• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.20-29
• /
• 2021

In the Industry 4.0 era, artificial intelligence has attracted considerable interest for learning mass data to improve the accuracy of forecasting and classification. On the other hand, the current method of detecting anomalies relies on traditional statistical methods for a limited amount of data, making it difficult to detect accurate anomalies. Therefore, this paper proposes an artificial intelligence-based anomaly detection methodology to improve the prediction accuracy and identify new data patterns. In particular, data were collected and analyzed from the point of view that sensor data collected at vehicle idle could be used to detect abnormalities. To this end, a sensor was designed to determine the appropriate time length of the data entered into the forecast model, compare the results of idling data with the overall driving data utilization, and make optimal predictions through a combination of various sensor data. In addition, the predictive accuracy of artificial intelligence techniques was presented by comparing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) as the predictive methodologies. According to the analysis, using idle data, using 1.5 times of the data for the idling periods, and using CNN over LSTM showed better prediction results.

• Journal of Industrial Convergence
• /
• v.20 no.6
• /
• pp.117-123
• /
• 2022

Appearance, which is directly related to competitiveness, has become one of the essential self-care for modern people living in the era of the 4th industrial revolution. For the purpose of producing more practical and convenient wigs for suffering customers, data of research subjects were collected through an information search portal site. The trend of new material development of leading wig companies was analyzed. As a result of the study, it was found that many applications for wig attachment and binding technology were applied before 2005, artificial hair-related manufacturing technology for wigs from 2006 to 2013, and functional-related wig technology after 2014. In addition, both H and M companies showed the development trend of new materials for shape memory materials and nanoskins with their own characteristics. We believe that this study will be provided as basic data for the development of functional wigs that can lead to customer satisfaction while providing customers with a comfortable and convenient fit in the wig industry market.

• Journal of IKEEE
• /
• v.26 no.4
• /
• pp.705-709
• /
• 2022

The effect of the annealing temperature of the poly(vinylidene fluoride-trifluoroethylene)(P(VDF-TrFE)) solid electrolyte layer on the electrical properties of the P(VDF-TrFE)-based memristor was analyzed. In morphological analyses, the P(VDF-TrFE) thin film with 200℃ annealing temperature (200P(VDF-TrFE)) was shown to have surface roughness ≈5 times larger and thickness ≈20% smaller than that with 100℃ annealing temperature (100P(VDF-TrFE)). Compared to the 100P(VDF-TrFE) memristor (M100), the set voltage of the 200P(VDF-TrFE) memristor (M200) decreased by ≈50% and the magnitude of its reset voltage increased by ≈30%. Moreover, M200 was found to have better memory retention characteristics than M100. These differences were attributed to relatively strong local electric fields inside M200 compared to M100. This study suggests the importance of the annealing temperature in polymer memristors.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.69-75
• /
• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• Journal of Marine Bioscience and Biotechnology
• /
• v.14 no.1
• /
• pp.9-24
• /
• 2022

Low-molecular weight peptides derived from fish collagen exhibit several bioactivities, including antioxidant, antiwrinkle, antimicrobial, antidiabetic, and antihypertension effects. These peptides are also involved in triglyceride suppression and memory improvement. This study aimed to investigate the optimal processing condition for preparing low-molecular weight peptides from flounder skin, and the properties of the hydrolysate. The optimal processing conditions for peptic hydrolysis were as follows: a ratio of pepsin to dried skin powder of 2% (w/w), pH of 2.0, and a temperature of 50℃. Peptic hydrolysate contains several low-molecular weight peptides below 300 Da. Gly-Pro-Hyp(GPHyp) peptide, a process control index, was detected only in peptic hydrolysate on matrix-assisted laser desorption/ionization-time-of-flight(MALDI-TOF) spectrum. 2,2'-azinobis-(3-3-ethylbenzothiazolline-6- sulfonic acid(ABTS) radical scavenging activity of the peptic hydrolysate was comparable to that of 1 mM ascorbic acid, which was used as a positive control at pH 5.5, whereas collagenase inhibition was five times higher with the peptic hydrolysate than with 1 mM ascorbic acid at pH 7.5. However, the tyrosinase inhibition ability of the peptic hydrolysate was lower than that of arbutin, which was used as a positive control. The antibacterial effect of the peptic hydrolysate against Propionibacterium acne was not observed. These results suggest that the peptic hydrolysate derived from a flounder skin is a promising antiwrinkle agent that can be used in various food and cosmetic products to prevent wrinkles caused by ultraviolet radiations.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.4
• /
• pp.1-8
• /
• 2009

In this paper, we deal an implementation of the software that produces sub-optimal solution of cutting-ironbar planning problem using dynamic programming. Generally, it is required to design an optimization algorithm to accept the practical requirements of cutting ironbar manufacturing. But, this problem is a multiple-sized 1-dimensional cutting stock problem and Linear Programming approaches to get the optimal solution is difficult to be applied due to the problem of explosive computation and memory limitation. In order to overcome this problem, we reform the problem for applying Dynamic Programming and propose a cutting-ironbar planning algorithm searching the sub-optimal solution in the space of fixed amount of combinated columns by using heuristics. Then, we design a graphic user interfaces and screen displays to be operated conveniently in the industry workplace and implement the software using open-source GUI library toolkit, GTK+.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.699-706
• /
• 2023

Recently, the 4 generation industry revolution is developed with advanced and combined with a variety of new technologies. Conventional healthcare system is applied with IoT application. It provides many advantages with mobility and swift data transfers to patient and doctor. In despite of these kinds of advantages, it occurred security issues between basic devices and protocols in their applications. Especially, internet of things have restricted and limited resources such as small memory capacity, low capability of computing power, etc. Therefore, we can not utilize conventional mechanism. In this paper, we analyzed attacks and vulnerability in terms of security issues. To analyze security structure, features, demands and requirements, we solve the methods to be reduced security issues.

• Korea Trade Review
• /
• v.48 no.2
• /
• pp.27-43
• /
• 2023

The maritime industry is playing an increasingly vital part in global economic expansion. Specifically, the Baltic Dry Index is highly correlated with global commodity prices. Hence, the importance of BDI prediction research increases. But, since the global situation has become more volatile, it has become methodologically more difficult to predict the BDI accurately. This paper proposes an integrated machine-learning strategy for accurately forecasting BDI trends. This study combines the benefits of a convolutional neural network (CNN) and long short-term memory neural network (LSTM) for research on prediction. We collected daily BDI data for over 27 years for model fitting. The research findings indicate that CNN successfully extracts BDI data features. On this basis, LSTM predicts BDI accurately. Model R2 attains 94.7 percent. Our research offers a novel, machine-learning-integrated approach to the field of shipping economic indicators research. In addition, this study provides a foundation for risk management decision-making in the fields of shipping institutions and financial investment.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.4
• /
• pp.69-78
• /
• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• The KIPS Transactions:PartA
• /
• v.19A no.1
• /
• pp.51-60
• /
• 2012

IC(Integrated circuits)card, generally be named smard card, embedded MPU(Micro Processor Unit) of small-size, memory, EEPROM, Card Operating System(COS) and security algorithm. The IC card is used in almost all industry such as a finance(credit, bank, stock etc.), a traffic, a communication, a medical, a electronic passport, a membership management and etc. Recently, a application field of IC card is on the increase by method for payments of T-commerce, as T-commerce is becoming a new growth engine of the broadcating industry by trend of broadcasting and telecommunication convergence, smart mechanization of TV. For example, we can pay in IC credit card(or IC cash card) on T-Commerce. or we can be provided TV banking service in IC cash card such as ATM. However, so far, T-commerce payment services have weakness in security such as storage and disclosure of card information as well as dropping sharply about custom ease because of taking advantage of card information input method using remote control. To solve this problem, This paper developed processing payment module for implementing TV electronic payment system using IC credit card payment standard, EMV.