• Tribology and Lubricants
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• v.35 no.2
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• pp.114-122
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• 2019

High-speed rotating machinery requires low cost and reliable bearing elements with low friction, stable rotordynamic characteristics, and a simple design. This study experimentally evaluates the effects of bearing-support elements on the vibrational characteristics of a small-sized, high-speed permanent magnetic motor. A series of coast down tests from 100 krpm characterize the vibrational behaviors, rotor displacement, and housing acceleration of motors supported by ball bearings, ball bearings with a metal mesh damper, and gas foil bearings, respectively. Two eddy-current sensors installed in the horizontal and vertical directions measure the displacement of the rotor at its front nut, and a 3-axis accelerometer attached to the motor housing measures the housing acceleration. The test results reveal that synchronous (1X) vibration components most significantly affect the rotor displacement and housing acceleration, independent of the bearing-support elements. The motor supported by the deep-groove ball bearings results in the largest rotor vibrations increasing with speed; this is due to the absence of a damping mechanism. Additionally, the metal mesh damper effectively reduces the rotor displacement, housing acceleration, and sound-pressure level in the high-speed region (i.e., above 40 krpm), thus implying its substantial damping performance when installed on the outer race of the ball bearing. Lastly, the gas foil bearing supported motor yields the smallest rotor displacement, housing acceleration, and lowest sound-pressure level because of its hydrodynamic airborne operation, which does not require rolling elements that may cause mechanical friction and vibrations.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.53-62
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• 2017

In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.68-77
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• 2021

In this paper, a GPS/MEMS IMU integrated navigation receiver module capable of operating in a high dynamic environment is designed and fabricated, and the results is confirmed. The designed module is composed of RF receiver unit, inertial measurement unit, signal processing unit, correlator, and navigation S/W. The RF receiver performs the functions of low noise amplification, frequency conversion, filtering, and automatic gain control. The inertial measurement unit collects measurement data from a MEMS class IMU applied with a 3-axis gyroscope, accelerometer, and geomagnetic sensor. In addition, it provides an interface to transmit to the navigation S/W. The signal processing unit and the correlator is implemented with FPGA logic to perform filtering and corrrelation value calculation. Navigation S/W is implemented using the internal CPU of the FPGA. The size of the manufactured module is 95.0×85.0×.12.5mm, the weight is 110g, and the navigation accuracy performance within the specification is confirmed in an environment of 1200m/s and acceleration of 10g.

• 전자공학회논문지 IE
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• v.49 no.1
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• pp.12-17
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• 2012

Fast development of ubiquitous technology prompted the broadening of the related application area. Application of ubiquitous techniques and system into the construction sites may give us many benefits. There are always a lot of hazard situations in construction sites, and the falling is known to have the high accident rate. To prevent the falling, there has been a lot of efforts including safety education and use of safety gears. In this study, we designed, fabricated and tested a system that can monitor the worker's safety and location informations in real time by using the wireless technology of TOA and RSSI. We used ATmegal28 that is popular in the industrial equipments as MCU and NanoPan 5357 module from Nanotron and CC2500 chipset from TI for radio circuits. We also used 3-axis accelerometer and pressure MEMS sensors to obtain the environmental information, and therefore to aquire the informations of the worker's movement and altitude. We used Labview software from National Instrument to monitor and control the system. We developed the system to send the warning alarms to the server operator and the workers when the workers in the danger zone did not wear the safety hook.

• 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.