• The Korean Journal of Pain
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• v.20 no.2
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• pp.163-168
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• 2007

Background: Temporomandibular joint disorder (TMD) is a group of musculoskeletal conditions characterized by pain in the pre-auricular area, limitation of jaw movement and palpable muscle tenderness. Thermography is a nonionizing, noninvasive diagnostic alternative for the evaluation of TMD. This study was conducted to evaluate the usefulness of thermography in the assessment of TMD. Methods: Thermography was conducted on the 61 patients who had been diagnosed with TMD, and on the 34 normal symptom-free volunteers. The temperature differences between opposite sides of the temporomandibular joint (${\Delta}T_{TMJ}$) and the masseter muscle (${\Delta}T_{MST}$) were calculated. The sensitivity and specificity of thermography was calculated at the cut off values of 0.2, 0.3, and $0.4^{\circ}C$. Results: In the patient group, the ${\Delta}T_{TMJ}$ was $0.42{\pm}0.38^{\circ}C$ and the ${\Delta}T_{MST}$ was $0.38{\pm}0.33^{\circ}C$, whereas in the control group the ${\Delta}T_{TMJ}$ was $0.10{\pm}0.07^{\circ}C$ and the ${\Delta}T_{MST}\;0.15{\pm}0.10^{\circ}C$. In addition, the patient group demonstrated a significantly lower level of thermal symmetry than the control group (P < 0.001) in both the temporomandibular joints and the masseter muscles. The sensitivity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 67.2, 49.2, and 42.6% in the temporomandibular joint (TMJ) and 60.7, 49.2 and 37.7% in the masseter muscle, respectively. The specificity of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 88.2, 100, and 100% in the TMJ and 61.8, 91.2 and 100% in the masseter muscles, respectively. The accuracy of thermography at the cut off values of 0.2, 0.3 and $0.4^{\circ}C$ was 74.7, 67.4, and 63.2% in TMJ and 61.1, 64.2 and 60.0% in the masseter muscles, respectively. Conclusions: Temperature differences exist between the opposite sides of the TMD and masseter muscles in patients with TMD. Although the sensitivity of thermography in the diagnosis of TMD is low, it has high specificity in the evaluation of TMD, and is therefore applicable to patients with TMD.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.452-458
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• 2019

Coix lacryma-jobi var. ma-yuen (Rom. Caill.) Stapf (CL), which contains riboflavin and coixol, has traditionally been used to treat cancer and arthritis. However, no method for the simultaneous determination of riboflavin and coixol in CL sprouts has been established. In this study, we established and validated a high-performance liquid chromatography-diode array detection (HPLC-DAD) method for the identification and quantification of two reference markers, riboflavin and coixol, in CL sprout extracts. CL sprouts (whole sprouts and leaves) were subjected to extraction with 70% ethanol at room temperature and at 80 ℃ under reflux conditions. The two extractions were validated with respect to specificity, accuracy, precision, and linearity. The content of the two reference markers was highest in leaves extracted under reflux conditions (riboflavin, 8.23 ± 0.32 mg/g; coixol, 5.95 ± 0.04 mg/g). We also investigated the antioxidant activity of the extracts via 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS⁺) scavenging assays. The results indicated that extracts obtained from sprouts under reflux conditions had the strongest antioxidative effects (DPPH half maximal inhibitory concentration [IC₅₀], 68.9 ± 4.1 g/mL; and ABTS, IC₅₀, 34.9 ± 0.1 g/mL). These results can serve as baseline data for the simultaneous determination of the two reference marker compounds, riboflavin and coixol, and development of functional food materials using CL sprouts.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.288-294
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• 2011

BACKGROUND: Methylmercury is analyzed by HPLC-ICP/MS because of the simplicity for sample preparation and interference. However, most of the pre-treatment methods for methylmercury need a further pH adjustment of the extracted solution and removal of organic matter for HPLC. The purpose of this study was to establish a rapid and accurate analytical method for determination of methylmercury in fish by using HPLC-ICP/MS. METHOD AND RESULTS: We conducted an experiment for pre-treatment and instrument conditions and analytical method verification. Pre-treatment condition was established with aqueous 1% L-cysteine HCl and heated at $60^{\circ}C$ in microwave for 20 min. Methylmercury in $50{\mu}L$ of filtered extract was separated by a C18 column and aqueous 0.1% L-cysteine HCl + 0.1% L-cysteine mobile phase at $25^{\circ}C$. The presence of cysteine in mobile phase and sample solution was essential to eliminate adsorption, peak tailing and memory effect problems. Correlation coefficient($r^2$) for the linearity was 0.9998. The limits of detection and quantitation for this method were 0.15 and $0.45{\mu}g/kg$ respectively. CONCLUSION: Result for analytical method verification, accuracy and repeatability of the analytes were in good agreement with the certified reference materials values of methylmercury at a 95% confidence level. The advantage of the established method is that the extracted solution can be directly injected into the HPLC column without additional processes and the memory effect of mercury in the ICP-MS can be eliminated.

• Analytical Science and Technology
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• v.25 no.2
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• pp.121-126
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• 2012

It is essential to evaluate the quality of Hanji record paper without damaging the record paper by previous destructive methods. The samples were Hanji record paper produced in the 1900s. Near-infrared (NIR) spectrometer was used as a non destructive method for evaluating the quality of record papers. Fourier transform (FT) spectrometer was used with 12,500 to 4,000 $cm^{-1}$ wavenumber range for quantitative analysis and it has high accuracy and good signal-to-noise ratio. The acidity and moisture content of Hanji record paper were measured by integrating sphere as diffuse reflectance type. The acidity (pH) of chemical factors as a quality evaluated factor of Hanji was correlated to NIR spectrum. The NIR spectrum was pretreated to obtain the coefficients of optimum correlation. Multiplicative scatter correction (MSC) and First derivative of Savitzky-Golay were used as pretreated methods. The coefficients of optimum correlation were calculated by PLSR (partial least square regression). The correlation coefficients ($R^2$) of acidity had 0.92 on NIR spectra without pretreatment. Also the standard error of prediction (SEP) of pH was 0.24. And then the NIR spectra with pretreatment would have better correlation coefficient ($R^2$ = 0.98) and 0.19 as SEP on pH. For moisture contents, the linearity correlation without pretreatment was higher than the case with pretreatment (MSC, $1^{st}$ derivative). As the best result, the $R^2$ was 0.99 and SEP was 0.45. This indicates that it is highly proper to evaluate the quality of Hanji record papers speedily with integrated sphere and FT NIR analyzer as a non-destructive method.

• Journal of Animal Science and Technology
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• v.54 no.4
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• pp.267-274
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• 2012

The method of sexing based on differences in the rate of feather growth provides a convenient and inexpensive approach. The locus of feather development gene (K) is located on the Z chromosome and can be utilized to produce phenotypes that distinguish between the sexes of chicks at hatching. To establish the auto-sexing native chicken strains, this study analyzed the genotype frequency of the feathering in domestic chicken breeds. The method of classification of slow- and rapid-feathering chickens was also investigated. In the slow-feathering chicks, the coverts were either the same length or longer than the primary wing feathers at hatching. However, the rapid-feathering chicks had the primary wing feathers that were longer than the coverts. The growth pattern of tail feather also distinctively differed between the rapid- and slow-feathering chicks after 5-days. The accuracy of wing feather sexing was about 98% compared with tail sexing. In domestic chicken breeds, Korean Black Cornish, Korean Rhode Island Red, and Korean Native Chicken-Red had both dominant (K) and recessive ($k^+$) feathering genes. The other breeds of chickens, Korean Brown Cornish, Ogol, White Leghorn, Korean Native Chicken-Yellow, -Gray, -White and -Black had only the recessive feathering gene ($k^+$). Consequently, feather sexing is available using the domestic chicken breeds. Establishing the maternal stock with dominant gene (K-) and paternal stock with recessive gene ($k^+k^+$), the slow-feathering characteristic is passed from mothers to their sons, and the rapid-feathering characteristic is inherited by daughters from their fathers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.174-184
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• 2009

Evapotranspiration (ET) is one of the major hydrologic processes in terrestrial ecosystems. A reliable estimation of spatially representavtive ET is necessary for deriving regional water budget, primary productivity of vegetation, and feedbacks of land surface to regional climate. Moderate resolution imaging spectroradiometer (MODIS) provides an opportunity to monitor ET for wide area at daily time scale. In this study, we applied a MODIS-based ET algorithm and tested its reliability for nine flux tower sites in East Asia. This is a stand-alone MODIS algorithm based on the Penman-Monteith equation and uses input data derived from MODIS. Instantaneous ET was estimated and scaled up to daily ET. For six flux sites, the MODIS-derived instantaneous ET showed a good agreement with the measured data ($r^2=0.38$ to 0.73, ME = -44 to $+31W\;m^{-2}$, RMSE =48 to $111W\;m^{-2}$). However, for the other three sites, a poor agreement was observed. The predictability of MODIS ET was improved when the up-scaled daily ET was used ($r^2\;=\;0.48$ to 0.89, ME = -0.7 to $-0.6\;mm\;day^{-1}$, $RMSE=\;0.5{\sim}1.1\;mm\;day^{-1}$). Errors in the canopy conductance were identified as a primary factor of uncertainty in MODIS-derived ET and hence, a more reliable estimation of canopy conductance is necessary to increase the accuracy of MODIS ET.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.19-28
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• 2012

After the internet era, we are moving to the ubiquitous society. Nowadays the people are interested in the multimodal interaction technology, which enables audience to naturally interact with the computing environment at the exhibitions such as gallery, museum, and park. Also, there are other attempts to provide additional service based on the location information of the audience, or to improve and deploy interaction between subjects and audience by analyzing the using pattern of the people. In order to provide multimodal interaction service to the audience at the exhibition, it is important to distinguish the individuals and trace their location and route. For the location tracking on the outside, GPS is widely used nowadays. GPS is able to get the real time location of the subjects moving fast, so this is one of the important technologies in the field requiring location tracking service. However, as GPS uses the location tracking method using satellites, the service cannot be used on the inside, because it cannot catch the satellite signal. For this reason, the studies about inside location tracking are going on using very short range communication service such as ZigBee, UWB, RFID, as well as using mobile communication network and wireless lan service. However these technologies have shortcomings in that the audience needs to use additional sensor device and it becomes difficult and expensive as the density of the target area gets higher. In addition, the usual exhibition environment has many obstacles for the network, which makes the performance of the system to fall. Above all these things, the biggest problem is that the interaction method using the devices based on the old technologies cannot provide natural service to the users. Plus the system uses sensor recognition method, so multiple users should equip the devices. Therefore, there is the limitation in the number of the users that can use the system simultaneously. In order to make up for these shortcomings, in this study we suggest a technology that gets the exact location information of the users through the location mapping technology using Wi-Fi and 3d camera of the smartphones. We applied the signal amplitude of access point using wireless lan, to develop inside location tracking system with lower price. AP is cheaper than other devices used in other tracking techniques, and by installing the software to the user's mobile device it can be directly used as the tracking system device. We used the Microsoft Kinect sensor for the 3D Camera. Kinect is equippedwith the function discriminating the depth and human information inside the shooting area. Therefore it is appropriate to extract user's body, vector, and acceleration information with low price. We confirm the location of the audience using the cell ID obtained from the Wi-Fi signal. By using smartphones as the basic device for the location service, we solve the problems of additional tagging device and provide environment that multiple users can get the interaction service simultaneously. 3d cameras located at each cell areas get the exact location and status information of the users. The 3d cameras are connected to the Camera Client, calculate the mapping information aligned to each cells, get the exact information of the users, and get the status and pattern information of the audience. The location mapping technique of Camera Client decreases the error rate that occurs on the inside location service, increases accuracy of individual discrimination in the area through the individual discrimination based on body information, and establishes the foundation of the multimodal interaction technology at the exhibition. Calculated data and information enables the users to get the appropriate interaction service through the main server.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• The KIPS Transactions:PartA
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• v.13A no.2 s.99
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• pp.123-136
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• 2006

The prefetching technique is an effective way to reduce the latency caused memory access. However, excessively aggressive prefetch not only leads to cache pollution so as to cancel out the benefits of prefetch but also increase bus traffic leading to overall performance degradation. In this thesis, a prefetch filtering scheme is proposed which dynamically decides whether to commence prefetching by referring a filtering table to reduce the cache pollution due to unnecessary prefetches In this thesis, First, prefetch hashing table 1bitSC filtering scheme(PHT1bSC) has been shown to analyze the lock problem of the conventional scheme, this scheme such as conventional scheme used to be N:1 mapping, but it has the two state to 1bit value of each entries. A complete block address table filtering scheme(CBAT) has been introduced to be used as a reference for the comparative study. A prefetch block address lookup table scheme(PBALT) has been proposed as the main idea of this paper which exhibits the most exact filtering performance. This scheme has a length of the table the same as the PHT1bSC scheme, the contents of each entry have the fields the same as CBAT scheme recently, never referenced data block address has been 1:1 mapping a entry of the filter table. On commonly used prefetch schemes and general benchmarks and multimedia programs simulates change cache parameters. The PBALT scheme compared with no filtering has shown enhanced the greatest 22%, the cache miss ratio has been decreased by 7.9% by virtue of enhanced filtering accuracy compared with conventional PHT2bSC. The MADT of the proposed PBALT scheme has been decreased by 6.1% compared with conventional schemes to reduce the total execution time.