• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.105-114
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• 2019

As the Industrial Revolution progressed, Big Data technologies were used to develop a system that instantly identified the consequences of older vehicles using mobile devices. First, data from the vehicle was collected using the OBD2 sensor, and the data collected was stored in the raspberry pie, giving it the same situation that the raspberry pie was driving. In the event that vehicle data is generated, the data is collected in real time, stored in multiple nodes, and visualized and printed based on the processed, refined, processed and processed data. We can use Big Data in this process and quickly process vehicle data to identify it effectively through mobile devices.

• Journal of Platform Technology
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• v.8 no.4
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• pp.59-69
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• 2020

Recently, the seriousness of ultrafine dust and fine dust has emerged as a national disaster, but small and medium-sized cities in provincial areas lack fine dust monitoring stations compared to their area, making it difficult to manage fine dust. Although the computing resources for collecting and processing fine dust data are not large, it is necessary to utilize cloud and private and public data to share data. In this paper, we proposed a small edge computing system that can measure fine dust, ultrafine dust and temperature and humidity and process it to provide real-time control of fine dust and service to the public. Collecting fine dust data and using public and private data to service fine dust ratings is efficient to handle with edge computing using raspberry pie because the amount of data is not large and the processing load is not large. For the experiment, the experiment system was constructed using three sensors, raspberry pie and Thinkspeak, and the fine dust measurement was conducted in northern part of kyongbuk region. The results of the experiment confirmed the measured fine dust measurement results over time based on the GIS data of the private sector.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.91-98
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• 2022

In this study, Pukyong National University's floating population survey and analysis were conducted using Raspberry Pie, an open source hardware, and object detection algorithms based on deep learning technology. After collecting images using Raspberry Pie, the person detection of the collected images using YOLO3's IMAGEAI and YOLOv5 models was performed, and Haar-like features and HOG models were used for accuracy comparison analysis. As a result of the analysis, the smallest floating population was observed due to the school anniversary. In general, the floating population at the entrance was larger than the floating population at the exit, and both the entrance and exit were found to be greatly affected by the school's anniversary and events.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.285-302
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• 2024

For this article, we conducted frequency analysis, topic modeling, and network analysis on eleven texts related to collection development policy found in the National Library of Korea. We deduced the main keywords related to collection development policies and analyzed the relationship between them. We subsequently conducted a pie coefficient analysis to identify the characteristics of collection development policies of university libraries and public libraries by category. The results showed that keywords such as "material," "library," "collection development," "user," and "collection" were the main keywords in frequency analysis and network centrality. Meanwhile, the pie coefficient analysis revealed that keywords such as "university," "construction," "student," "target," and "cost" were prevalent in university libraries, indicating that the academic needs of users and the discussion of digital resources were primary issues, while keywords related to the information needs of various user groups-including "adults," "survey," "feature," and "religion" -appeared in public libraries.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.165-172
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• 2024

In this paper, we designed a smart home system that can be used intuitively and easily in everyday life, such as sending text messages to users, providing various information and scheduling using smart AI, and providing lighting and atmosphere suitable for the atmosphere in situations such as listening to music using neopixels, as well as using ESP32, RFID, and Google Cloude Console using raspberry pie. As a result of the experiment, it was confirmed that security characters were normally sent to users when RFID was recognized on ESP32 connected to Wi-Fi even if the power was reconnected, and smart AI using Neopixel lighting, Raspberry Pie, and voice recognition, which calculated frequency, also changed the recognition rate over distance, but it worked.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1562-1582
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• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.971-981
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• 2012

Google map has become one of the most recognized and comfortable means for providing statistical information of geographically referenced data. In this article, we introduce R functions to embed google map images on R interface and develop a function to represent statistical graphs such as bar graph, pie chart, and rectangle graph on a google map images.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1039-1046
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• 2015

We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.435-440
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• 1996

PHEBUS FPT0 노내실험의 핵연료 다발에 대한 실험후 비파괴 검사 및 파괴 검사 결과를 분석하여 노심손상 후기과정을 정alf 분석하였다. 분석한 비파괴 검사결과는 gamma scanning, radiography, tomographies 였으며 파괴 검사 결과는 정밀사진, metallography, Electron Probe Micro Analysis(EPMA)였다. 그 결과, PHEBUS-FPT0 실험에 사용한 핵연료다발은 기존에 수행된 어떤 다른 노내실험의 핵연료다발보다 많이 용융되었으며 용융 pool 및 피막충의 형성, 용융물 내부의 자연대류 열전달과 이에 따른 shroud 물질 손상, 핵연료다발 물질들간의 eutectic 형성 등을 보여주었다. 특히 Ag-In-Cd 제어봉 물질과 stainless-steel이 핵연료봉 물질과 반응하여 이들의 용융온도를 낮게하여 실험 예측값보다 많이 핵연료다발이 손상되어 기존 중대사고 해석 전산코드의 개선이 요구되었다.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.47-51
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• 2003

Microfluidic chips such as lab-on-a-chip (LOC) include micro-channels for sample delivery, mixing, reaction, and separation. Pressure driven flow or electro-osmotic flow (EOF) has been usually employed to deliver bio-samples. Having some advantages of easy control, the flow characteristics of EOF in microchannels should be fully understood to effectively control the electro-osmotic pump for bio-sam-pie delivery. In this study, a micro PIV system with an epifluorescence inverted microscope and a cooled CCD was used to measure velocity fields of EOF in a glass microchannel and a PDMS microchannel. The EOF velocity fields were changed with respect to electric charge of seeding particles and microchannel materials used. The EOF has nearly uniform velocity distribution inside the microchannel when pressure gradient effect is negligible. The mean streamwise velocity is nearly proportional to the applied electric field. Glass microchannels give better repeatability in PIV results, compared with PDMS microchannels which are easy to fabricate and more suitable for PIV experiments.