheme provides various level of shape representation and the SVM can be a useful classifier in analyzing the shape differences between two groups. A stereoscopic-haptic virtual environment combining an auto-stereoscopic display with a force-feedback (or haptic) device takes an advantage of 3D applications for medicine because it improves space and depth perception.

• Hwankyungkyoyuk
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• v.18 no.3 s.28
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• pp.75-90
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• 2005

The purpose of this study was to analyze middle school students' consumption lifestyle and develop a program for measuring Ecological Footprint (EF) for them. For this study, 200 male and female middle school students in large cities, medium & small cities were selected to analyze their consumption lifestyle. It was also that the existing programs for measuring EF were studied and basic rules of setting up new EF indicators were established based on the results of survey and literature study. 15 indexes was selected by dividing the life areas into food, housing, traffic, goods and services areas and than the delpi computer programming tools was used to develop program for measuring EF in this study. The program for measuring EF can be used as educational materials for consumers' environment education in the areas of social environment education and school environment education. The followings are suggestions coming out of this study. First, it is required to revise and complement program for measuring EF analyzing the problems that occur when applying it to middle school students actually. Second, some data that used during normalization of EF ate originally from the USA. So it is necessary to change the data to meet the Korean situation. Third, it is necessary to have design work that can invite interests of students with consumers' environmental education materials through cooperation between environmental education experts and computer programmers. Fourth, it is necessary to have practical research with consumers' environmental education adding educational contents into EF measurement program. Fifth, it is necessary to develop a method for distribution an expansion of the program for measuring EF to make it usable in different types of environmental education materials.