• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.2
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• pp.26-36
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• 1999

In this paper, the novel scheme of OAM performance management function is proposed to supply the most of network resources and reliable services by processing data having various QoS(quality of service) in the view of cell loss and cell delay of ATM networks Also, the special fields of OAM cell are defined in order to improve correlate control, operation, and management technique between networks which is required to flexibility and precision control as detecting the performance information of the variable networks periodically. The proposed OAM function, the input/output function of cell, and the interface function of the accessory device which is likely to the memory/CPU are designed to ASIC. The designed chip is carried out the back-end simulation using Verilog-XL simulator of Cadence. In result, it is able to performs an accurate control in $2{\mu}s$.

• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.51-59
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• 2003

It is effective to be located in the double nodes in the distributed object replication systems, then object which nodes share is the same contents. The nodes store an access information on their local cache as it access to the system. and then the nodes fetch and use it, when it needed. But with time the coherence Problems will happen because a data carl be updated by other nodes. So keeping the coherence of the system we need a mechanism that we managed the to improve to improve the performance and availability of the system effectively. In this paper to keep coherence in the shared memory condition, we can set the limited parallel performance without the additional cost except the coherence cost using it to keep the object at the proposed adaptive duplication object(ADO) algorithms. Also to minimize the coherence maintenance cost which is the bi99est overhead in the duplication method, we must manage the object effectively for the number of replication and location of the object replica which is the most important points, and then it determines the cos. And that we must study the adaptive duplication object management mechanism which will improve the entire run time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.159-168
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• 2009

In this paper, we present an efficient way of implementing OpenGL ES 1.1 standard for the environments with hardware-supported OpenGL API, such as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it becomes a new three-dimensional graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functionalities and others. Currently, it is the official three dimensional graphics library for Google Android, Apple iPhone, PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a simple memory management scheme to mange the converted data for the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different and thus we used completely new software-implementations. Our final implementation result of OpenGL ES library provides all of over than 200 functions in OpenGL ES 1.1 standard and completely passed its conformance test, to show its compliance with the standard. From the efficiency viewpoint, we measured its execution times for several OpenGL ES-specific application programs and achieved at most 33.147 times improvements, to become the fastest one among the OpenGL ES implementations in the same category.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1260-1268
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• 2005

Ladder Diagram(LD) is the most extensively used among PLC standard language for the design of control system. But LD has the disadvantages for data processing and maintenance. On the other hand, the Sequential Function Chart(SFC) graphic language is very powerful for describing the sequential logic control algorithm. SFC is based on flow chart, so control flow understanding is very easy and divergence can possible improving its ability. In this paper, we propose the efficient management elevator system using the action qualifiers and choice divergence. From the result, we confirm the SFC language reduced program memory capacity and processing time is faster than LD language.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.179-183
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• 2008

연속적이고 무한히 발생되는 데이터 스트림을 관리하는 데이터 스트림 관리시스템(DSMS)은 연속질의를 이용하여 스트림을 처리한다. 연속질의는 질의 별로 독립적인 큐를 유지하기 때문에 질의 개수가 증가함에 따라서 메모리 비용이 증가되며, 잦은 메모리 할당으로 인한 시스템의 성능 저하를 갖는다. 이러한 문제점을 해결하기 위한 기존의 연구로 메모리 풀을 이용한 메모리 관리 기법이 있다. 하지만 페이지의 크기가 고정되어 있기 때문에 각 질의마다 필요로 하는 데이터 스트림의 최적의 크기에 적합하게 할당되지 못하여 메모리를 낭비하는 문제점이 있다. 본 논문에서는 이러한 문제를 해결하기 위해 연속질의 처리를 위한 큐 메모리 관리 기법을 제안한다. 제안기법은 큐 관리 테이블에서 관리하는 각각의 큐 메모리들을 타임스탬프를 가지고 일정한 기간을 주기로 큐 메모리의 사용량을 분석한다. 분석된 큐 메모리들은 이전의 큐 메모리의 사용량과 현재 사용된 큐 메모리의 사용량을 비교함으로써 상한 값과 하한 값을 구함으로써 현재 큐 메모리에서 가지고 있는 사용량을 추가할 것인지, 줄일 것인지를 판단하여, 메모리의 사용량을 최적화 함으로써 시스템의 메모리 가용성을 향상한다. 제안 기법은 성능평가를 통해 메모리의 가용성이 기존의 방식에 비하여 향상된 성능을 보인다.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.