• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.761-766
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• 2005

This paper presents a dynamic precedence queue mechanism to resolve unexpected transmission delay of a lower priority transaction in a CAN based system which keeps a fixed priority in data transactions. The mechanism is implemented in the upper sub-layer of the data link layer (DLL), which is fully compatible with the original medium access control layer protocol of CAN. Thus the mechanism can be implemented dynamically while the data transactions are going on without any hardware modification. The CAN protocol was originally developed to be used in the automotive industry and it was recently applied for a broader class of automated factories. Even though CAN is able to satisfy most of real-time requirements found in automated environments, it is not to enforce either a fair subdivision of the network bandwidth among the stations or a satisfactory distribution of the access delays in message transmissions. The proposed solution provides a superset of the CAN logical link layer control, which can coexist with the older CAN applications. Through the real experiments, effectiveness of the proposed mechanism is verified.

• Clean Technology
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• v.7 no.2
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• pp.119-126
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• 2001

The prediction of ozone formation was studied using the neural network and the stochastic method. Parameter estimation method and artificial neural network(ANN) method were employed in the stochastic scheme. In the parameter estimation method, extended least squares(ELS) method and recursive maximum likelihood(RML) were used to achieve the real time parameter estimation. Autoregressive moving average model with external input(ARMAX) was used as the ozone formation model for the parameter estimation method. ANN with 3 layers was also tested to predict the ozone formation. To demonstrate the performance of the ozone formation prediction schemes used in this work, the prediction results of ozone formation were compared with the real data. From the comparison it was found that the prediction schemes based on the parameter estimation method and ANN method show an acceptable accuracy with limited prediction horizon.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1849-1876
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• 2022

Wireless Sensor Network (WSN) is considered as an integral part of the Internet of Things (IoT) for collecting real-time data from the site having many applications in industry 4.0 and smart cities. The task of nodes is to sense the environment and send the relevant information over the internet. Though this task seems very straightforward but it is vulnerable to certain issues like energy consumption, delay, throughput, etc. To efficiently address these issues, this work develops a cross-layer model for the optimization between MAC and the Network layer of the OSI model for WSN. A high value of duty cycle for nodes is selected to control the delay and further enhances data transmission reliability. A node measurement prediction system based on the Kalman filter has been introduced, which uses the constraint based on covariance value to decide the scheduling scheme of the nodes. The concept of duty cycle for node scheduling is employed with a greedy data forwarding scheme. The proposed Duty Cycle-based Greedy Routing (DCGR) scheme aims to minimize the hop count, thereby mitigating the energy consumption rate. The proposed algorithm is tested using a real-world wastewater treatment dataset. The proposed method marks an 87.5% increase in the energy efficiency and reduction in the network latency by 61% when validated with other similar pre-existing schemes.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• 한국정보통신설비학회:학술대회논문집
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• 2003.08a
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• pp.253-257
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• 2003

CDMA 방식의 디지털 이동통신 망은 기존의 2G 방식에서 IX, IxEV-DO을 거쳐 WCDMA로 비약적으로 발전하고 있으며, 이에 따라 무선망 운용 및 최적화 방법도 진화해가고 있다. 운용자들이 Field Tool을 사용하여 직접 Field 데이터를 측정, 분석하고 조치하는 방식이 가장 기본적인 방법이라면, Field 데이터와 Network 데이터를 함께 수집하여 분석하는, 보다. 발전된 방법도 사용되고 있다. 그러나, 이러한 방법도 여러 Tool에서 데이터를 off-line으로 수집한 후 분석 작업을 수동으로 반복 수행해야하는 번거로움이 있어, 실시간 on-line 무선망 최적화 시스템을 통한 체계적이고 과학적인 운용 방법을 생각해 볼 수 있다. 우선, 타 운용 Tool 들과의 on-line 연동으로 중앙 집중적 데이터베이스를 구축하여, 무선망에 관련된 모든 데이터에 대한 통합적인 관리가 필요하다. 이 데이터베이스를 이용하여, 실시간으로 무선망 성능 및 효율 저하 원인 분석을 실시하며, 분석된 결과는 기지국의 상태 및 문제점 도출에서부터 최종 처방까지 제시해준다. 본 논문에서는 이러한 솔루션을 구축하기 위한 다양한 네트웍 데이터 연동(성능, 장애, 구성, RF, 실측 데이터 등), 주요 KPI (Key Performance Indicator) 모니터링, 통계적 분석, 무선망 분석 등에 대해 고찰해본다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.532-543
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• 2003

In this paper, we proposed structure of a reversible discrete-time cellular neural network (DTCNN) for labeling digital images to protect copylight. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary pseudo-random images sequences. We presented some, output examples of different kinds of reversible DTCNNs to show their complex behaviors. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Since the reversibility of a reversible DTCNN, the same reversible DTCNN can recover the copyright label from a labeled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. Computer simulation results are presented.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.49-56
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• 2000

Instrumental observation of earth quakes in KIGAM was first attempted in the earty 1980`s by using 6 portable seismographs in the vicinity of Yang-San Faults. Now twenty-four permanent stations, which are equipped with short-period or broad-band seismometer, are included in seismic research network in KIGAM, including KSRS array station in Wonju which is consisted of 26 bore-hole stations. The seismic network of KIGAM is also linked to that of KEPRI(Korea Electric Power Research Institute)which is consisted of eight stations installed within and around the nuclear power plants. Owing to real-time data acquisition by telemetry, it became feasible to automatically locate hypocenters of the local events within fifteen minutes by computer data processing system, named KEMS(Korea Earthquake Monitoring System). Results of the hypocenter determination, together with observational data, are compiled and stored in the data base system. And they are published via web site whose URL is http://quake.kigam.re.kr KIGAM is also running t재 permanent geomagnetic stations installed in Daejun and Kyungju. The observed geomagnetic data are transmitted to Earthquake Research Centre in KIGAM by seismic network and compiled for the purpose of earthquake prediction research and other basic geophysical research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.232-234
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• 2012

NMEA2000 international standards was developed for real-time communication among sensors and devices in ships. This paper describes analysis of NMEA2000 network protocols and implementation of a gateway for wireless data services in NMEA2000 networks. For scalability of the network, developed gateway didn't participate in NMEA2000 network but simply relayed data between wireless devices and the network.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1601-1604
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• 1997

The most important topic in research of mobile robot is path planning in order to avoid with obstacle. In this study the path planning algorithm using a neural network model is proposed. The inputs of neural network are range data which are acquired form laser range finderm and weights are based on difference with goal direction. The thresholds are made by consdiering the marginal distance between mobile robot and obstacle. Consequently the outputs are obtained by multiplying input and weight. The obtained heading directiion enables the mobile robot to approach the goal, without any collision with obstacles around. The effectiveness of the this method of real-time navigation of a mobile robot is estimated by computer simulation in complex environment.