• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of the Korean Wood Science and Technology
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• v.34 no.4
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• pp.37-45
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• 2006

Recently, wood-framed houses has been built in the Korea for pension. Wood is good material for human healthy, while the construction lumbers are treated with preservative such as CCA (chromated copper arsenate), which contain some toxic elements for human body. However, if the waste woody biomass treated with various heavy metals, which has been collected from house construction or demolition, was fired in the field, and incinerated or landfilled after mass collection, such components will result in the toxic air pollutants in the burning or land fills, and spreaded into other areas. So the careful selection of wood and chemicals are required in advance for house construction, in particular, for environment-friendly housings. Therefore, this study was carried out to determine the content of toxic heavy metals in woody materials such as domestic hinoki and imported hemlock treated with CCA for housing materials, and the post-treated wood components such as organic fertilizer, sludge, dry-distilled charcoal and carbonized charcoal, to be returned finally into soil. The results are as follows. 1) The chemical analysis of toxic trace elements in various solid biomass required accurate control and management of laboratory environment, and reagents and water used, because of the error of data due to various foreign substances added in various processing and transporting steps. So a systematic analyzers was necessary to monitor the toxic pollutants of construction materials. 2) In particular, the biomass treated with industrial biological or thermal conditions such as sludge or charcoals was not fully dissolvable after third addition of $HNO_3$ and HF. 3) The natural woody materials such as organic fertilizer, sludge. and charcoals without any treatment of preservatives or heavy metal components were nontoxic in landfill because of the standard of organic fertilizers, even after thermal or biological treatments. 4) The CC A-treated wood for making the construction wood durable should not be landfilled, because of its higher contents of toxic metals than the criterion of organic fertilizer for agriculture or of natural environment. So the demolished waste should be treated separately from municipal wastes.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.547-553
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• 2017

In the current medical field, lead is widely used as a radiation shield. However, the lead weight is very heavy, so wearing protective clothing such as apron is difficult to wear for long periods of time and there is a problem with the danger of lethal toxicity in humans. Recently, many studies have been conducted to develop substitute materials of lead to resolve these problems. As a substitute materials for lead, barium(Ba) and iodine(I) have excellent shielding ability. But, It has characteristics emitting characteristic X-rays from the energy area near 30 keV. For patients or radiation workers, shielding materials is often made into contact with the human body. Therefore, the characteristic X-rays generated by the shielding material are directly exposured in the human body, which increases the risk of increasing radiation absorbed dose. In this study, we have developed the FLUKA transport code, one of the most suitable elements of radiation transport codes, to remove the characteristic X-rays generated by barium or iodine. We have verified the reliability of the shielding fraction of the structure of the structure shielding by comparing with the MCPDX simulations conducted as a prior study. Using the MCNPX and FLUKA, the double layer shielding structures with the various thickness combination consisting of barium sulphate ($BaSO_4$) and bismuth oxide($Bi_2O_3$) are designed. The accuracy of the type shown in IEC 61331-1 was geometrically identical to the simulation. In addition, the transmission spectrum and absorbed dose of the shielding material for the successive x-rays of 120 kVp spectra were compared with lead. In results, $0.3mm-BaSO_4/0.3mm-Bi_2O_3$ and $0.1mm-BaSO_4/0.5mm-Bi_2O_3$ structures have been absorbed in both 33 keV and 37 keV characteristic X-rays. In addition, for high-energy X-rays greater than 90 keV, the shielding efficiency was shown close to lead. Also, the transport code of the FLUKA's photon transport code was showed cut-off on low-energy X-rays(below 33keV) and is limited to computerized X-rays of the low-energy X-rays. But, In high-energy areas above 40 keV, the relative error with MCNPX was found to be highly reliable within 6 %.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.59-70
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• 2018

With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.

• Radiation Oncology Journal
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• v.16 no.3
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• pp.325-335
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• 1998

Purpose : Since the mid cranial fossa is composed of various thickness of bone, the tissue inhomogeneity caused by bone would produce dose attenuation in cobalt-60 gamma knife irradiation. The correction factor for bone attenuation of cobalt-60 which is used for gamma knife source is -3.5$\%$. More importantly, nearly all the radiosurgery treatment planning systems assume a treatment volume of unit density: any perturbation due to tissue inhomogeneity is neglected, This study was performed to confirm the bone attenuation in mid cranial fossa using gamma knife. Materials and Methods : Computed tomography was performed after Leksell stereotactic frame had been liked to the Alderson Rando Phantom (human phantom) skull area. Kodak X-omat V film was inserted into two sites of pituitary adenoma point and acoustic neurinoma point, and irradiated by gamma knife with 14mm and 18mm collimator. An automatic scanning densitometer with a 1mm aperture is used to measure the dose profile along the x and y axis. Results : Isodose curve constriction in mid cranial fossa is observed with various ranges. Pituitary tumor point is greater than acoustic neurinoma point (0.2-3.0 mm vs 0.1-1.3 mm) and generally 14 mm collimator is greater than 18mm collimator (0.4-3.0 mm vs. 0.2-2.2 mm) Even though the isodose constriction is found, constriction of 50$\%$ isodose curve which is used for treatment reference line does not exceed 1 mm. This range is too small to influence the treatment planning and treatment results. Conclusion : Radiosurgery planning system of gamma knife does not show significant error to be corrected without consideration of bone attenuation.

• Journal of Life Science
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• v.21 no.1
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• pp.119-126
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• 2011

There is a growing recognition of the significance of $H_2S$ as a biological signaling molecule involved in vascular and nervous system functions. In mammals, two enzymes in the transsulfuration pathway, cystathionine ${\beta}$-synthase (CBS) and cystathionine ${\gamma}$-lyase (CGL), are believed to be chiefly responsible for $H_2S$ biogenesis. Genetic inborn error of CGL leads to human genetic disease, cystathioninuria, by accumulating cystathionine in the body. This disease is secondarily associated with a wide range of diseases including diabetes insipidus and Down's syndrome. Although the human CGL (hCGL) overexpression is essential for the investigation of its function, structure, reaction specificity, substrate specificity, and protein-protein interactions, there is no clear report concerning optimum overexpression conditions. In this study, we report a detailed analysis of the overexpression conditions of the hCGL using a bacterial system. Maximum overexpression was obtained in conditions of low culture temperature after inducer addition, performing low aeration during overexpression, and using a low concentration inducer (0.1 mM, IPTG) for induction. Expressed hCGL was purified by His-tag affinity column chromatography and confirmed by Western blot using hCGL antibody and enzyme activity analysis. We also report that the His tag with TEV site attached protein exhibits 76% activity for ${\alpha}-{\gamma}$ elimination reaction with L-cystathionine and 88% for ${\alpha}-{\beta}$ elimination reaction with L-cysteine compared to those of wild type hCGL, respectively. His tag with TEV site attached protein also exhibits a 420 nm absorption maximum, which is attributed to the binding cofactor, pyridoxal 5'-phosphate (PLP).

• Archives of design research
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• v.18 no.3 s.61
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• pp.85-94
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• 2005

Most of product designers use 3D CAD system as a inevitable design tool nowadays and many new products are developed through a concurrent engineering process. However, it is very difficult for novice designers to get the sense of reality from modeling objects shown in the computer screens. Such a intangibility problem comes from the lack of haptic interactions and contextual information about the real space because designers tend to do 3D modeling works only in a virtual space of 3D CAD system. To address this problem, this research investigate the possibility of a interactive quantified structure simulation for product design using AR(augmented reality) which can register a 3D CAD modeling object on the real space. We built a quantified structure simulation system based on AR and conducted a series of experiments to measure how accurately human perceive and adjust the size of virtual objects under varied experimental conditions in the AR environment. The experiment participants adjusted a virtual cube to a reference real cube within 1.3% relative error(5.3% relative StDev). The results gave the strong evidence that the participants can perceive the size of a virtual object very accurately. Furthermore, we found that it is easier to perceive the size of a virtual object in the condition of presenting plenty of real reference objects than few reference objects, and using LCD panel than HMD. We tried to apply the simulation system to identify preference characteristics for the appearance design of a home-service robot as a case study which explores the potential application of the system. There were significant variances in participants' preferred characteristics about robot appearance and that was supposed to come from the lack of typicality of robot image. Then, several characteristic groups were segmented by duster analysis. On the other hand, it was interesting finding that participants have significantly different preference characteristics between robot with arm and armless robot and there was a very strong correlation between the height of robot and arm length as a human body.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.17-24
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• 2013

This study estimate radiation biological danger factor by measuring patient's exposed dose and propose the low way of patient's exposed dose in panoramic radiography. We seek correcting constant of OSL dosimeter for minimize the error of exposed dose's measurement and measure the Left, Right crystalline lens, thyroid, directly included upper, lower lips, the maxillary bone and the center of photographing that indirect included in panoramic radiography by using the human body model standard phantom advised in ICRP. In result, the center of photographing's level of radiation maximum value is $413.67{\pm}6.53{\mu}Gy$ and each upper, lower lips is $217.80{\pm}2.98{\mu}Gy$, $215.33{\pm}2.61{\mu}Gy$. Also in panoramic radiography, indirect included Left, Right crystalline lens's level of radiation are $30.73{\pm}2.34{\mu}Gy$, $31.87{\pm}2.50{\mu}Gy$, and thyroid's level of measured exposed dose can cause effect of radiation biological and we need justifiable analysis about radiation defense rule and substantiation advised international organization for the low way of patient's exposed dose in panoramic radiography of dental clinic and we judge need the additional study about radiation defense organization for protect the systematize protocol's finance and around internal organs for minimize until accepted by many people that is technological, economical and social fact by using panoramic measurement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.