• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.163-171
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• 2020

The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.219-220
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• 2021

It is considered that the construction industry is one of the pivotal players in the national economy in terms of Gross Domestic Production (GDP) and employment. Behind the positive role of this industrial sector to the national economy, the construction industry generates approximately 50 % of the total waste generation from all the industrial sectors. There are several measures to mitigate the adverse impacts of the construction waste such as reduce, reuse and recycle. Recycling would be one of the effective strategies for waste minimisation, which would be able to reduce the demand upon new resources as well as enhance reusing the construction materials on sites. The automated construction waste classification system would make it possible not only to reduce the amount of labour input but also mitigate the possibility of errors during the manual classification process. In this study, we proposed an automated waste segmentation and classification system for recycling the construction and demolition waste in the real construction site context. Since the practical application to the real-world construction sites was one of the significant factors to develop the system, a YOLACT (You Only Look At CoefficienTs) algorithm was chosen to conduct the study. In this study, it is expected that the proposed system would make it possible to enhance the productivity as well as the cost efficiency by reducing the manpower for the construction and demolition waste management at the construction site.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.23-35
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• 2020

The development of digital technology is changing the paradigm of the healthcare industry to preventive and consumer-oriented. The combination of the ICT industry and the bio-healthcare industry is emerging as a core industry in the era of the Fourth Industrial Revolution. The Korean government has also selected the bio-healthcare industry as one of the three key future development industries. In May, the government announced its bio-health industry innovation strategy and set a goal of 300,000 employees. Therefore, analyzing the effects of employment on the related industries of the digital bio-healthcare industry is very important for the establishment of future industrial and technology development policies. The research method restructures the integrated classification of 32 industries into 34, including the digital bio-healthcare industry, using the classification criteria of the government and professional institutions, and then reorganizes the digital bio-healthcare industry into eight industries classified as one industry group. The analysis data was taken from the Bank of Korea's 2019 data. Various trigger coefficients and ripple effects coefficients were rewritten using the analysis method of the Input-output Statistics. The analysis of the results compares the employment-induced effects of the digital bio-healthcare industry and the ripple effects of related industries in production, investment and value-added. In addition, in terms of investment effect, the effects of in-house and related industries were compared. It is hoped that the results of this study will be used to establish employment and industrial policies.

• Korean Security Journal
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• no.8
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• pp.155-196
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• 2004

In the 1970th, Security industry in Korea based auxiliary measures for confrontation about increase of a crime by Industrialization and Urbanization. However, This based growth of 1980th - 1990th Security industry, On the Study consider expansion step of Security industry in Korea with classification policing conditions in the 1970th and Security Law in the 1976th. In the 1976th, Security industry in Korea play an important part by maintenance of social order and inspire 'Security of one's own accord' into the hearts of the people.

• The Research Journal of the Costume Culture
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• v.21 no.6
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• pp.961-966
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• 2013

Being one of the most important textile and apparel producers for years, Turkey began to become active in terms of retailing. Although retailing industry is in its growing phase, the social and economic influences caused the customers' tastes and demands to be more distinctive and segmented in parallel with the advancement of the retail industry. Therefore, the retail industry began to develop in more fragmented way where clear boundaries between different types of retailers were established. In this study, the apparel retail market is overviewed and analyzed within the context for determination of the current situation and future prospective. To this aim, the textile and apparel companies that are active in Turkey were classified into groups based on the type of distribution channels they used. Then, the performances of the groups were established using the secondary type of resources. Finally, the findings were summarized, by showing the similarities and differences between different channels.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2011.06a
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• pp.250-253
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• 2011

The cruise industry is one of the world's fastest growing device industry which is structural construction coasts are expensive. Cruise out-fittings are thirty times compare with general large vessels. According to the characteristics of materials or equipments, most equipments are imported from europe. At present domestic shipbuilding industries and minor enterprises have a hard times therefore enhancing technology for the cruise industry could be infused vitality on the industries. If domestic small and midium industries have high technology for the cruise ship, domestic small and midium industries can have global competitiveness compare with european equipment suppliers which has small-scale and high technology. Therefore on this study, cruise ship's new equipment development and a study for the performance assessment and classification type approval certificate was carried out.