• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.710-715
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• 2006

With recent advances in device fabrication technology, economical deployment of large scale sensor networks, a design of pervasive monitoring and control system has been made possible. In this paper, we present a new algorithm for one of the most likely applications for sensor networks; tracking moving targets. The proposed algorithm uses a cooperations between the sensor nodes which detect moving objects. Therefore, the proposed scheme is robust against prediction failures which may result in temporary loss of the target. Using simulations we show that tile proposed moving object tracking algorithm is capable of accurately tracking targets with random movement patterns.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.3
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• pp.1-12
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• 2022

With the rapid development of artificial intelligence technology that gives existing sensors functions similar to human intelligence is drawing attention. Previously, researches were mainly focused on an improvement of fundamental performance indicators as sensors. However, recently, attempts to combine artificial intelligence such as classification and prediction with sensors have been explored. Based on this, intelligent sensor research has been actively reported in almost all kinds of sensing fields such as disease detection, motion detection, and gas sensor. In this paper, we introduce the basic concepts, types, and driving mechanisms of artificial intelligence and review some examples of its use.

• Annual Conference of KIPS
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• 2009.11a
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• pp.547-548
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• 2009

The development of USN(Ubiquitous Sensor Network) technology is creating numerous application areas. Although a network configuration with fixed sensors was the norm in the past, the coexistence of mobile and fixed sensor nodes is a new trend. Fixed sensor networks focused on the energy efficiency of nodes, but the latest studies consider guaranteeing the mobility of nodes and maintaining their connectivity, while remaining energy efficient at the same time. This paper proposes a routing protocol for a mobile ad-hoc sensor network that improves the mobility, connectivity and energy efficiency of nodes while allowing for the management and maintenance of a large number of nodes even in a complex communication environment where mobile and fixed nodes coexist. An algorithm for multi-hop multi-paths, a technique for topology reconfiguration by node movement prediction and vibration sensors, path setting for a large number of nodes, and efficient data transfer technology have been introduced to implement the modified LEAHC-AOMDV protocol. Furthermore, the excellence of this protocol was verified through a comparative experiment with the conventional LEACH protocol.

• Journal of Korean Society of Rural Planning
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• v.21 no.3
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• pp.171-179
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• 2015

The objectives of this research have been focussed on 1) developing prediction techniques for the flash flood and landslide based on rainfall prediction data in agricultural area and 2) developing an integrated forecasting system for the abrupt disasters using USN based real-time disaster sensing techniques. This study contains following steps to achieve the objective; 1) selecting rainfall prediction data, 2) constructing prediction techniques for flash flood and landslide, 3) developing USN and communication network protocol for detecting the abrupt disaster suitable for rural area, & 4) developing mobile application and SMS based early warning service system for local resident and tourist. Local prediction model (LDAPS, UM1.5km) supported by Korean meteorological administration was used for the rainfall prediction by considering spatial and temporal resolution. NRCS TR-20 and infinite slope stability analysis model were used to predict flash flood and landslide. There are limitations in terms of communication distance and cost using Zigbee and CDMA which have been used for existing disaster sensors. Rural suitable sensor-network module for water level and tilting gauge and gateway based on proprietary RF network were developed by consideration of low-cost, low-power, and long-distance for communication suitable for rural condition. SMS & mobile application forecasting & alarming system for local resident and tourist was set up for minimizing damage on the critical regions for abrupt disaster. The developed H/W & S/W for integrated abrupt disaster forecasting & alarming system was verified by field application.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.944-952
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• 2001

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.81-87
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• 2007

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. However, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis and artificial neural network were used as the research methods. And, the results of two prediction methods were compared and analyzed.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1180-1188
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• 2008

As many systems depend on electronics, concern for fault tolerance is growing rapidly in the safety critical system such as intelligent vehicle. In order to make system fault tolerant, there has been a body of research mainly from aerospace field including predictive hybrid redundancy by Lee. Although the predictive hybrid redundancy has the fault tolerant mechanism to satisfy the fault tolerant requirement of safety crucial system such as x-by-wire system, it suffers form the variability of prediction performance according to the input feature of system. As an alternative to the prediction method of predictive hybrid redundancy for robust fault tolerant, Kalman prediction has attracted some attention because of its well-known and often-used with its structure called Kalman hybrid redundancy. In addition, several numerical simulation results are given where the Kalman hybrid redundancy outperforms with predictive smoothing voter.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.87-93
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• 2012

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activities protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks.