• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.

• Journal of Platform Technology
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• v.12 no.1
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• pp.12-31
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• 2024

Today, many countries around the world recognize the development of autonomous vehicles as a national growth engine, support technology development through various projects, and promote it as national policy. China and Korea are representative countries that are strongly promoting autonomous vehicle policies. The Chinese government's policy direction for self-driving cars focuses on support for fostering new industries. Korea has established mid- to long-term goals and plans to foster the future mobility industry as a key growth engine and is promoting these as a national task. Recently, China and Korea have established national pilot areas to test autonomous vehicle operation and are actively pursuing policies. We aim to compare and analyze the operation status of self-driving cars in China's Xiong'an New Area and South Korea's Sejong City and derive policy implications regarding self-driving cars, which are emerging as a key industry of the future. According to the analysis results, it was found that China's Xiong'an New District is ahead of Korea's Sejong City in terms of leader leadership. As a result, autonomous driving is being operated at the government-wide and national level in Xiong'an New Area. In terms of the driving force, in the case of Xiongan New Area, the policy is being promoted by companies centered on Baidu, and in the case of Sejong City, the policy is being promoted by the local government. As a result, it is estimated that Xiongan New Area will be able to reach commercialization before Sejong City. In the final policy proposal, it was proposed to break away from the existing government-led method and switch to a collaboration with the private sector and a private-led method.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.812-816
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• 2007

Having been speedy development of the IT industry, devices such as set-top box and smart phone are used in the broad filed. Because Java has merits that are platform independency, security and mobility, that is important software platform to offer stable services in the small device. This needs JVM(Java Virtual Machine) to execute Java application in the small device. CVM(Classic Virtual Machine) which is the kind of JVM is designed for embedded device to have limited resources. In this paper, CVM which is defined by CDC has designed and implemented on the Real-Time Operating System, UbiFOS$^{TM}$.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.3
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• pp.142-152
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• 2014

Vehicular Ad-hoc Networks (VANETs) are comprised of wireless mobile nodes characterized by a randomly changing topology, high mobility, availability of geographic position, and fewer power constraints. Orthogonal Frequency Division Multiplexing (OFDM) is a promising candidate for the physical layer of VANET because of the inherent characteristics of the spectral efficiency and robustness to channel impairments. The susceptibility of OFDM to Inter-Carrier Interference (ICI) is a challenging issue. The high mobility of nodes in VANET causes higher Doppler shifts, which results in ICI in the OFDM system. In this paper, a frequency domain com-btype channel estimation was used to cancel out ICI. The channel frequency response at the pilot tones was estimated using a Least Square (LS) estimator. An efficient interpolation technique is required to estimate the channel at the data tones with low interpolation error. This paper proposes a robust interpolation technique to estimate the channel frequency response at the data subcarriers. The channel induced noise tended to degrade the Bit Error Rate (BER) performance of the system. Parallel concatenated Convolutional codes were used for error correction. At the decoding end, different decoding algorithms were considered for the component decoders of the iterative Turbo decoder. A performance and complexity comparison among the various decoding algorithms was also carried out.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.281-282
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• 2022

현재 전동 킥보드 헬멧 미착용으로 인한 사고가 끊임없이 야기되고 있다. 개인형 이동장치 이용자 수가 증가함에 따라 법 개정을 통하여 헬멧 착용이 의무 사항이지만 여전히 낮은 착용률을 나타내고 있다. 본 논문에서는 모든 공유 킥보드 회사에서 사용 가능한 딥러닝 기반의 전동킥보드 헬멧 착용 인식시스템을 제시한다. 타 공유 전동킥보드 회사 앱에서 본 논문의 결과물을 사용할 때는 사용자가 타사 앱에서 헬멧 인식 요청 시 자사 앱에서 헬멧 착용 여부를 인식하여 결과를 전송한다. 자사 앱 사용자는 인식 기록을 조회할 수 있고, 타사 관리자는 사용자의 정보를 조회 및 관리할 수 있다. 본 시스템을 통해 전동킥보드 이용 시 헬멧 착용을 장려하여 착용률 증가와 사고 시 인명피해 감소를 기대한다.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.399-408
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• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.

• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.283-289
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• 2019

Recently, product mobility, data compatibility and communication connectivity have become very important to the control system, depending on the application of smart manufacturing. Accordingly, embedded systems are essential in all industries including home appliances, telecommunication, and national defense. Therefore, the demand for embedded system development personnel is increasing further, and education and training programs are needed to combine the practical skills of industrial sites, including programming skills and hardware. Currently, embedded system education offers a variety of education centered on Aduino, but this is mostly for beginners and is not sufficient for majors. In addition, while various prototype studies related to embedded systems are active, the training and training programs for working-level human resources needed at industrial sites are very scarce. Therefore, in order to foster the working personnel of the embedded system for the application of smart manufacturing, this paper selected the capability unit through in-depth interviews and survey analysis of 10 experts based on NCS, and developed education and training programs and contents.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.197-208
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• 2012

Recently, the development of smart devices such as smart phones and tablet PCs, with mobility, convenience and real-time computing, promotes proliferation and activation of social media. It also causes innovative changes in communication methods. Since 2010, researches in SNS (Social Networking Services) have focused on developing marketing strategies using SNS. On the other hand, the main purpose of this study is to provide a requirement analysis method of smart phone users by using content analysis of SNS. An information systems developed in this study in order to analyze content of SNS automatically because it is very difficult and time consuming to analyze it manually. In addition, this study compares the result of content analysis with that of Kano survey in order to examine consistency between the two results.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.385-395
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• 2013

In this paper, we target to develop weather platform and services to use in smart devices which is in real-time mobility environment. The existing TPEG-based navigation service requires a dedicated terminal, DMB communication method, and service scalability, so there are limits. In this paper, we analyze, processing and storage the real-time weather information suitable for navigation on the end user's smart devices by weather information service platform has been developed that can provide a standardized. In addition, we develop weavigation services and API for the developer to develop weather services easily. And we introduce system for serving information of dangerous district forecast based on natural disaster dangerous district data.