• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1367-1374
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• 2022

In this paper, we propose an automatic cosmetic foundation recommendation system that suggests a good foundation product based on the user's skin color. The proposed system receives and preprocesses user images and detects skin color with OpenCV and machine learning algorithms. The system then compares the performance of the training model using XGBoost, Gradient Boost, Random Forest, and Adaptive Boost (AdaBoost), based on 550 datasets collected as essential bestsellers in the United States. Based on the comparison results, this paper implements a recommendation system using the highest performing machine learning model. As a result of the experiment, our system can effectively recommend a suitable skin color foundation. Thus, our system model is 98% accurate. Furthermore, our system can reduce the selection trials of foundations against the user's skin color. It can also save time in selecting foundations.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.21-32
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• 2023

The design of complex man-made systems mostly involves a conceptual modeling phase; therefore, it is important to ensure an appropriate analysis method for these models. A key concept for such analysis is the development of a diagramming technique (e.g., UML) because diagrams can describe entities and processes and emphasize important aspects of the systems being described. The analysis also includes an examination of ontological concepts such as states and events, which are used as a basis for the modeling process. Studying fundamental concepts allows us to understand more deeply the relationship between these concepts and modeling frameworks. In this paper, we critically analyze the classic definition of a state utilizing the Thinging machine (TM) model. States in state machine diagrams are considered the appropriate basis for modeling system behavioral aspects. Despite its wide application in hardware design, the integration of a state machine model into a software system's modeling requirements increased the difficulty of graphical representation (e.g., integration between structural and behavioral diagrams). To understand such a problem, in this paper, we project (create an equivalent representation of) states in TM machines. As a case study, we re-modeled a state machine of an assembly line system in a TM. Additionally, we added possible triggers (transitions) of the given states to the TM representation. The outcome is a complicated picture of assembly line behavior. Therefore, as an alternative solution, we re-modeled the assembly line based solely on the TM. This new model presents a clear contrast between state-based modeling of assembly line behavior and the TM approach. The TM modeling seems more systematic than its counterpart, the state machine, and its notions are well defined. In a TM, states are just compound events. A model of a more complex system than the one in the assembly line has strengthened such a conclusion.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of Fashion Business
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• v.1 no.2
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• pp.46-54
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• 1997

The purpose of this study is to understand women's wear manufacturing industries. First, the study was to investigate the present production systems and how much the automatic facility are by comparing them. This study enhanced more efficient, stable, and suitable work line. This intern will direct the way in which automatic facilities will be created. Second, through this study on the general character of the inspectors, the ratio of impaired goods, and the reasons for unsatisfactory goods, I intended to find out a way to decrease the impaired goods and to produce competitive and high quality goods. The results of the survey can be summarized as follows; 1. The result of the research on the automatic industrial facilities shows that the majority of the factories (77.4%) are 40% below the automatic facility rate. The reasons for this according to order are that was a deficit in money, no reason for expensive machines, and lack of the technique and the number of workers required to handle the machines. 2. At this time, the most required equipments are shown according to its importance; automatic sewing machine, automatic cutting machine, automatic spreading machine, and finishing & pressing machine. So in the women's wear manufacturing industries, they think that they need more automatic cutting machine, automatic spreading machine in the cutting field rather than high price automatic machine in the sewing field such as pattern former, pocket welting, automatic sleeve connecting machine and automatic label connecting machine. 3. The result of the research in the goods quality shows that the average impaired rate is 12.7% at the first inspection. In addition the average rate for complete impaired rate is 1.52%. The line system shows that it has a impaired rate that is double the rate of the pair system. Because of this, the industries plan to combine the line system and pair system to create an improved and suitable production system which can boost the quality and productivity of the goods. 4. The fabric is the main point of the impaired goods. The factors of the impaired goods in manufacturing are the lack of mental abilities of the worker, impaired fabrics and a lack of cooperation in the working system. Furthermore, there is a lack of technique for new material. 5. To prohibit impaired goods in manufacturing, there need to be a way to educate the workers and to enhance the workers' mind on the productive goods. Also there need to increase in the investments of automatic production machines. Finally there need to be a standardized working line. Therefore, there need to be an improvement on the management of the production of goods, the development of technique and an increase in the education for the workers, with this there will be a decrease in impaired goods, and an increase in better quality of goods to enforce the domestic apparel industries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.241-248
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• 2011

In this paper we proposed and implemented a diagnosis system to control nuclear fuel handling machine. The proposed system consists of data acquisition system, diagnosis algorithm and faults simulator. Since the test on real operation of the fuel handling machine is impossible, we evaluated the proposed system by diagnosis experiments using the faults simulator, with which test signals on abnormal states of the bearing ball and the inner race of the bearing are generated. The experiments showed that resulting diagnosis analysis are consistent with the theoretical expectations.

• Journal of Mechanical Science and Technology
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• v.18 no.12
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• pp.2209-2215
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• 2004

In the present research work, an automated machine vision system and a new algorithm to interpret the inspection data has been developed. In the past, the control of tolerance of front-chassis-module was done manually. In the present work a machine vision system and required algorithm was developed to carryout dimensional evaluation automatically. The present system is used to verify whether the automobile front-chassis-module is within the tolerance limit or not. The directional ability parameters related with front-chassis-module such as camber, caster, toe and king-pin angle are also determined using the present algorithm. The above mentioned parameters are evaluated by the pose of interlinks in the assembly of an automobile front-chassis-module. The location of ball-joint center is important factor to determine these parameters. A method to determine the location of ball-joint center using geometric features is also suggested in this paper. In the present work a 3-D best fitting method is used for determining the relationship between nominal design coordinate system and the corresponding feature coordinate system.

• 한국금형공학회:학술대회논문집
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• 2008.06a
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• pp.61-66
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• 2008

The hydraulic system have been used much in a heavy machine which high power source is desired. In the case of the heavy press machine and the injection molding machine, the use of the hydraulic power is essential especially for increasing productivity and getting the good products. Because the hydraulic circuit is very complex and the system parameters are uncertain, the development of the simulation model for hydraulic system is not easy in the heavy machine. In this case, Many researchers have used a commercial program for analysis and development in a major field of study. In this paper, the aim is to develop the simulation model of the hydraulic system with various commercial program for trajectory tracking. And adaptive control method is applied to the simulation model for the trajectory tracking of a cylinder motion. Load on the cylinder is modeled in ADAMS program, the hydraulic circuit including pump, spool valve and cylinder is modeled in AMESim program and a controller is designed in MatLab/simulink program. The suggested model is applied for the tracking of a cylinder motion, and through computer simulation, its trajectory tracking performance is illustrated.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.8
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• pp.400-407
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• 1999

This paper treated the design, manufacture and the performance characteristics under each mode of high speed motor/generator for an electro-mechanical battery(EMB). This machine is employed as an integral part of a flywheel energy storage system(FESS), i.e., a modular flywheel system to be used as a device for storing electrical or mechanical energy. In this machine, the magnetic field system is constructed by using special magnet array, dipole Halbach array with 16 permanent magnet segments and the armature is composed of a plastic bobbin and multi-phase windings with Litz wire. The magnet array produces a highly uniform dipole field without back iron. The motor/generator is 3-phase machine in which the dipole Halbach array surrounding the winding is rotating. Since there are no iron laminations, this field system offers some unique advantages for the simplicity of the design and the theoretical prediction of characteristics of a high speed electric machine. This paper describes the results obtained when EMB system was tested in the laboratory.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.