• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.95-105
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• 1997

A new open-loop signal processing technique of digital phase tracking is known to have a Potential to solve the problems in the open-loop processor such as limited dynamic range, dependence on the optical intensity fluctuations, and dependence on gain fluctuations of signal path. But new problems with digital phase tracking must be solved before it can be a useful signal processing method. In this paper, barriers to the success of the digital phase tracking such as harmonics content, phase difference, amplitude variations of the phase modulation(PM) signal, bandwidth limit of the signal path, and the implementation of the mixer, are pointed out and their effects on the performance of the signal processor are analyzed to calculate the requirements of the signal processor for $1{\mu}rad$-grade FOG.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.115-125
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• 2011

The smartphones which have been recently are embedded with high resolution quality camera, assisted GPS, accelerometer, gyroscope and various sensors including magnetometer sensor that could be directly used for measurement. This study aims to suggest the possible application of smartphone camera providing high resolution images in terms of photogrammetry by calibrating it and assessing its accuracy. First of all, prior to the accuracy assessment of smartphone camera, camera calibration was conducted to correct lens distortion of each camera and the accuracy of image coordinates and object coordinates calculated by bundle adjustment during this procedure was analyzed. Also regarding three-dimensional positioning, result analysis depending on considering lens distortion coefficients was conducted, and finally relative accuracy of smartphone camera on metric camera was assessed. The result showed that in terms of distortion correction of smartphone camera, also higher order symmetric radial lens distortion coefficients should be considered, and three dimensional position determined by smartphone images was a little difference from that by metric camera. Therefore it is expected that smartphone images have huge possibility to be used for photogrammetry.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.11
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• pp.921-933
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• 2018

There is a method to enhance the pure navigation performance of INS(Inertial Navigation System) through the rotation of inertial measurement unit to compensate error sources of inertial sensors each other and that INS using this principle of operation is called rotational INS. In this paper, the exact error analysis of rotational INS based on ring laser gyro considering the coupling effect with gravity and earth rate is performed to evaluate the navigation performance by inertial sensor error sources. And error analysis and performance evaluation result confirmed by modelling and simulation is also proposed in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1275-1282
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• 2022

In this study, real-time character navigation using AR lens developed by Nreal is developed. Real-time character navigation is not possible with general marker-based AR because NPC characters must guide while moving in an unspecified space. To replace this, a markerless AR system was developed using Digital Twin technology. Existing markerless AR is operated based on hardware such as GPS, gyroscope, and magnetic sensor, so location accuracy is low and processing time in the system is long, resulting in low reliability in real-time AR environment. In order to solve this problem, using the SLAM technique to construct a space into a 3D object and to construct a markerless AR based on point location, AR can be implemented without any hardware intervention in a real-time AR environment. This real-time AR environment configuration made it possible to implement a navigation system using characters in tourist attractions such as Suncheon Bay Garden and Suncheon Drama Filming Site.

• PNF and Movement
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• v.20 no.3
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• pp.451-459
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• 2022

Purpose: Temporomandibular disorder (TMD) is a common musculoskeletal problem that causes pain in and disability of masticatory muscles, the temporo-mandibular joint (TMJ), and related structures. The purpose of this study was to compare pressure pain thresholds (PPTs) of masticatory muscles, cervical ranges of motion (ROM), and pelvic mobility during gait of subjects with or without TMD. Methods: In this study, pain thresholds and changes in the mobility of the cervical vertebrae and pelvis were measured in 25 patients with TMD and 25 healthy controls. Using a pressure algometer, the pressure pain thresholds (PPTs) of the masseter and temporalis muscles were measured in both groups. A gyroscope sensor with a mobile application was used to determine cervical ROM in the frontal and sagittal planes. A 3D-motion analysis system was used to evaluate pelvic mobility in the sagittal, frontal, and transverse planes during gait. Results: The TMD group showed significantly decreased PPTs of masseter and temporalis muscles compared with the control group (p < 0.05). Cervical ROM in flexion, extension, and lateral bending were significantly decreased in the TMD group compared with the control group (p < 0.05). In addition, antero-posterior pelvic tilt was significantly decreased in the TMD group (p < 0.05). Conclusion: The results of the current study suggest that there are close anatomical and functional relationships between TMD and muscle chains related to the cervical spine and pelvis. Therefore, more comprehensive body posture assessments, especially of painful areas, should be undertaken when studying TMD patients.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.325-332
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• 2022

In this study, real-time character navigation using AR lens developed by Nreal is developed. Real-time character navigation is not possible with general marker-based AR because NPC characters must guide while moving in an unspecified space. To replace this, a markerless AR system was developed using Digital Twin technology. Existing markerless AR is operated based on hardware such as GPS, gyroscope, and magnetic sensor, so location accuracy is low and processing time in the system is long, which results low reliability in real-time AR environment. In order to solve this problem, using the SLAM technique to construct a space into a 3D object and to construct a markerless AR based on point location, AR can be implemented without any hardware intervention in a real-time AR environment. This real-time AR environment configuration made it possible to implement a navigation system using characters in tourist attractions such as Suncheon Bay Garden and Suncheon Drama Filming Site.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.835-841
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• 2013

Modern solid-state gyroscopes (HRG) with hemispherical resonators from high-purity quartz glass and special surface superfinishing and ultrathin gold coating become the best instruments for precise-grade inertial reference units (IRU) targeting long-term space missions. Designing of these sensors could be a notable contribution into development of Korea as a space nation. In participial, 40mm diameter thin-shell resonator from high-purity fused quartz, fabricated as a single-piece with its supporting stem has been designed, machined, etched, tuned, tested, and delivered by STM Co. (ATS of Ukraine) several years ago; an extremely-high Q-factor (upto 10~20 millions) has been shown. Understanding of the best way how to match such a unique sensor with inner glass assembly of the gyro means how to use the high potential in a maximal extent; and this has become the urgent task. Inner quartz glass assembly has a very thin indium (In) layer soldered the resonator and its silica base (case), but effects of internal resonances between operational modal pair of the shell-cup and its side (parasitic) modes can notable degrade the potential of the sensor as a whole, instead of so low level of resonator's intrinsic losses. Unfortunately, there are special combinations of dimensions of the parts (so-called, "resonant sizes"), when intensive losses of energy occurs. The authors proposed to use the length of stem's fixture as an additional design parameter to avoid such cases. So-called, a cyclic scheme of finite element method (FEM) and ANSYS software were employed to estimate different combinations of gyro assembly parameters. This variant has no mismatches of numerical origin due to FEM's discrete mesh. The optimum length and dangerous "resonant lengths" have been found. The special attention has been paid to analyses of 3D effects in a cup-stem transient zone, including determination of a difference between the positions of geometrical Pole of the resonant hemisphere and of its "dynamical Pole", i.e., its real zone of oscillation node. Boundary effects between the shell (cup) and 3D short "beams" (inner and outer stems) have been ranged. The results of the numerical experiments have been compared with the classic model of a quasi-hemispherical shell band with inextensional midsurface, and the solution using Rayleigh's functions of the $1^{st}$ and $2^{nd}$ kinds. To guarantee the truth of the recommended sizes to a designer of the real device, the analytical and FEM results have been compared with experimental data for a party of real resonators. The consistency of the results obtained by different means has been shown with errors less than 5%. The results notably differ from the data published earlier by different researchers.

• Journal of Information Technology Applications and Management
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• v.28 no.3
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• pp.59-75
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• 2021

This study aims to establish a foundation for autonomous driving on campus and communication of abundant university information in the HCI environment in a VR environment where college freshmen can freely travel around campus facilities. The purpose of this study is to develop a three-dimensional VR-style campus tour system to establish a media environment to provide abundant university information guidance services to freshmen in non-face-to-face situations. This study designed a three-dimensional virtual reality campus tour system to solve the problem of discontinuity in which VR360 filming space does not lead to space like reality, and to solve many problems of expertise in VR technology by constructing an integrated production environment of tour system. We aim to solve the problem of inefficiency, which requires a large amount of momentum in virtual space, by constructing a GUI that utilizes the motion of the field of view focus. The campus environment was designed as a three-dimensional virtual reality using a three-dimensional graphic design. In non-face-to-face situations, college freshmen freely transformed the HMD VR device, smartphone, FPS operation mode of the gyroscope sensor. The design elements of the three-dimensional virtual reality campus tour system were classified as ①Visualization of factual experiences, ②Continuity of space movement, ③Operation, automatic operation mode, ④Natural landscape animation, ⑤Animation according to wind direction, ⑥Actual space movement mode, ⑦Informatization of spatial understanding, ⑧GUI by experience environment, ⑨Text GUI by building, ⑩VR360, 3D360 Studio Environment, ⑪Three-dimensional virtual space coupling block module, ⑫3D360-3D Virtual Space Transmedia Zone, ⑬Transformable GUI(VR Device Dual Viewer-Gyro Sensor Full Viewer-FPS Operation Viewer) and an integrated production environment was established with each element. It is launched online (http://vautu.com/u1) by constructing a GUI for free driving mode and college information screens to adapt to college life for freshmen, and designing an environment that can be used simultaneously by current media such as PCs, Android, and iPads. Therefore, it conducted user research, held a development presentation, a forum on excellence in university innovation support projects, and applied it as a system on the website of a particular university. College freshmen will be able to experience university information directly from the web and app to the virtual reality campus environment.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.