• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.274-282
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• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.551-552
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• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.157-161
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• 1996

In the geometric accuracy, most of studies have been concentrated on the analysis of the geometric error, or a control path of grinding using the value of measured geometric error. In this paper, by using the value of measured motor current through hall sensor, detection of the geometric error have been accomplished, and in-process control path of grinding for improvement geometric accuracy, too.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.7
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• pp.653-660
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• 2015

This paper describes the design of the force measuring system for an industrial robot's deburring work. The force measuring system is composed of a three-axis force sensor, a measuring device, a housing and a cover. The three-axis force sensor can detect x-direction force, y-direction force and z-direction force at the same time. The measuring device is designed using DSP(Digital Signal Processor), and have a RS-232 and a RS-485 communication port for sending force data to PC or other controller. As a result of test, the repeatability error and the non-lineality error of the three-axis force sensor are less than 0.03%, and the interference error of the sensor is less than 0.95%. It is thought that the force measuring system can be used for an industrial robot's deburring work.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.265-273
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• 2007

This paper expands an analytical performance model of 802.11 to accurately estimate throughput and energy demand of 802.11-based wireless sensor network (WSN) when sensor nodes employ Reed-Solomon (RS) codes, one of block forward error correction (FEC) techniques. This model evaluates these two metrics as a function of the channel bit error rate (BER) and the RS symbol size. Since the basic recovery unit of RS codes is a symbol not a bit, the symbol size affects the WSN performance even if each packet carries the same amount of FEC check bits. The larger size is more effective to recover long-lasting error bursts although it increases the computational complexity of encoding and decoding RS codes. For applying the extended model to WSNs, this paper collects traffic traces from a WSN consisting of two TIP50CM sensor nodes and measures its energy consumption for processing RS codes. Based on traces, it approximates WSN channels with Gilbert models. The computational analyses confirm that the adoption of RS codes in 802.11 significantly improves its throughput and energy efficiency of WSNs with a high BER. They also predict that the choice of an appropriate RS symbol size causes a lot of difference in throughput and power waste over short-term durations while the symbol size rarely affects the long-term average of these metrics.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.13-21
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• 2022

Recently, in wireless video sensor networks(WVSN), various schemes for efficient video data transmission have been studied. In this paper, a layer based cooperative relaying(LCR) algorithm is proposed for minimizing scalable video transmission distortion from packet loss in WVSN. The proposed LCR algorithm consists of two modules. In the first step, a parameter based error propagation metric is proposed to predict the effect of each scalable layer on video quality degradation at low complexity. In the second step, a layer-based cooperative relay algorithm is proposed to minimize distortion due to packet loss using the proposed error propagation metric and channel information of the video sensor node and relay node. In the experiment, the proposed algorithm showed that the improvement of peak signal-to-noise ratio (PSNR) in various channel environments, compared to the previous algorithm(Energy based Cooperative Relaying, ECR) without considering the metric of error propagation.The proposed LCR algorithm minimizes video quality degradation from packet loss using both the channel information of relaying node and the amount of layer based error propagation in scalable video.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1254-1258
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• 2003

One of the major factors that effect sensor performance is analog noise that added in a sensor signal such as voltage. In multi-axis force sensors, error sources may be classified mainly in two groups. One is structural error due to inaccuracy of sensor body. The other error source is noise signals existing in the sensed information. This paper presents a brief review about the principle of multi-axis force sensors, and then proposes a method that can reduce the effect of noise signal to sensor performance. The method is to convert analog voltage signal to digital numbers near sensor body and then to read these digital signals and conduct signal processing in the computer. By this way, we can eliminate a bad effect of electromagnetic wave emitted from computer and of 60 Hz noise emitted from AC source. The proposed method is investigated through experimental demonstration. The experimental results show that it improves S/N ratio of the sensor about 40 times in our experimental setup.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.65-72
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• 2018

Since data-driven building technologies have been widely applied to building energy systems, the accuracy of building sensors has more impacts on the building performance and system performance analysis. Various building sensors, however, can have typical errors including a random error (noise) and a systematic error (bias). The systematic error is indicated by the difference between the mean of measurements and their true value. It may occur due to the sensor's physical condition, measured phenomena, working environments inside the systems. Unfortunately, a conventional calibration method has limitations in calibrating the systematic errors because of the difference between working environments and calibration conditions. In such situations, a novel sensor calibration method is needed to handle various sensor errors, especially for systematic errors, in building energy systems having various thermodynamic environments. This study proposes a building sensor calibration method named Virtual In-situ Calibration (VIC) and shows how it is applied into a real building system and how it solves the sensor errors.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1158-1168
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• 2020

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.405-423
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• 2022

The distance vector-hop (DV-Hop) is one of the emblematic algorithms that use node connectivity for locating, which often accompanies by a large positioning error. To reduce positioning error, the bio-inspired algorithm and weight optimization model are introduced to address positioning. Most scholars argue that the weight value decreases as the hop counts increases. However, this point of view ignores the intrinsic relationship between the error and weight. To address this issue, this paper constructs the relationship model between error and hop counts based on actual communication characteristics of sensor nodes in wireless sensor network. Additionally, we prove that the error converges to 1/6CR when the hop count increase and tendency to infinity. Finally, this paper presents a modified error-oriented weight positioning model, and implements it with genetic algorithm. The experimental results demonstrate excellent robustness and error removal.