• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.275-276
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• 2009

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.54-61
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• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

• Survey Research
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• v.1 no.1
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• pp.73-108
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• 2000

This study investigates methodological implications of cyberspace and information technology for online survey research Online survey method provides useful methodological tools for collecting empirical data for the study of cyberspace, In terms of sampling frame , selection method, and questionnaire from four major types of the online survey research can be found: member survey visitor survey e-mail survey and the electronic survey. Online survey research with sample design(population specification error. sampling frame error, selection error) measurement (measurement instrument bias system-related processing error, nonrespone, and response bias) and research ethics (privacy and netiquette) pose important methodological issues for the survey. The realization of the technological possibilities of online survey will be possible only through a right consideration of these issues.

• 한국초전도학회:학술대회논문집
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• v.10
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• pp.85-85
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• 2000

The normal state of high-Tc superconducting materials has been believed to contain important clues to finding the correct mechanism of the high-Tc superconductivity. One example is the existence of pseudogap in the normal state even above Tc, as observed in various measurements such as photoemission spectroscopy and tunneling conductance. In this pseudogap regime the existence of preformed pairs only with local phase coherence has been debated. Recently Choi, Bang, and Campbell[1] have proposed the occurrence of the zero-bias conductance enhancement due to Andreev quasiparticle reflection from the preformed pairs even with the local phase coherence. In this study we examine the zero-bias enhancement of the differential conductance near or slightly above Tc, using c-axis tunneling in mesa structure of Bi2Sr20a0u208+d single crystals. In slightly overdoped samples zero-bias conductance enhancement (ZBCE) has been observed over a range of 2 K above Tc. In contrast, in underdoped samples with Tc${\sim}$72K the ZBCE appears over a range of 5-6 K above Tc, a much wider temperature range than in overdoped samples. This result may pose as positive signs of the existence of prefurmed pairs in the normal state of high- Tc superconducting materials.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1045-1050
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• 2005

This paper describes a design experience of a low-cost 6 DOF spatial tracker system where relative low accuracy and relatively long ranges, wireless communication will be achieved by means of low cost accelerometers and gyros with contemporary microprocessor. However, there are two key problems; one is the bias drift problem and the other is that single or double integration of acceleration signal suffers not only from noise but also from nonlinear effects caused by gravity. To be specific, beginning and stopping of hand motions needs to be accurately detected to initiate and terminate integration process to get position and pose of the hand from accelerometer and gyro signals, since errors due to noise and/or hand-shaking motions accumulated by integration processes. Several experimental results are shown to validate our proposed algorithms.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.9-17
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• 2022

The state-of-the-art AI systems pose many ethical issues ranging from massive data collection to bias in algorithms. In response, this paper proposes a more ethic-friendly AI architecture by combining Federated Learning(FL) and Blockchain. We discuss the importance of each issues and provide requirements for an ethical AI system to show how our solutions can achieve more ethical paradigms. By committing to our design, adopters can perform AI services more ethically.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1566-1576
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• 2007

Camera pose information from 2D face image is very important for making virtual 3D face model synchronize with the real face. It is also very important for any other uses such as: human computer interface, 3D object estimation, automatic camera control etc. In this paper, we have presented a camera position determination algorithm from a single 2D face image using the relationship between mouth position information and face region boundary information. Our algorithm first corrects the color bias by a lighting compensation algorithm, then we nonlinearly transformed the image into $YC_bC_r$ color space and use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between $C_b\;and\;C_r$ cluster of face feature to detect mouth position. And then we use the geometrical relationship between mouth position information and face region boundary information to determine rotation angles in both x-axis and y-axis of camera position and use the relationship between face region size information and Camera-Face distance information to determine the camera-face distance. Experimental results demonstrate the validity of our algorithm and the correct determination rate is accredited for applying it into practice.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2687-2693
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• 2012

Objective: Passive smoking has been considered as a risk factor of many cancers. To examine whether it might also pose a risk for cervical cancer, we performed a meta-analysis based on published case-control studies. Methods: We searched the PubMed database and references of included studies up to February 10th, 2012 for relevant studies. After two authors independently assessed the methodological quality and extracted data, a meta-analysis was conducted using CMA v2 software. Publication bias was evaluated by funnel plot, using Egger's and Begg's tests. Results: Finally 11 eligible studies yielded, involving 3,230 cases and 2,982 controls. The results showed that women who never smoke but exposed to smoking experience a 73% increase in risk of cervical cancer compared with non-exposed women (OR = 1.73, 95% CI = 1.35 - 2.21, p<0.001). Subgroup and sensitivity analyses indicated this result to be robust. Moderate publication bias was detected by visualing funnel plot, Egger's and Begg's tests. Conclusion: Based on currently available evidence, the findings of this meta-analysis suggests that passive smoking significantly and independently increases the risk of cervical cancer.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.