• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.985-996
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• 2006

As the internet technologies develop, the geographic information system environment is changing to the web-based service. Since geospatial information of the existing Web-GIS services were developed independently, there is no interoperability to support diverse map formats. In spite of the same geospatial information object it can be used for various proposes that is duplicated in GIS separately. It needs intelligent strategies for optimal replica selection, which is identification of replication geospatial information objects. And for management of replication objects, OMG, GLOBE and GRID computing suggested related frameworks. But these researches are not thorough going enough in case of geospatial information object. This paper presents a model of location service, which is supported for optimal selection among replication and management of replication objects. It is consist of tree main services. The first is binding service which can save names and properties of object defined by users according to service offers and enable clients to search them on the service of offers. The second is location service which can manage location information with contact records. And obtains performance information by the Load Sharing Facility on system independently with contact address. The third is intelligent selection service which can obtain basic/performance information from the binding service/location service and provide both faster access and better performance characteristics by rules as intelligent model based on rough sets. For the validity of location service model, this research presents the processes of location service execution with Graphic User Interface.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.257-264
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• 2014

Purpose : Photoneutron dose in high-energy photon radiotherapy at linear accelerator increase the risk for secondary cancer. The purpose of this investigation is to evaluate the dose variation of photoneutron with different treatment method, flattening filter, dose rate and gantry angle in radiation therapy with high-energy photon beam ($E{\geq}8MeV$). Materials and Methods : TrueBeam $ST{\time}TM$(Ver1.5, Varian, USA) and Korea Tissue Equivalent Proportional Counter (KTEPC) were used to detect the photoneutron dose out of the high-energy photon field. Complex Patient plans using Eclipse planning system (Version 10.0, Varian, USA) was used to experiment with different treatment technique(IMRT, VMAT), condition of flattening filter and three different dose rate. Scattered photoneutron dose was measured at eight different gantry angles with open field (Field size : $5{\time}5cm$). Results : The mean values of the detected photoneutron dose from IMRT and VMAT were $449.7{\mu}Sv$, $2940.7{\mu}Sv$. The mean values of the detected photoneutron dose with Flattening Filter(FF) and Flattening Filter Free(FFF) were measured as $2940.7{\mu}Sv$, $232.0{\mu}Sv$. The mean values of the photoneutron dose for each test plan (case 1, case 2 and case 3) with FFF at the three different dose rate (400, 1200, 2400 MU/min) were $3242.5{\mu}Sv$, $3189.4{\mu}Sv$, $3191.2{\mu}Sv$ with case 1, $3493.2{\mu}Sv$, $3482.6{\mu}Sv$, $3477.2{\mu}Sv$ with case 2 and $4592.2{\mu}Sv$, $4580.0{\mu}Sv$, $4542.3{\mu}Sv$ with case 3, respectively. The mean values of the photoneutron dose at eight different gantry angles ($0^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$, $180^{\circ}$, $225^{\circ}$, $270^{\circ}$, $315^{\circ}$) were measured as $3.2{\mu}Sv$, $4.3{\mu}Sv$, $5.3{\mu}Sv$, $11.3{\mu}Sv$, $14.7{\mu}Sv$, $11.2{\mu}Sv$, $3.7{\mu}Sv$, $3.0{\mu}Sv$ at 10MV and as $373.7{\mu}Sv$, $369.6{\mu}Sv$, $384.4{\mu}Sv$, $423.6{\mu}Sv$, $447.1{\mu}Sv$, $448.0{\mu}Sv$, $384.5{\mu}Sv$, $377.3{\mu}Sv$ at 15MV. Conclusion : As a result, it is possible to reduce photoneutron dose using FFF mode and VMAT method with TrueBeam $ST{\time}TM$. The risk for secondary cancer of the patients will be decreased with continuous evaluation of the photoneutron dose.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.4
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• pp.457-468
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• 1995

In order to develop a more practical culture system from the present running seawater tank system, two experiments of environmental factors, growth, survival rate and rearing density of olive flounder (Paralichthys olivaceus) were carried out for two consecutive years. Two groups of fish in initially averaging 7.5cm of total length, and 3.4g of body weight (EXP. I) and 5.0cm and 1.8g (EXP. II) were reared in the semi-closed recirculating seawater system equipped with the rotating biological contactors with the commercial culture scale. The dissolved inorganic nitrogen concentrations is EXP. I ranged 0,247-0.512 ppm of $NH_4-N$ (0.010-0.043 ppm of$NO_2-N$, and 0.108-0.342 ppm of $NO_3-N$, and those in EXP. II were 0.091-0.715 ppm, 0.002-0.045 ppm, and 0.007-0.277 ppm, respectively. Daily feeding rates of the fish were $0.67-2.41\%$ in EXP. I and $0.69-2.22\%$ in EXP_ II, and teed efficiency were $34.8-59.8\%\;and\;40.5-88.4\%$ in EXP. I and II, respectively. The average total ten說h and body weight were 40.0-42.8cm and 695.0-852.69g after 340 days culture in EXP. I, and 36.7-39.7cm and 552.4-706.4 g after 365 days culture in EXP. II, respectively. Survival rates of the fish at the end of EXP. I and II were $92.0\%\;and\;96.0\%,$ respectively. The ratio to body surface area of non-ocular side in all fish to bottom area of rearing tank, so-called covering rate, was used as an indicator of rearing density. The highest cowering rate and weight density of fish per $m^2$ of rearing tank at the end of experimental period were 2.2 and 34.1kg in EXP. I, and 2.6 and 36.3kg in EXP. II, respectively. For the commercial culture of olive flounder, the semi-closed recirculating seawater system was found to be more effective than the running seawater tank system in aspect to the fish productivity and protection of marine environment.

• Korean Chemical Engineering Research
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• v.52 no.5
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• pp.592-602
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• 2014

In the Integrated Gasification Combined Cycle (IGCC), the stability of the gasifier has strong influences on the rest of the plant as it supplies the feed to the rest of the power generation system. In order to ensure a safe and stable operation of the entrained-flow gasifier and for protection of the gasifier wall from the high internal temperature, the solid slag layer thickness should be regulated tightly but its control is hampered by the lack of on-line measurement for it. In this study, a previously published dynamic simulation model of a Shell-type gasifier is reproduced and two different linear model predictive control strategies are simulated and compared for multivariable control of the entrained-flow gasifier. The first approach is to control a measured secondary variable as a surrogate to the unmeasured slag thickness. The control results of this approach depended strongly on the unmeasured disturbance type. In other words, the slag thickness could not be controlled tightly for a certain type of unmeasured disturbance. The second approach is to estimate the unmeasured slag thickness through the Kalman filter and to use the estimate to predict and control the slag thickness directly. Using the second approach, the slag thickness could be controlled well regardless of the type of unmeasured disturbances.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing a portable gas analyzer (K4B2), an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• Journal of the Korean Chemical Society
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• v.52 no.3
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• pp.273-280
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• 2008

Mycobacterium tuberculosis (MTB) remains a major worldwide public health problem. In recent years, the incidence of MTB has been rising. Rapid and reliable diagnosis of Mycobacterium tuberculosis is essential to initiate correct treatment, avoid severe complications, and prevent transmission. LAMP was used to develop a rapid and sensitive laboratory diagnostic system for the MTB. In this research, the loop-mediated isothermal amplification method (LAMP) that amplifies DNA with high specificity and rapidity at an isothermal condition was evaluated for rapid detection of MTB. Undiluted DNA (2.10 × 106 copy/mL), 10-1, 10-2, 10-3, 10-4, 10-5 and 10-6 (copy/mL) of MTB DNA were amplified by PCR and LAMP to determine the sensitivity of the assay. At results, the LAMP assay reported here has the advantages of rapid amplification, high sensitivity, and high specificity and will be useful for rapid and reliable clinical diagnosis of MTB in hospital clinical laboratory.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.95-105
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• 2003

A novel reference point detection method is proposed by exploiting tile gradient probabilistic model that captures the curvature information of fingerprint. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in a probabilistic sense. The uniformly distributed gradient texture represents either the core point itself or those of similar points that can be used to establish the rigid reference from which to map the features for recognition. Key benefits are reductions in preprocessing and consistency of locating the same points as the reference points even when processing arch type fingerprints. Moreover, the new feature extraction method is proposed by improving the existing feature extraction using filterbank method. Experimental results indicate the superiority of tile proposed scheme in terms of computational time in feature extraction and verification rate in various noisy environments. In particular, the proposed gradient probabilistic model achieved 49% improvement under ambient noise, 39.2% under brightness noise and 15.7% under a salt and pepper noise environment, respectively, in FAR for the arch type fingerprints. Moreover, a reduction of 0.07sec in reference point detection time of the GPM is shown possible compared to using the leading the poincare index method and a reduction of 0.06sec in code extraction time of the new filterbank mettled is shown possible compared to using the leading the existing filterbank method.