• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3889-3903
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• 2017

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1756-1768
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• 2021

The containment filtered venting system (CFVS) filters the atmosphere of the containment building and discharges a part of it to the outside environment to prevent containment overpressure during severe accidents. The Korean CFVS has a tank that filters fission products from the containment atmosphere by pool scrubbing, which is the primary decontamination process; however, prediction of its performance has been done based on researches conducted under mild conditions than those of severe accidents. Bubble behavior in a pool is a key parameter of pool scrubbing. Therefore, the bubble behavior in the pool was analyzed under various injection flow rates observed at the venturi nozzles used in the Korean CFVS using a wire-mesh sensor. Based on the experimental results, void fraction model was modified using the existing correlation, and a new bubble size prediction model was developed. The modified void fraction model agreed well with the obtained experimental data. However, the newly developed bubble size prediction model showed different results to those established in previous studies because the venturi nozzle diameter considered in this study was larger than those in previous studies. Therefore, this is the first model that reflects actual design of a venturi scrubbing nozzle.

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1011-1019
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• 2019

In this paper, we propose a system that can more precisely identify and monitor the position of the tool used in the assembling workplace such as automobile production. The proposed positioning monitoring system is a combination of UWB communication module and MEMS IMU sensor. Since UWB does not need modulation and demodulation function and has low power density, UWB is widely used in indoor positioning field. However, it may cause positioning error due to errors in RF transmission and reception process, which may cause positioning accuracy. Therefore, in this paper, we propose an algorithm that uses IMU as an auxiliary means to compensate for errors that may occur in positioning using only UWB. The tag and anchor of UWB module measure the transmission / reception time by transmitting signals to each other and then estimate the distance between tag and anchor. The MEMS IMU sensor serves to provide positioning calibration information. The tag, which is a mobile node and attached to a moving tool, measures the three-dimensional position of the tool and transfers the coordinate data to the anchor. Thus, it is possible to confirm whether or not the specific tool is properly used according to the prescribed regulations.

• International Journal of Contents
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• v.15 no.2
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• pp.75-78
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• 2019

The Integration technique of combining the measurement method with the fine precision of the sensor collecting the satellite-based information to determine the displacement space is available to a variety of diagnostic information. The measurement method by a GNSS with the sensors is needed since there will always be occasional occurrence of natural disasters caused by various environmental factors and the surroundings. Such attempts carried out nationally by distributed torsional displacement of the terrain and facilities. The combination of the various positioning analysis of mm-class for the facility of main area observed is required constantly in real time information of the USN/IoT Smart sensors and should be able to utilize such information as a precisely fine positioning information for the precisely fine displacement of the semi-permanent main facilities. In this study, for the installation of the receiving system, the USN/IoT base line positioning are easily accessible for the target bridges. Transmitting hourly from the received data is also executed in real time using the wireless Wi-Fi/Bluetooth bridges and related facilities to automatically process a fine position displacement. The results obtained from this method can be analyzed by real-time monitoring for a large structure or facilities for disaster prevention.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.117-124
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• 2023

In the machine vision inspection equipment, diameter measurement is important process in inspection of cylindrical object. However, machine vision inspection equipment requires complex algorithm processing such as camera distortion correction and perspective distortion correction, and the increase in processing time and cost required for precise diameter measurement. In this paper, we proposed the algorithm for diameter measurement of cylindrical object using the laser displacement sensor. In order to fit circle for given four input outer points, grid search algorithms using root-mean-square error and mean-absolute error are applied and compared. To solve the limitations of the grid search algorithm, we finally apply the singular-value-decomposition based circle fitting algorithm. In order to compare the performance of the algorithms, we generated the pseudo data of the outer points of the cylindrical object and applied each algorithm. As a result of the experiment, the grid search using root-mean-square error confirmed stable measurement results, but it was confirmed that real-time processing was difficult as the execution time was 10.8059 second. The execution time of mean-absolute error algorithm was greatly improved as 0.3639 second, but there was no weight according to the distance, so the result of algorithm is abnormal. On the other hand, the singular-value-decomposition method was not affected by the grid and could not only obtain precise detection results, but also confirmed a very good execution time of 0.6 millisecond.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.114-114
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• 2020

Water contamination in a water distribution network (WDN) is harmful since it directly induces the consumer's health problem and suspends water service in a wide area. Actions need to be taken rapidly to countermeasure a contamination event. A contaminant source ident ification (CSI) is an important initial step to mitigate the harmful event. Here, a CSI approach focused on determining the contaminant intrusion possible location and time (PLoT) is introduced. One of the methods to discover the PLoT is an inverse calculation to connect all the paths leading to the report specification of a sensor. A filtering procedure is then applied to narrow down the PLoT using the results from individual sensors. First, we spatially reduce the suspect intrusion points by locating the highly suspicious nodes that have similar intrusion time. Then, we narrow the possible intrusion time by matching the suspicious intrusion time to the reported information. Finally, a likelihood-score is estimated for each suspect. Another important aspect that needs to be considered in CSI is that there are inherent uncertainties, such as the variations in user demand and inaccuracy of sensor data. The uncertainties can lead to overlooking the real intrusion point and time. To reflect the uncertainties in the CSI process, the Monte-Carlo Simulation (MCS) is conducted to explore the ranges of PLoT. By analyzing all the accumulated scores through the random sets, a spread of contaminant intrusion PLoT can then be identified in the network.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.179-184
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• 2024

In the car industry, welding is a fundamental linking technique used for joining components, such as steel, molds, and automobile parts. However, accurate inspection is required to test the reliability of the welding components. In this study, we investigate the detection of weld beads using 2D image processing in an automatic recognition system. The sample image is obtained using a 2D vision camera embedded in a lighting system, from where a portion of the bead is successfully extracted after image processing. In this process, the soot removal algorithm plays an important role in accurate weld bead detection, and adopts adaptive local gamma correction and gray color coordinates. Using this automatic recognition system, geometric parameters of the weld bead, such as its length, width, angle, and defect size can also be defined. Finally, on comparing the obtained data with the industrial standards, we can determine whether the weld bead is at an acceptable level or not.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.262-266
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• 1999

Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

• Korean Journal of Food Science and Technology
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• v.19 no.2
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• pp.129-133
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• 1987

To develop a microcomputer-based data acquisition system for measurement of variables such as temperature and weight in food process, a low-cost data acquisition system was developed using an Apple II microcomputer. The system consisted of a microcomputer, a temperature sensor made of pt-100, a strain gauge load cell for weighing, a preamplifier for signal conditionings and an interface device. Interface device was built with programmable interface chip MC 6821, tristate buffer 74244 and analog-to-digital converter ADC 0809. The analog signals of temperature and weight were serially acquisited upon the program. The BASIC language was used for operating the data acquisition and data handling programs. The system successfully measured the variables such as temperature and weight with various sampling intervals in food dehydration process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.8
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• pp.2555-2560
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• 1996

Inspecting the dimensional accuracy of a car-body in assembly line is a very important process to assure high productivity. Now there exist two common inspecting methods in practice. One is to measure a sampled car-body with three dimensional measuring machine, and the other is to measure car-body with three dimensional measuring machine, and the other is to measure car-body in assembly line using many sensors fixed to a large jig frame. The formal method takes too long to inspect a sampled car-body of a same sort, and cannot therefore give an useful error trend for the whole production. On the other hand, the latter lacks flexibility and is very cost-intensive. By using industrial robots and sensors, an in-line Car-Body Measuring(CBM) system which ensured high flexiblity and sufficient accuracy was developed. This CBM cell operates in real production line and measures the check points by the non-contact type using camera and laser displacement sensor(LDS). This system can handle about 15 Measuring points within a cycle time of 40 seconds. A process computer controls whole process such as data acquisition file handling and data analysis. Robot arms changes in length due to ambient temperature fluctuation affecting the measuring accuracy. To compensate this error, a robot arm calibration process was developed.