• Journal of Internet Computing and Services
• /
• v.13 no.1
• /
• pp.45-55
• /
• 2012

Electronic data management is getting important to reduce overall cost and run-time of clinical data management with the enhancement of data quality. It also critically needs to meet regulated guidelines for the overall quality and safety of electronic clinical trials. The purpose of this paper is to develop the performance evaluation framework in electronic clinical data management. Four key metrics in the area of infrastructure, intellectual preparation, study implementation and study completion covering major aspects of clinical trial processes are proposed. The performance measures evaluate the extent of regulation compliance, data quality, cost and efficiency of electronic data management process. They also provide measurement indicators for each evaluation items. Based on the key metrics, the performance evaluation framework is developed in three major areas involved in clinical data management - clinical site, monitoring and data coordinating center. As of the initial attempt how to evaluate the extent of electronic data management in clinical trials by Delphi survey, further empirical studies are planned and recommended.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.04a
• /
• pp.187-196
• /
• 2000

A lot of Internet shopping malls strive for obtaining a competitive advantage over others in an increasingly tighter electronic marketplace. To this end, understanding customer preference toward products (or services) and administering appropriate marketing strategy is essential for their continuous survival. However, only a few marketing researchers and practicioners focused on this issue, compared with academic and industry efforts devoted to traditional market segmentation. In this paper, we suggest a methodology of conjoint segmentation for electronic shopping malls. Traditional market segmentation methodologies based on customer's profile sometimes fail to utilize abundant information given while navigating around cyber shopping malls. In this methodology, we do not impose information overload to the customer for preference elicitation, but this methodology, we do not impose information overload to the customer for preference elicitation, but capture automatically generated surfing or buying data and analyze them to get useful market segmentation information. The methodology consists of 4-stages: 1) analyzing legacy homepages, 2) data preparation, 3) estimating and interpreting the result, and 4) developing marketing mix. Our methodology was to give useful guidelines for market segmentation to companies working in the electronic marketplace.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.127-133
• /
• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Geophysics and Geophysical Exploration
• /
• v.24 no.3
• /
• pp.78-88
• /
• 2021

Owing to the abnormal weather conditions due to global warming, carbon capture and storage (CCS) technology has attracted global attention as a countermeasure to reduce CO₂ emissions. In the Pohang CCS demonstration project in South Korea, 100 tons of CO₂ were successfully injected into the subsurface CO₂ storage in early 2017. However, after the 2017 Pohang earthquake, the Pohang CCS demonstration project was suspended due to an increase in social concerns about the safety of the CCS project. In this study, to reconfirm the structural suitability of the CO₂ storage site in the Pohang Basin, we employed seismic imaging based on reverse-time migration (RTM) to analyze small-scale ocean-bottom seismic data, which have not been utilized in previous studies. Compared with seismic images using marine streamer data, the continuity of subsurface layers in the RTM image using the ocean-bottom seismic data is improved. Based on the obtained subsurface image, we discuss the structural suitability of the Pohang CO₂ storage site.

• Asian-Australasian Journal of Animal Sciences
• /
• v.4 no.4
• /
• pp.305-328
• /
• 1991

Pig breeding programs have been very successful in the improvement of animals by the simple expedient of focusing on a few traits of economic importance, particularly growth efficiency and leanness. Further reductions in leanness may become more difficult to achieve, due to reduced genetic variation, and less desirable, due to adverse correlated effects on meat and eating quality. Best linear unbiased prediction (BLUP) of breeding values makes possible the incorporation of data from many sources and increases the value of including traits such as sow performance in the breeding objective. Advances in technology, such as electronic animal identification, electronic feeders, improved ultrasonic scanners and automated data capture at slaughter houses, increase the number of sources of information that can be included in breeding value predictions. Breeding program structures will evolve to reflect these changes and a common structure is likely to be several or many breeding farms genetically linked by A.i., with data collected on a number of traits from many sources and integrated into a single breeding value prediction using BLUP. Future developments will include the production of a porcine gene map which may make it possible to identify genes controlling economically valuable traits, such as those for litter size in the Meishan, and introgress them into nucleus populations. Genes identified from the gene map or from other sources will provide insight into the genetic basis of performance and may provide the raw material from which transgenic programs will channel additional genetic variance into nucleus populations undergoing selection.

• Transactions on Electrical and Electronic Materials
• /
• v.7 no.6
• /
• pp.313-318
• /
• 2006

This paper introduces a partial discharge (PD) severity metric, S, based on the evaluation of time-sequence PD data capture and resulting Time-Sequence-Analysis Discharge (TSAD) level distributions. Basically based on an IEC60270 measurement technique, each PD event is time stamped and the discharge level noted. By evaluating the time differences between a previous and subsequent discharge, a 3D plot of time-sequence activity and discharge levels can be produced. From these parameters a measurement of severity, which takes into account dynamic or instantaneous variations in both the time of occurrence and the level of discharge, rather than using standard repetition rate techniques, can be formulated. The idea is to provide a measure of the severity of PD activity for potentially measuring the state of insulation within an item of plant. This severity measure is evaluated for a simple point-plane geometry in $SF_{6}$ as a function of gap distance and applied high voltage. The results show that as the partial discharge activity increases, the severity measure also increases. The importance of future investigations, quantifications and evaluations of the robustness, sensitivity and importance of such a severity measurement, as well as comparing it with typical repetition rate assessment techniques, and other monitoring techniques, are also very briefly discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.4
• /
• pp.249-256
• /
• 2022

In the case of Drone Photogrammetry, the "pixel to point tool" module of Metashape, Pix4D Mapper, ContextCapture, and Global MapperGIS, which is a simple software, are widely used. Each SW has its own logic for the analysis of aerial triangulation, but from the user's point of view, it is necessary to select a SW by comparative analysis of the coordinate values of geospatial information for the result. Taking aerial photos for drone photogrammetry, surveying GCP reference points through VRS-GPS Survey, processing the acquired basic data using each SW to construct ortho image and DSM, and GCPSurvey performance and acquisition from each SW The coordinates (X,Y) of the center point of the GCP target on the Ortho-Image and the height value (EL) of the GCP point by DSM were compared. According to the "Public Surveying Work Regulations", the results of each SW are all within the margin of error. It turned out that there is no problem with the regulations no matter which SW is included within the scope.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1452-1455
• /
• 2021

Recently, point clouds are generated by capturing real space in 3D, and it is actively applied and serviced for performances, exhibitions, education, and training. These point cloud data require post-correction work to be used in virtual environments due to errors caused by the capture environment with sensors and cameras. In this paper, we propose an enhancement technique for 3D point cloud data by applying generative adversarial network(GAN). Thus, we performed an approach to regenerate point clouds as an input of GAN. Through our method presented in this paper, point clouds with a lot of noise is configured in the same shape as the real object and environment, enabling precise interaction with the reconstructed content.

• PNF and Movement
• /
• v.18 no.2
• /
• pp.287-296
• /
• 2020

Purpose: The study aims were to develop a wearable inertial sensor-based gait analysis device that uses machine learning algorithms, and to validate this novel device using temporal gait parameters. Methods: Thirty-four healthy young participants (22 male, 12 female, aged 25.76 years) with no musculoskeletal disorders were asked to walk at three different speeds. As they walked, data were simultaneously collected by a motion capture system and inertial measurement units (Reseed®). The data were sent to a machine learning algorithm adapted to the wearable inertial sensor-based gait analysis device. The validity of the newly developed instrument was assessed by comparing it to data from the motion capture system. Results: At normal speeds, intra-class correlation coefficients (ICC) for the temporal gait parameters were excellent (ICC [2, 1], 0.99~0.99), and coefficient of variation (CV) error values were insignificant for all gait parameters (0.31~1.08%). At slow speeds, ICCs for the temporal gait parameters were excellent (ICC [2, 1], 0.98~0.99), and CV error values were very small for all gait parameters (0.33~1.24%). At the fastest speeds, ICCs for temporal gait parameters were excellent (ICC [2, 1], 0.86~0.99) but less impressive than for the other speeds. CV error values were small for all gait parameters (0.17~5.58%). Conclusion: These results confirm that both the wearable inertial sensor-based gait analysis device and the machine learning algorithms have strong concurrent validity for temporal variables. On that basis, this novel wearable device is likely to prove useful for establishing temporal gait parameters while assessing gait.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.157-164
• /
• 2023

In modern society, smart home has become a part of people's daily life. But traditional smart home systems often have problems such as security, data centralization and easy tampering, so a blockchain is an emerging technology that solves the problems. This paper proposes a blockchain-based smart home system which consists in a home and a blockchain network part. The blockchain network with 8 nodes is implemented by HyperLeger Fabric platform on Docker. ECC(Elliptic Curve Cryptography) technology is used for data transmission security and RBAC(role-based access control) manages the certificates of network members. Raft consensus algorithm maintains data consistency across all nodes in a distributed system and reduces block generation time. The query and data submission are controlled by the smart contract which allows nodes to safely and efficiently access smart home data. The experimental results show that the proposed system maintains a stable average query and submit time of 84.5 [ms] and 93.67 [ms] under high concurrent accesses, respectively and the transmission data is secured through simulated packet capture attacks.