• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.71-78
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• 2024

In indoor environments, since global positioning system (GPS) signals can be blocked by obstacles, such as building structure. the performance of GPS-based positioning methods can be degraded because of the loss of GPS signals. To solve this problem, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope, accelerometer, and magnetometer, have been proposed to enhance the positioning accuracy in indoor environments. IMU-based positioning methods can estimate the location of the user by calculating the velocity and heading angle of the user without the help of GPS. However, low-cost MEMS IMUs may lead to drift error and large bias. In addition, positioning errors in IMU-based positioning approaches can be caused by the irrelevant motion of the pedestrian. In this study, we propose an enhanced indoor positioning method that provides more reliable localization results by using the camera, light detection and right (LiDAR), and ARKit framework on the iPhone. Through reliable positioning results and augmented reality (AR) experiences, our indoor positioning system can provide indoor space guidance services.

• 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.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.54-64
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• 2013

In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.53-62
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• 1999

Micro polysilicon actuators, which are widely used in the field of MEMS (Microelectromechanical System) technology, were fabricated using polysilicon thin layers. Polysilicon deposition were carried out to have symmetrical layer structures with a LPCVD (Low Pressure Chemical Vapor Deposition) system, and we have measured physical characteristics by micro test patterns, such as bridges and cantilevers to verify minimal mechanical stress and stress gradient in the polysilicon layers according to the methods of mutilayer deposition, doping, and thermal treatment, also, analyzed the properties of each specimen, which have a different process condition, by XRD, and SIMS etc.. Finally, the fabricated planar polysilicon resonator, symmetrically stacked to $6.5{\mu}m$ thickness, showed Q of 1270 and oscillation ampitude of $5{\mu}m$ under DC 15V, AC 0.05V, and 1000 mtorr pressure. The developed micro polysilicon resonator can be utilized to micro gyroscope and accelerometer sensor.

• 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 Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1776-1782
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• 2010

The interferometric fiber optic gyroscopes (FOGs) are well known as sensors of rotation, which are based on Sagnac effect, and have been under development for a number of years to meet a wide range of performance requirements. This paper describes the development of open-loop FOG and digital signal processing techniques implemented on FPGA. Our primary goal was to obtain intermediate accuracy (pointing grade) with a good bias stability (0.22deg) and scale factor stability, extremely low angle random walk (0.07deg) and significant cost savings by using a single mode fiber. A secondary goal is to design all digital FOG signal processing algorithms with which the SNR at the digital demodulator output is enhanced substantially due to processing gain. The Cascaded integrator bomb(CIC) type of decimation filter only requires adders and shift registers, low cost processors which has low computing power still can used in this all digital FOG processor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.153-156
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• 2009

The interferometric fiber optic gyroscopes (FOGs) are well known as sensors of rotation, which are based on Sagnac effect, and have been under development for a number of years to meet a wide range of performance requirements. This paper describes the development of open-loop FOG and digital signal processing techniques implemented on FPGA. Our primary goal was to obtain intermediate accuracy (pointing grade) with a good bias stability ($0.22^{\circ}/hr$) and scale factor stability, extremely low angle random walk ($0.07^{\circ}/\sqrt{hr}$) and significant cost savings by using a single mode fiber. A secondary goal is to design all digital FOG signal processing algorithms with which the SNR at the digital demodulator output is enhanced substantially due to processing gain. The CIC type of decimation block only requires adders and shift registers, low cost processors which has low computing power still can used in this all digital FOG processor.

• 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 Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.111-120
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• 2022

It is essential to make a purchase decision to make sure that the furniture matches well with other structures in the room. Moreover, in the Untact Marketing situation caused by the COVID-19 crisis, this is becoming an even more impact factor. Accordingly, methods of measuring length using AR(Augmented Reality) are emerging with the advent of AR open sources such as ARCore and ARKit for furniture arrangement interior simulation. Since this existing method using AR generates a Depth Map based on a flat camera image and it also involves complex three-dimensional calculations, limitations are revealed in work that requires the information of accurate room size using a smartphone. In this paper, we propose a method to accurately measure the size of a room using only the accelerometer and gyroscope sensors built in smartphones without using ARCore or ARKit. In addition, as an example of application using the presented technique, a method for applying a pre-designed room interior to each room is presented.