• Title/Summary/Keyword: Laboratory data

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Real-world multimodal lifelog dataset for human behavior study

  • Chung, Seungeun;Jeong, Chi Yoon;Lim, Jeong Mook;Lim, Jiyoun;Noh, Kyoung Ju;Kim, Gague;Jeong, Hyuntae
    • ETRI Journal
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    • v.44 no.3
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    • pp.426-437
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    • 2022
  • To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

A Serial Input/Output Circuit with 8 bit and 16 bit Selection Modes

  • Yang, Yil-Suk;Kim, Jong-Dae;Roh, Tae-Moon;Lee, Dae-Woo;Koo, Jin-Gun;Kim, Sang-Gi;Park, Il-Yong;Yu, Byoung-Gon
    • ETRI Journal
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    • v.24 no.6
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    • pp.462-464
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    • 2002
  • This paper presents a serial interface circuit that permits selection of the amount of data converted from serial-to-parallel and parallel-to-serial and overcomes the disadvantages of the conventional serial input/output interface. Based on the selected data length operating mode, 8 bit or 16 bit serial-to-parallel and 8 bit or 16 bit parallel-to-serial conversion takes place in data blocks of the selected data length.

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Dynamic Data Migration in Hybrid Main Memories for In-Memory Big Data Storage

  • Mai, Hai Thanh;Park, Kyoung Hyun;Lee, Hun Soon;Kim, Chang Soo;Lee, Miyoung;Hur, Sung Jin
    • ETRI Journal
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    • v.36 no.6
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    • pp.988-998
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    • 2014
  • For memory-based big data storage, using hybrid memories consisting of both dynamic random-access memory (DRAM) and non-volatile random-access memories (NVRAMs) is a promising approach. DRAM supports low access time but consumes much energy, whereas NVRAMs have high access time but do not need energy to retain data. In this paper, we propose a new data migration method that can dynamically move data pages into the most appropriate memories to exploit their strengths and alleviate their weaknesses. We predict the access frequency values of the data pages and then measure comprehensively the gains and costs of each placement choice based on these predicted values. Next, we compute the potential benefits of all choices for each candidate page to make page migration decisions. Extensive experiments show that our method improves over the existing ones the access response time by as much as a factor of four, with similar rates of energy consumption.

Assessment of Trophic State for Daecheong reservoir Using Landsat TM Imagery Data (Landsat TM 영상자료를 이용한 대청호의 영양상태 평가)

  • Han, E.J.;Kim, K.T.;Jeong, D.H.;Cheon, S.Y.;Kim, S.J.;Yu, S.J.;Hwang, J.Y.;Kim, T.S.;Kim, M.H.
    • Journal of Environmental Impact Assessment
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    • v.7 no.1
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    • pp.81-91
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    • 1998
  • The objective of this study was to use remotely sensed data, combined with in situ data, for the assessment of trophic state for Daecheong reservoir. Three Landsat TM(Thematic Mapper) imagery data were processed to portray trophic state conditions. The remotely sensed data and the measured data were obtained on 20 June 1995. Regression models have been developed between the chlorophyll-a concentration and reflectance which was converted to Landsat TM digital data. The regression model was determined based on the correlation coefficient which was higher than 0.7 and was applied to the entire study area to generate a distribution map of chlorophyll-a and trophic state. The equation, providing estimates of chlorophyll-a concentration, represented the year-to-year spatial variation of trophic zones in the reservoir. Satellite remote sensing data derived from Landsat TM had been successfully used for trophic slate mapping in Daecheong reservoir.

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Harmonization of laboratory results by data adjustment in multicenter clinical trials

  • Lee, Sang Gon;Chung, Hee-Jung;Park, Jeong Bae;Park, Hyosoon;Lee, Eun Hee
    • The Korean journal of internal medicine
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    • v.33 no.6
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    • pp.1119-1128
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    • 2018
  • Background/Aims: In multicenter clinical trials, laboratory tests are performed in the laboratory of each center, mostly using different measuring methodologies. The purpose of this study was to evaluate coefficients of variation (CVs) of laboratory results produced by various measuring methods and to determine whether mathematical data adjustment could achieve harmonization between the methods. Methods: We chose 10 clinical laboratories, including Green Cross Laboratories (GC Labs), the central laboratory, for the measurement of total cholesterol, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), serum triglycerides, creatinine, and glucose. The serum panels made with patient samples referred to GC Labs were sent to the other laboratories. Twenty serum samples for each analyte were prepared, sent frozen, and analyzed by each participating laboratory. Results: All methods used by participating laboratories for the six analytes had traceability by reference materials and methods. When the results from the nine laboratories were compared with those from GC Labs, the mean CVs for total cholesterol, HDL-C, LDL-C, and glucose analyzed using the same method were 1.7%, 3.7%, 4.3%, and 1.7%, respectively; and those for triglycerides and creatinine analyzed using two different methods were 4.5% and 4.48%, respectively. After adjusting data using Deming regression, the mean CV were 0.7%, 1.4%, 1.8%, 1.4%, 1.6%, and 0.8% for total cholesterol, HDL-C, LDL-C, triglyceride, creatinine, and glucose, respectively. Conclusions: We found that more comparable results can be produced by laboratory data harmonization using commutable samples. Therefore, harmonization efforts should be undertaken in multicenter trials for accurate data analysis (CRIS number; KCT0001235).

Efficient Public Verification on the Integrity of Multi-Owner Data in the Cloud

  • Wang, Boyang;Li, Hui;Liu, Xuefeng;Li, Fenghua;Li, Xiaoqing
    • Journal of Communications and Networks
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    • v.16 no.6
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    • pp.592-599
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    • 2014
  • Cloud computing enables users to easily store their data and simply share data with others. Due to the security threats in an untrusted cloud, users are recommended to compute verification metadata, such as signatures, on their data to protect the integrity. Many mechanisms have been proposed to allow a public verifier to efficiently audit cloud data integrity without receiving the entire data from the cloud. However, to the best of our knowledge, none of them has considered about the efficiency of public verification on multi-owner data, where each block in data is signed by multiple owners. In this paper, we propose a novel public verification mechanism to audit the integrity of multi-owner data in an untrusted cloud by taking the advantage of multisignatures. With our mechanism, the verification time and storage overhead of signatures on multi-owner data in the cloud are independent with the number of owners. In addition, we demonstrate the security of our scheme with rigorous proofs. Compared to the straightforward extension of previous mechanisms, our mechanism shows a better performance in experiments.