• Title/Summary/Keyword: Time synchronization

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Case Study of the Shallow Seismic Refraction Survey using Wave Glider (웨이브글라이더를 이용한 천해저 탄성파 굴절법 탐사 사례)

  • Kim, Young-Jun;Cheong, Snons;Koo, Nam-Hyung;Chun, Jong-Hwa;Kim, Jeong-Ki;Hwang, Kyu-Duk;Lee, Ho-Young;Heo, Sin;Moon, Ki-Don;Jeong, Cheol-Hun;Hong, Sung-Du
    • Geophysics and Geophysical Exploration
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    • v.20 no.1
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    • pp.43-48
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    • 2017
  • The applicability of refraction survey has been tested using a wave glider widely used in long-term ocean observations around the world. To record seismic refractions, a single channel streamer with metal weight and a seismic recording system were mounted on the wave glider. We used GPS precise time synchronization signal and radio frequency (RF) communication to synchronize shot and recorder triggers and to control acquired data quality in real time. When the wave glider is positioned close to the set point, a 2,000 J sparker is exploded along the designed track at 2 second intervals. Through the test survey, we were able to successfully acquire refractions from the subsurface.

A Study on Adaptive Pilot Beacon for Hard Handoff at CDMA Communication Network (CDMA 통신망의 하드핸드오프 지원을 위한 적응형 파일럿 비콘에 관한 연구)

  • Jeong Ki Hyeok;Hong Dong Ho;Hong Wan Pyo;Ra Keuk Hwawn
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.10A
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    • pp.922-929
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    • 2005
  • This paper proposes an adaptive pilot beacon equipment for mobile communication systems based on direct spread spectrum technology which generates the pilot channel for handoff between base stations by using the information acquired from the downstream wireless signal regarding the overhead channel information. Such an adaptive pilot beacon equipment will enable low power operation since among the wireless signals, only the pilot channel will be generated and transmitted. The pilot channel in the downstream link of the CDMA receiver is used to acquire time and frequency synchronization and this is used to calibrate the offset for the beacon, which implies that time synchronization using GPS is not required and any location where forward receive signal can be received can be used as the installation site. The downstream link pilot signal searching within the CDMA receiver is performed by FPGA and DSP. The FPGA is used to perform the initial synchronization for the pilot searcher and DSP is used to perform the offset correction between beacon clock and base station clock. The CDMA transmitter the adaptive pilot beacon equipment will use the timing offset information in the pilot channel acquired from the CDMA receiver and generate the downstream link pilot signal synchronized to the base station. The intermediate frequency signal is passed through the FIR filter and subsequently upconverted and amplified before being radiated through the antenna.

Research on Digital twin-based Smart City model: Survey (디지털 트윈 기반 스마트 시티 모델 연구 동향 분석)

  • Han, Kun-Hee;Hong, Sunghyuck
    • Journal of Convergence for Information Technology
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    • v.11 no.11
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    • pp.172-177
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    • 2021
  • As part of the digital era, a digital twin that simulates the weak part of a product by performing a stress test that reduces the lifespan of some expensive equipment that cannot be done in reality by accurately moving the real world to virtual reality is being actively used in the manufacturing industry. Due to the development of IoT, the digital twin, which accurately collects data collected from the real world and makes it the same in the virtual space, is mutually beneficial through accurate prediction of urban life problems such as traffic, disaster, housing, quarantine, energy, environment, and aging. Based on its action, it is positioned as a necessary tool for smart city construction. Although digital twin is widely applied to the manufacturing field, this study proposes a smart city model suitable for the 4th industrial revolution era by using it to smart cities and increasing citizens' safety, welfare, and convenience through the proposed model. In addition, when a digital twin is applied to a smart city, it is expected that more accurate prediction and analysis will be possible by real-time synchronization between the real and virtual by maintaining realism and immediacy through real-time interaction.

A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • 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.