• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1161-1170
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• 2016

In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.

• Journal of Internet of Things and Convergence
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• v.2 no.2
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• pp.1-6
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• 2016

Recently, various types of wearable computers have been studied. In this paper, we analyze the characteristics of head motion information for the operation of the motion classifier produced motion headset that the user can use while listening to music. The prototype receives music from smart phone over bluetooth communications, and transmits the motion information measured by the acceleration sensor to the smart phone. And the smartphone classifies the motion of the head through a motion classifier. we implemented a prototype for our experiment. The user's head motion "up", "down", "left" and "right" were classified using a Bayesian classifier. As a result, in case of the movement of the head "up" and "down", there are a large changes in the x, z-axis values. In future we have a plan to perform a user study to find suitable variables for creating motion classifier.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.4
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• pp.61-67
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• 2018

High-frequency signals are analyzed at the inlet/outlet pipeline and pump casing during cavitation tests of the LOx pump for liquid rocket engines. Root-mean square values of all data are investigated with respect to cavitation number. The values of synchronous, harmonic, and cavitation instability frequencies are also calculated. Pressure pulsations of the inlet and outlet pipelines are affected by cavitation instabilities. The 3x component (i.e., the blade-passing frequency of the inducer) is predominant in the outlet pulsation sensor. This seems to be related to the fact that the number of impeller blades is a multiple of the number of the inducer blades. The cavitation instability is also measured at the accelerometer of the pump casing.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.49-56
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• 2018

As the era of the 4th industrial revolution comes, there is a growing interest in the use of UAVs. While various technologies are being developed using drones, controlling flight of drones is the most basic. Hovering control is essential in order to enable autonomous flight, especially during flight control of drones. In this paper, we design drones based on ATmega2560, Sonar, Optical Flow, and acceleration / gyro 6 axis sensor for drones hovering control, and developed horizontal control, altitude control, position tracking and fixed algorithm based on PID control. In this research, in order to measure the objective result of the drone, keeping the altitude immediately after the drone takes off according to the time, measure the movement value until the position is fixed and stable hovering is maintained and compared analyzed. Experimental results show that the drones can stably hover within 4cm horizontal and 2cm vertical from 50cm above the reference coordinates.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.88-95
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• 2020

In this paper, we propose a new arm swing walking pattern-based walking safety system for safe walking of elderly pedestrians. The proposed system is a walking safety system for elderly pedestrians using haptic-based devices such as smart bands and smart watches, and arm swing-based walking patterns to solve the problem that it is difficult to recognize the fall situation of pedestrians with the existing walking patterns of lower limb movements. Use. The arm swing-based walking pattern recognizes the number of steps and the fall situation of pedestrians through the swing of the arm using the acceleration sensor of the device, and creates a database of the location of the fall situation to warn elderly pedestrians when walking near the expected fall location. It delivers a message to provide pedestrian safety to the elderly. This system is expected to improve the safe walking rights and environment of the elderly.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1649-1654
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• 2021

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Composites Research
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• v.22 no.5
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• pp.1-7
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• 2009

The vehicle structure of Automated People Mover(APM) made of aluminum honeycomb sandwich with WR580INF4000 glass-fabric epoxy laminate facesheets was evaluated by structural test and finite element analysis. The test of the vehicle structure was conducted according to JIS E 7105. The structural integrity of vehicle structure was evaluated by stress, deflection and natural frequency obtained from dial-gauge and acceleration sensor. And the proposed finite element models were compared with the results of structural test. The results of finite element analysis showed good agreement with those of structural test. Also, in order to improve the stiffness of vehicle structure, the modified underframe model with reinforced side sill was proposed in design stage. The composite vehicle structures with modified underframe model had the improved structural stiffness about 44%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1653-1659
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• 2022

Virtual reality content provides users with a high sense of immersion by using HMD devices. However, while wearing the HMD device, it is difficult to determine the user's location or distance from obstacles, resulting in injuries due to physical collisions. In this paper, we propose a reminder module to prevent accidents by notifying the risk of collision with obstacles while wearing the HMD device. The proposed module receives the user's state from the acceleration and gyro sensor and determines the motion that is likely to cause a collision. If there is an obstacle in the expected collision range, a buzzer sounds to the wearer. As a result of the experiment, the accuracy of obstacle detection in the state of wearing the HMD was 86.6% in the 1st stage and 83.3% in the 2nd stage, confirming the performance of the accident prevention reminder.