• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.53-60
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• 2018

The ICT concept has been introduced to realize a highly productive smart factory and respond to the demand for small quantity and mass production between textile processes. ICT convergence monitoring system that can produce high productivity textile products by improving product development period, cost, quality and delivery time through ICT based production and optimization of manufacturing process is needed. In this paper, we propose and implement a system design that senses the amount of remaining sewing material using a non-contact sensor that can be mounted on a sewing machine and displays it on a display using IOT-based LATTE-PANDA board.

• Smart Media Journal
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• v.12 no.2
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• pp.36-41
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• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.168-169
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• 2015

Recent wearable smart device products, but with a variety of features and form that go out after a series of releases in has been outside for a lack of consumer content. The device advances, the type of equipment attached to the user's body was released, which was the background to be subjected to a health-care products of interest to the user and the machine-to-machine interaction. This study is to identify health care elements wearable smart device content around the market with features to interact with the game content and game content derived elements fit smart wearable devices. Survey research method was developed or released wearable devices and game content and take advantage of this any existing research literature related to game development. Based on this we derive the interactive elements for a wearable smart devices based.

• Smart Media Journal
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• v.12 no.8
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• pp.18-25
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• 2023

The COVID-19 outbreak in 2020 caused continuous damage worldwode, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are colsed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administraion and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2155-2161
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• 2016

Since Korea is highly dependent on energy imports, it has been making efforts to save energy resources by enacting laws to promote resource saving and recycling. Recently, as a part of recycling of empty bottles, a bottle unattended collecting machine has been installed in the big shopping mall in the metropolitan area. However, there is no commercialization of the unattended collecting machine in Korea and the smart device application for providing information on empty bottle recycling is not yet provided. In this paper, we have designed and constructed a bottle recycling information management system to promote resource recycling. The manager has built a homepage that can manage the information of the empty bottle and the a bottle unattended collecting machine. Also, many people with smart devices can easily access bottle recycling information by using camera and barcode search and label search.

• Smart Media Journal
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• v.6 no.2
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• pp.33-41
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• 2017

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.4
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• pp.434-440
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• 2015

A smart phone is a multimedia device that is a necessity for modern people. It includes a wireless networking system to share information and pictures. However, numerous smart phones are discarded every year, since they have a very fast technology development cycle. This paper presents the development of a telemetry algorithm to measure displacement and strain with a discarded smart phone and digital image correlation methods. To implement the measurement algorithm, the LabVIEW 2010 program development platform was used. In order to verify reliability, an open hole tension test was conducted using a smart phone and a universal test machine. In addition, the measurement results from the smart phone were compared with FEM analysis results.