• Journal of Bio-Environment Control
• /
• v.10 no.1
• /
• pp.42-49
• /
• 2001

This study was carried out to develop a fuzzy control technique of ventilation window for controlling a temperature in a greenhouse. To reduce the fuzzy variables, the inside air temperature shop was taken as one of fuzzy variables, because the inside air temperature variation of a greenhouse by ventilation at the same window aperture is affected by difference between inside and outside air temperature, outside wind speed and the wind direction. Therefore, the antecedent variables for fuzzy algorithm were used the control error and its slop, which was same value as the inside air temperature slop during the control period, and the conclusion variable was used the window aperture opening rate. Through the basic and applicative control experiment with the control period of 3 minutes the optimum ranges of fuzzy variables were decided. The control error and its slop were taken as 3 and 1.5 times compared with target error in steady state, and the window opening rate were taken as 30% of full size of the window aperture. To evaluate the developed fuzzy algorithm in which the optimized 19 rules of fuzzy production were used, the performances of fuzzy control and PID control were compared. The temperature control errors by the fuzzy control and PID control were lower than 1.3$^{\circ}C$ and 2.2$^{\circ}C$ respectively. The accumulated operating size of the window, the number of operating and the number of inverse operating for the fuzzy control were 0.4 times, 0.5 times and 0.3 times of those compared with the PID control. Therefore, the fuzzy control can operating the window more smooth and reduce the operating energy by 1/2 times of PID control.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.2
• /
• pp.115-121
• /
• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Korean Journal of Clinical Laboratory Science
• /
• v.51 no.1
• /
• pp.78-85
• /
• 2019

Membrane proteins are involved in many common diseases, including heart disease and cancer. In various disease states, such as cancer, abnormal signaling pathways that are related to the membrane proteins cause the cells to divide out of control and the expression of membrane proteins can be altered. Membrane proteins have the hydrophobic environment of a lipid bilayer, which makes an analysis of the membrane proteins notoriously difficult. Therefore, this study evaluated the efficacy of two different methods for optimal membrane protein extraction. High-speed centrifuge and reagent-based method with a -/+ filter aided sample preparation (FASP) were compared. As a result, the high-speed centrifuge method is quite effective in analyzing the mitochondrial inner membranes, while the reagent-based method is useful for endoplasmic reticulum membrane analysis. In addition, the function of the membrane proteins extracted from the two methods were analyzed using GeneGo software. GO processes showed that the endoplasmic reticulum-related responses had higher significance in the reagent-based method. An analysis of the process networks showed that one cluster in the high-speed centrifuge method and four clusters in the reagent-based method were visualized. In conclusion, the two methods are useful for the analysis of different subcellular membrane proteins, and are expected to assist in selecting the membrane protein extraction method by considering the target subcellular membrane proteins for study.

• Progress in Medical Physics
• /
• v.24 no.1
• /
• pp.1-24
• /
• 2013

As image-guided radiation therapy (IGRT) has been commonly used for more accurate patient setup and monitoring tumor movement during radiation therapy, the necessity for management of imaging dose is increased. However, it has not been an interest issue to radiation therapy communities because the imaging dose is much lower than the therapeutic dose. However, since the cumulative dose from 4DCT and repeated imaging for daily setup verificationin would not be ignorable, appropriate dose management based on ALARA (As Low As Reasonably Achievable) principle is required. In this study, we aimed that (1) survey on imaging equipments and modalities used for IGRT, (2) estimation of IGRT imaging dose depending on treatment types and equipments, (3) collecting data of effective dose on treatment sites from each equipment and imaging protocol, and thus finally provide guideline for imaging dose reduction and optimization.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.71-84
• /
• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.18 no.8
• /
• pp.880-886
• /
• 2007

We have proposed a new "hybrid" envelope elimination and restoration(EER) transmitter architecture using an efficiency optimized power amplifier(PA) and modified bias modulator. The efficiency of the PA at the average drain voltage is very important for the overall transmitter efficiency because the PA operates mostly at the average power region of the modulation signal. Accordingly, the efficiency of the PA has been optimized at the region. Besides, the bias modulator has been accompanied with the emitter follower for the minimization of memory effect. A saturation amplifier, class $F^{-1}$ is built using a 5-W PEP LDMOSFET for forward-link single-carrier wideband code-division multiple-access(WCDMA) at 1-GHz. For the interlock experiment, the bias modulator has been built with the efficiency of 64.16% and peak output voltage of 31.8 V. The transmitter with the proposed PA and bias modulator has been achieved an efficiency of 44.19%, an improvement of 8.11%. Besides, the output power is enhanced to 32.33 dBm due to the class F operation and the PAE is 38.28% with ACLRs of -35.9 dBc at 5-MHz offset. These results show that the proposed architecture is a very good candidate for the linear and efficient high power transmitter.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2004.12a
• /
• pp.169-181
• /
• 2004

SK Telecom has already constructed GIMS system as the base common framework of LBS/GIS service system based on OGC(OpenGIS Consortium)'s international standard for the first mobile vector map service in 2002, But as service content appears more complex, renovation has been needed to satisfy multi-purpose, multi-function and maximum efficiency as requirements have been increased. This research is for preparation ion of GML3-based platform to upgrade service from GML2 based GIMS system. And with this, it will be possible for variety of application services to provide location and geographic data easily and freely. In GML 3.0, it has been selected animation, event handling, resource for style mapping, topology specification for 3D and telematics services for mobile LBS multimedia service. And the schema and transfer protocol has been developed and organized to optimize data transfer to MS(Mobile Stat ion) Upgrade to GML 3.0-based GIMS system has provided innovative framework in the view of not only construction but also service which has been implemented and applied to previous research and system. Also GIMS channel interface has been implemented to simplify access to GIMS system, and service component of GIMS internals, WFS and WMS, has gotten enhanded and expanded function.

• Journal of the East Asian Society of Dietary Life
• /
• v.12 no.2
• /
• pp.91-99
• /
• 2002

This study was carried out to develop a diabetic-oriented convenience flood using 7 medicinal plants (Schisandra chinensis, Coix lachryma-jobi, Dioscorea batatas, Ophipogon japonicus, Lyicium chinense, Houttuynia cordata, Polygonatum sibiricum) and chicken. Portion size was 310g, total calorie was 551.6 kcal and carbohydrate, lipid and protein were consisted of 53.0%, 20.9% and 26.1%, respectively. Calcium, zinc and iron content were 268.9mg, 5.4mg and 6.1mg, respectively. Crude fiber content was 22.9g. In sensory evaluation, the scores of taste, color, texture and overall acceptability were higher than normal diabetic meal. Hypoglycemic effect of the device meal for diabetic persons was excellent compared to that of normal diabetic meal. The above results indicate that the 7 medicinal plants can be used as functional ingredients fur diabetic-oriented convenience flood industry. Also, device meal can be used as ready-prepared food for weight control.