• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.87-95
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• 2008

Ladar (Laser Detection And Ranging, Lidar) is a sensor to acquire precise distances to the surfaces of target region using laser signals, which can be suitably applied to ATD (Automatic Target Detection) for guided missiles or aerial vehicles recently. It provides a range image in which each measured distance is expressed as the brightness of the corresponding pixel. Since the precise 3D models can be generated from the Ladar range image, more robust identification and recognition of the targets can be possible. If we simulate the data of Ladar sensor, we can efficiently use this simulator to design and develop Ladar sensors and systems and to develop the data processing algorithm. The purposes of this study are thus to simulate the signals of a Ladar sensor based on linear frequency modulation and to create range images from the simulated Ladar signals. We first simulated the laser signals of a Ladar using FM chirp modulator and then computed the distances from the sensor to a target using the FFT process of the simulated signals. Finally, we created the range image using the distances set.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.325-330
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• 2022

The sensors of aircraft are necessity for mission performance and fusion process of data from them is applied for increase of mission efficiency and decrease of aircraft pilot workload. Data fusion is applied and developed to provide pilot a series of more processed data format about a specific target from sensors in aircraft. Military aircraft currently in operation are linked with a tactical data link such as Link-16 to display improved tactical situation to pilots to increase mission efficiency. By fusing the sensor data with improved accuracy obtained as the sensors' performance mounted on the aircraft become higher and the tactical situation information received through the tactical data link, it provides the pilot with a highly reliable tactical situation and mission environment, and expects efficient mission performance and high survivability. In this paper, a fusion architecture to produce fused data with realtime information from the sensors and data through a tactical data link is shown.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.81-94
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• 2024

This study explores and conducts an in-depth comparison of the epistemic characteristics in different data types utilized in the science inquiries of preservice teachers regarding noise as a risk in everyday life. Focusing on primary and secondary data in the context of science inquiries about noise, we examined how these data types differ in science inquires in terms of inquiry design, data collection, and analyses. The findings reveal that sensor-based primary data enable direct measurement and observation of key phenomena. Conversely, secondary data rely on predetermined measurement methods within a public data system. These differences require different epistemic considerations during the inquiry process. Based on these findings, we discuss the educational implications concerning teaching approaches for science inquiries, teacher education for inquiry teaching, and the development of risk response competencies in preparation for the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) era.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2940-2955
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• 2012

In this paper, we propose aggregate signcryption for achieving data security in UWSNs. The main challenge of these networks established in sensitive environments is offline sink visiting. Moreover, the sensors must retain collected data for long enough time to offload them onto the itinerant sink. Thus, the unattended nature of data collection intervals might offer the adversary the opportunity to apply various attacks without detection. In this paper, employing low order operations (in time and space), we propose a new secure scheme in which various security goals such as confidentiality (through encrypting), authentication and integrity (through signing) are achieved. In addition, the aggregation process of our scheme reduces the space and communication overheads both for sensors and sink, i.e. the proposed technique efficiently enables the sensors and sink to protect, verify and recover all the related data. We further compare our scheme with the best alternative work in the literature.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1260-1262
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• 1996

There has been much interested on the issues of designing and implementing the data acquisition and display system. This paper describes how to acquire and manage the data to be generated from sensor sources in a manufacturing process. The functionality of this data management system is composed of data acquisition, database management, processing and display of the available data. Also, this system has a adaptability to be carried throughout configuration management.

• Design & Manufacturing
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• v.14 no.4
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• pp.25-39
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• 2020

In this study, an edge device that monitors the injection molding process by measuring the mold vibration(acceleration) signal and the mold surface temperature was developed and evaluated its performance. During injection molding, signals of the injection start, V/P switchover, and packing end sections were obtained through the measurement of the mold vibration and the injection time and packing time were calculated by using the difference between the times of the sections. Then, the mold closed and mold open signals were obtained using a magnetic hall sensor, and cycle time was calculated by using the time difference between the mold closed time each process. As a result of evaluating the performance by comparing the process data monitored by the edge device with the shot data recorded on the injection molding machine, the cycle time, injection time, and packing time showed very small error of 0.70±0.38%, 1.40±1.17%, and 0.69±0.82%, respectively, and the values close to the actual were monitored and the accuracy and reliability of the edge device were confirmed. In addition, it was confirmed that the mold surface temperature measured by the edge device was similar to the actual mold surface temperature.

• Smart Media Journal
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• v.8 no.3
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• pp.53-61
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• 2019

In this paper, designing and implementing multi-communication middleware in multi-thread environmet through event-based synchronization method are proposed for stable data transmission of electronic optical equipment, which requires combining camera and various sensors to process multiple high-speed data. To verify the performance of the implemented communication middleware, image data and sensor data were sent to compare differences in reception-based and transmission-based cycles, and the maximum number of communication possibilities to transmit and process multiple was measured and analyzed. In addition, the proposed communication middleware's performance was verified through experiments such as validating the integrity of the transmitted data and measuring the Round Trip Time.

• The Journal of Bigdata
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• v.4 no.2
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• pp.61-71
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• 2019

Chemical Mechanical Planarization (CMP) process that planarizes semiconductor wafer's surface by polishing is difficult to manage reliably since it is under various chemicals and physical machinery. In CMP process, Material Removal Rate (MRR) is often used for a quality indicator, and it is important to predict MRR in managing CMP process stably. In this study, we introduce prediction models using machine learning techniques of analyzing time-series sensor data collected in CMP process, and the classification models that are used to interpret process quality conditions. In addition, we find meaningful variables affecting process quality and explain process variables' conditions to keep process quality high by analyzing classification result.