• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.329-338
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• 2007

The quality assurance issue of drug products is more important than the general product because it is highly related to the human health and life. In this reason, the regulatory guide lines have continuously been intensified all around the world. In order to achieve effective quality assurance and real-time product release (RTPR) of drug products, process analytical technology (PAT), which can analyze and control a manufacturing process, has been proposed from the United States. With the PAT process, we can obtain significant process features of materials, quality characteristics and product capabilities from a raw material to the final product in the real-time procedure. PAT can also be utilized to process validation using information system that can analyze the risk of drug products through out an entire product life-cycle. In this paper, we first offered a new concept for the off-line process design methods to prepare the improved quality assurance restrictions and a real-time control method by establishing an information system. We also introduced an automatic inspection system by obtaining surrogate variables based on drug product formulations. Finally, we proposed an advanced PAT concept using validation and feedback principles through out the entire life-cycle of drug product manufacturing processes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3044-3050
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• 2015

The purpose of this study is to analyze the performance of the flow control oil pump for automatic transmission. The numerical model for analysis the performance of the flow control oil pump was develop and the characteristics of the internal flow, discharge flow rate, displacement of outer ring, driving torque, generation of cavitation was investigated according to rotating speed. As a result, the cavitation generation increased as the rotating speed increased. The volumetric efficiency was 90% for 2200 rpm and it decreased rapidly, then it decreased about 81% for 5000 rpm. Besides, the cavitation generation was 20%~30% for inlet of suction part, but it reduced below 13% owing to the compression. However, it shows higher cavitation generation for high rotating speed like 5000 rpm.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• Journal of Convergence for Information Technology
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• v.9 no.7
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• pp.106-113
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• 2019

The purpose of this paper is aimed to derive suggestions for convergence technology for effective management of distance education by analyzing a long-term case. The experiment was designed with notebook, smartphone or tablet based robot controlled by a remote instructor and a learner, who have experience of distance learning including robot assisted learning. The tablet based robot has the display system of feedback to speakers. During five months, three types of experiments were conducted randomly and a participant was interviewed thoroughly. The result, like the previous research, demonstrates that the task performance of the learner in telepresence robot-assisted learning was better than that in the notebook, and smartphone based. However, it is believed to be necessary to adjust the system for eye-contact and voice transmission for the remote instructor. The instructor required an additional sight by supplementing an extra camera and automatic direction control to source of sound.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.117-122
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• 2023

This paper introduces the improved and expanded contents of the previous research that implemented a system that guides the visually impaired using the vibration of a smart watch. In the previous study, we proposed an algorithm that recommends the most safe route based on calculating negative and positive values for the topography of the route and the features installed on the route. In addition, by designing several vibration patterns, a system that guides the user through vibration was proposed and implemented. In this paper, the user's personal preferences are reflected in the algorithm that recommends the safe route first. In addition, an automatic feedback function was added to the algorithm so that the user's satisfaction evaluation on the recommended route was immediately reflected. A history simulation function was added so that the past route could be reviewed, and the compass of a smart watch was used to help visually impaired people find directions at junctions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.203-211
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• 2024

Utilizing programming languages for data visualization can enhance the efficiency and effectiveness in handling data volume, processing time, and flexibility. However, practice is required to become proficient in programming. Therefore public data-based the problem bank was developed to practice data visualization in a programming automatic assessment system. Public data were collected based on topics suggested in the curriculum and were preprocessed to make it suitable for users to visualize. The problem bank was associated with the mathematics curriculum to learn various data visualization methods. The developed problems were reviewed to expert and pilot testing, which validated the level of the questions and the potential of integrating data visualization in math education. However, feedback indicated a lack of student interest in the topics, leading us to develop additional questions using student-center data. The developed problem bank is expected to be used when students who have learned Python in primary school information gifted or middle school or higher learn data visualization.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3874-3884
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• 2000

An information retrieval system has to retrieve all and only documents which are relevant to a user query, even if index terms and query terms are not matched exactly. However, term mismatches between index terms and qucry terms have been a serious obstacle to the enhancement of retrieval performance. In this paper, we discuss automatic term normalization between words in text corpora and their application to a Korean information retrieval system. We perform two types of term normalizations to alleviate semantic term mismatches: equivalence class and co-occurrence cluster. First, transliterations, spelling errors, and synonyms are normalized into equivalence classes bv using contextual similarity. Second, context-based terms are normalized by using a combination of mutual information and word context to establish word similarities. Next, unsupervised clustering is done by using K-means algorithm and co-occurrence clusters are identified. In this paper, these normalized term products are used in the query expansion to alleviate semantic tem1 mismatches. In other words, we utilize two kinds of tcrm normalizations, equivalence class and co-occurrence cluster, to expand user's queries with new tcrms, in an attempt to make user's queries more comprehensive (adding transliterations) or more specific (adding spc'Cializationsl. For query expansion, we employ two complementary methods: term suggestion and term relevance feedback. The experimental results show that our proposed system can alleviatl' semantic term mismatches and can also provide the appropriate similarity measurements. As a result, we know that our system can improve the rctrieval efficiency of the information retrieval system.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1059-1071
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• 2008

Users in ubiquitous computing environment can easily access and use a multitude of devices and services anywhere and anytime. The key technology to realize this scenario is the method to intuitively provide proper user interfaces for each device and service. Previous attempts simply provided a designated user interface for each device and service or provided an abstract user interface to control common functions of different services. To select a target appliance, either user directly specified the target device or depended on sensors such as RFID tags and readers limiting the applicable scenarios. In this paper, we present UbiController which uniquely uses camera on the mobile device to recognize markers of appliances to acquire user interface for controlling task. UbiController aims to provide automatic discovery of multiple services in the smart home environment, support traditional GUI and novel camera-based recognition method as well as intuitive interaction methods for users. In this paper, we show experiments on the performance of UbiController's discovery and recognition methods and user feedback on interaction methods from a user study.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.