• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.7
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• pp.1169-1177
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• 1994

The function estimation characteristics of neural networks can be used sensor signal estimation of the nuclear power plants. In case of applying the neural network to the signal estimation of redundant sensors, it is an important problem that the redundant sensor signals used as the input signals of neural network should be validated. In this paper, we simplify the conventional parity space method in order to input the validated signal to the neural network and lso propose the sensor signal validation method, which estimates the reliable sensor output combining the neural network with the simplified parity space method. The acceptability of the proposed process variable estimation method is demonstrated by using the simulation data in safety injection accident of the nuclear power plant.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.27-33
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• 2009

The emergences of GeoSensor and researches about GIS have promoted many researches of u-GIS. The disaster application coupled in the u-GIS can apply to monitor accident area and to prevent spread of accident. The application needs the u-GIS DSMS technique to acquire, to process GeoSensor data and to integrate them with GIS data. The u-GIS DSMS must process big and large-volume data stream such as spatial data and multimedia data. Due to the feature of the data stream, in u-GIS DSMS, query processing can be delayed. Moreover, as increasing the input rate of data in the area generating events, the network traffic is increased. To solve this problem, in this paper we describe TRIGGER ACTION clause in CQ on the u-GIS DSMS environment and proposes data source management. Data source weight table controls GES information and incoming data rate. It controls incoming data rate as increasing weight at GES of disaster area. Consequently, it can contribute query processing rate and accuracy

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.28-40
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• 2022

Although precise temperature control in the heat treatment process is a key factor in process reliability, there are many cases where there is no separate heat source control optimization system in the field. To solve this problem, the program monitors the temperature data according to the heat source change through sensor communication in a recursive method based on multiple variables that affect the process, and the target heat source value and the actual heat treatment heat source to match the internal air temperature and material temperature. A control optimization system was constructed. Through this study, the error rate between the target temperature and the atmosphere (material surface) temperature of around 10.7% with the existing heat source control method was improved to an improved result of around 0.1% using a process optimization algorithm and system.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.135-145
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• 2011

These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through the some experimental scenarios. For the this, we use a sensor data generator implemented by ourselves.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.828-840
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• 2014

There is a new evolution in technological advancement taking place called the Internet of Things (IoT). The IoT enables physical world objects in our surroundings to be connected to the Internet. For this idea to come to life, two architectures are required: the Sensing Entity in the environment which collects data and connects to the cloud and the Cloud Service that hosts the data. In particular, the combination of wireless sensor network for sensing and cloud computing for managing sensor data is becoming a popular intervention for the IoT era. The pharmaceutical cold chain requires controlled environmental conditions for the sensitive products in order for them to maintain their potency and fit for consumption. The monitoring of distribution process is the only assurance that a process has been successfully validated. The distribution process is so critical that anomaly at any point will result in the process being no longer valid. Taking the cold chain monitoring to IoT and using its benefits and power will result in better management and product handling in the cold chain. In this paper, Arduino based wireless sensor network for storage and logistics (land and sea) is presented and integrated with Xively cloud service to offer a real-time and innovative solution for pharmaceutical cold chain monitoring.

• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.72-85
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• 2002

Advances in computer and sensor technology have made it possible to obtain superlarge manufacturing process data in real time, letting us extract meaningful information from these superlarge data sets. We propose a systematic data analysis procedure which field engineers can apply easily to manufacture quality products. The procedure consists of data cleaning and data analysis stages. Data cleaning stage is to construct a database suitable for statistical analysis from the original superlarge manufacturing process data. In the data analysis stage, we suggest a graphical easy-to-implement approach to extract practical information from the cleaned database. This study will help manufacturing companies to achieve six sigma quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4B
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• pp.303-310
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• 2006

In sensor network environments, a sensor node has a limited power because of their resource constraints. Therefore it is important to efficiently use its power in sensor networks. Power consumption of sensor node is closely related to its amount of transmission data. So, we need to reduce the transmission data in order to minimize the power consumption. And sensor networks are inherently unreliable because radio transmission can fail, node can move, and so on. In this paper, we propose the reliable data aggregation protocol in order to these problems. This protocol performs the routing and the query inserting process at the same time to minimize the packet loss caused by network changes. And, this protocol removes the unnecessary routing caused by the periodic routing without query. Additionally, we suggest the countermeasure algorithm against the frequent errors in sensor networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.434-437
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• 2016

In this paper, we consider a measurement allocation problem for gathering reliable data from a spatially correlated sensor field. We allocate the probability of each sensor's being measured considering its marginal contribution in entire data gathering; higher measurement probability is given to a sensor that gives higher reilable data. First we establish a correlation model considering limit in each sensor's transmission power, noise in the process of measurement and transmission, and attenutations in wireless channel. Then we evaluate the reliability of gathered data by estimating distortion error in sink node. We model the measurement allocation problem in spatially correlated sensor field into a cooperative game, and quantifiy each sensor's marginal contribution using Shapley Value. Then, the probability of each sensor's being measured is given in proportion to the Shapley Value.

• Journal of IKEEE
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• v.23 no.3
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• pp.858-864
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• 2019

A clock system is proposed to eliminate data loss due to frequency difference between sensor nodes in a sensor utility network. The proposed clock system for each sensor node consists of a bust clock-data recovery (CDR) circuit, a digital phase-locked loop outputting a 32-phase clock, and a digital frequency synthesizer using a programmable open-loop fractional divider. A CMOS oscillator using an active inductor is used instead of a burst CDR circuit for the first sensor node. The proposed clock system is designed by using a 65 nm CMOS process with a 1.2 V supply voltage. When the frequency error between the sensor nodes is 1%, the proposed burst CDR has a time jitter of only 4.95 ns with a frequency multiplied by 64 for a data rate of 5 Mbps as the reference clock. Furthermore, the frequency change of the designed digital frequency synthesizer is performed within one period of the output clock in the frequency range of 100 kHz to 320 MHz.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1137-1140
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• 2005

Automatic welding has been used frequently on pipeline projects. The productivity and reliability are most essential features of the automatic welding system. The mechanized GMAW process is the most widely used welding process and the carriage and band system is most effective welding system for pipeline laying. This application-oriented paper introduces new automatic welding equipment for pipeline construction. It is based on cutting-edge design and practical welding physics to minimize downtime. This paper also describes the control system which was designed and implemented for new automatic welding equipment. The system has the self diagnostic function which facilitates maintenance and repairs, and also has the network function via which the welding task data can be transmitted and the welding process data can be monitored. The laser vision sensor was designed for narrow welding groove in order to implement higher accuracy of seam tracking and fully automatic operation.