• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.105-111
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• 2009

The study aims to propose the intelligent clustering technique that calculates the distance by improving the problems of multi-hop clustering technique for inter-vehicular secure communications. After calculating the distance between vehicles with no connection for rapid transit and clustering it, the connection between nodes is created through a set distance vale. Header is selected by the distance value between nodes that become the identical members, and the information within a group is transmitted to the member nodes. After selecting the header, when the header is separated due to its mobility, the urgent situation may occur. At this time, the information transfer is prepared to select the new cluster header and transmit it through using the intelligent cluster provided from node by the execution of programs included in packet. The study proposes the cluster technique of the intelligent distance estimation for the mobile Ad-hoc network that calculates the cluster with the Store-Compute-Forward method that adds computing ability to the existing Store-and-Forward routing scheme. The cluster technique of intelligent distance estimation for the mobile Ad-hoc network suggested in the study is the active and intelligent multi-hop cluster routing protocol to make secure communications.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.151-160
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• 2023

Cyanobacteria Pseudanabaena strains are known to produce 2-MIB(odorous material) in freshwater systems, thereby causing problems in water use. However, their physiological responses to environmental factors in relation with 2-MIB production is not well explored. This study was conducted to evaluate the effect of temperature on the growth and 2-MIB production of Pseudanabaena redekei. The experimental cyanobacteria strains were separated from the Uiam Reservoir (North Han River) and cultured in the BG-11 medium. Temperature was set to 10, 15, 20, 25, and 30℃ for the experiment, in the reflection of the seasonal water temperature variation in situ. For each temperature treatment, cyanobacterial biomass(Chl-a) and 2-MIB concentration (intra-cellular and extra-cellular fractions) were measured every 2 days for 18 days. Both maximal growth and total 2-MIB production of P. redekei appeared at 30℃. While intra-cellular 2-MIB contents were similar (26~29 ng L^-1) regardless of treated temperatures, extra-cellular 2-MIB concentration was higher only in high temperature conditions (25~30℃), indicating that the extents of 2-MIB biosynthesis and release by P. redekei vary with temperature. The 2-MIB productivity of P. redekei was much higher in low-temperature conditions (10~15℃) than high temperature conditions (25~30℃). This study demonstrated that temperature was a critical factor contributing to 2-MIB biosynthesis and its release in cell growth (r=0.605, p<0.01). These results are important to understand the dynamics of 2-MIB in the field and thereby provide basic information for managing odorous material in drinking water resources.

• Clean Technology
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• v.30 no.1
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• pp.28-36
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• 2024

As demand for electric vehicles increases, the market for lithium-ion batteries is also rapidly increasing. The battery life of lithium-ion batteries is limited, so waste lithium-ion batteries are inevitably generated. Accordingly, lithium was selectively preleached from waste lithium iron phosphate (LiFePO₄, hereafter referred to as the LFP) cathode material powder among lithium ion batteries, and iron phosphate (FePO₄) powder was recovered. The recovered iron phosphate powder was mixed with alkaline sodium carbonate (Na₂CO₃) powder and heat treated to confirm its crystalline phase. The heat treatment temperature was set as a variable, and then the leaching rate and powder characteristics of each ingredient were compared after water leaching using Di-water. In this study, lithium showed a leaching rate of approximately 100%, and in the case of powder heat-treated at 800 ℃, phosphorus was leached by approximately 99%, and the leaching residue was confirmed to be a single crystal phase of Fe₂O₃. Therefore, in this study, lithium, phosphorus, and iron components were individually separated and recovered from waste LFP powder.

• The Journal of Korean Society for Radiation Therapy
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• v.23 no.1
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• pp.1-6
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• 2011

Purpose: Immobilizations used in order to maintain the reproducibility of a patient set-up and the stable posture for a long period are important more than anything else for the accurate treatment when the stereotactic body radiotherapy is underway. So the purpose of this study is to adapt the optimum immobilizations for the stereotactic body radiotherapy by comparing two commercial immobilizations with the self-manufactured immobilizations. Materials and Methods: Five people were selected for the experiment and three different immobilizations (A: Wing-board, B: BodyFix system, C: Arm up holder with vac-lock) were used to each target. After deciding on the target's most stable respiratory cycles, the targets were asked to wear a goggle monitor and maintain their respiration regularly for thirty minutes to obtain the respiratory signals. To analyze the respiratory signal, the standard deviation and the variation value of the peak value and the valley value of the respiratory signal were separated by time zone with the self-developed program at the hospital and each tie-downs were compared for the estimation by calculating a comparative index using the above. Results: The stability of each immobilizations were measured in consideration of deviation changes studied in each respiratory time lapse. Comparative indexes of each immobilizations of each experimenter are shown to be A: 11.20, B: 4.87, C: 1.63 / A: 3.94, B: 0.67, C: 0.13 / A: 2.41, B: 0.29, C: 0.04 / A: 0.16, B: 0.19, C: 0.007 / A: 35.70, B: 2.37, C: 1.86. And when all five experimenters wore the immobilizations C, the test proved the most stable value while four people wearing A and one man wearing D expressed relatively the most unstable respiratory outcomes. Conclusion: The self-developed immobilizations, so called the arm up holder vac-lock for the stereotactic body radiotherapy is expected to improve the effect of the treatment by decreasing the intra-fraction organ motions because it keeps the respiration more stable than other two immobilizations. Particularly in case of the stereotactic body therapy which requires the maintenance of set-up state for a long time, the self-developed immobilizations is thought to more useful for stereotactic body radiotherapy rather than the rest two immobilizations with instable respiratory cycle as time passes.

• 한국노년학
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• v.31 no.2
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• pp.355-369
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• 2011

The purpose of this study was to find the effects of long-term combined exercise on regional bone mineral density(BMD) and cardiovascular disease(CVD) risk factors in the elderly with osteoporosis(OP). For the purpose, the subjects of this study were separated by two groups with thirty-one elderly women, who the first group was combined exercise group(CEG, n=16) and second group was non exercise group(CON, n=15). The combined exercise program was made up of warm-up (10min), work-out (aerobic; 30~45min/HRR 40~60%, resistance; 1RM * 50-70%, 8-10 * 2set ~ 10-15 * 1set), and cool-down (10min). Exercise group of the inspection have been trained 5 times a week for 1years. The results : At first, the variables of regional BMD were significantly different to pelvis, spine, trunk and T-score in two groups. At second, the variables of CVD risk factors were significantly different to SBP and DBP as well as TC, TG, LDL-C and HDL-C in two groups. As results of these conclusion, this study have positively effect shown that CEG was superior to CON in regional BMD(pelvis, spine, trunk and T-score), blood pressure(SBP, DBP) and plasma lipids(TC, TG, and LDL-C). Especially, the long-term combined exercise was provides a striking overall health quality of life with improving BMD and reduced CVD risk factors in the elderly with OP. In the future, other researches should deal with specific measures that reduction in mortality due to chronic disease and improvement quality of life for the development of programs in multiple researches of osteoporosis and chronic diseases.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Korean Journal of Food And Nutrition
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• v.14 no.6
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• pp.548-561
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• 2001

Listeria spp. from sea water, fishes and shellfishes have been troubled in many countries. So we exam ined its distribution rates, biochemical characteristics of a separated strain, growth curve of pH at set times to 4 species of standard strain, and yes or no of growth inhibition for precautionary measure of food poisoning by L. monocytogenes, garlic, mustard, wasabi, and green tea extracts including sensitivity of antibiotics 10 species. As its results, check numbers of its positivity to Listeria spp. were 32 species in total examination body 200 species, and its isolation rates were 16%, L. innocua was 14.0%, L. monocytogenes 1.0%, and L. seeligeri 1.0% by the strain species. All the standard strain of 4 species showed growth inhibition bellow pH 3.0, its pH conditions of the optimum growth at 7.0∼8.0, and its growth was more active in alkali co]tuition than in acid condition. Its growth inhibition examination by garlic extracts had an the worst effects with O.D values of 0.078∼0.210. But the case of mustard and wasabi had weakened effect, and the case of green tea had some effect as the time went by. The results of sensitivity examination of antibiotics 10 species were as fellows. L. innocua of the 16 cases showed sensitivity of 100% in all 5 species, Ampicillin, etc, and Ciprofloxacin showed sensitivity of 43.7% and gentamicin, 93.7%. But tetracycline showed tolerance of 31.3% , cefotaxine. 75%, nalidixic acid, 100%. L. monocytogenes of the 6 cases showed sensitivity of 100% in all 6 species, ciprofloxacin, etc.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.113-130
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• 2002

In this paper, we developed DNL(Dynamic Network Loading) model based on Moving cell theory to analyze the dynamic characteristics of traffic flow in congested network. In this paper vehicles entered into link at same interval would construct one cell, and the cells moved according to Cell following rule. In the past researches relating to DNL model a continuous single link is separated into two sections such as running section and queuing section to describe physical queue so that various dynamic states generated in real link are only simplified by running and queuing state. However, the approach has some difficulties in simulating various dynamic flow characteristics. To overcome these problems, we present Moving cell theory which is developed by combining Car following theory and Lagrangian method mainly using for the analysis of air pollutants dispersion. In Moving cell theory platoons are represented by cells and each cell is processed by Cell following theory. This type of simulation model is firstly presented by Cremer et al(1999). However they did not develop merging and diverging model because their model was applied to basic freeway section. Moreover they set the number of vehicles which can be included in one cell in one interval so this formulation cant apply to signalized intersection in urban network. To solve these difficulties we develop new approach using Moving cell theory and simulate traffic flow dynamics continuously by movement and state transition of the cells. The developed model are played on simple network including merging and diverging section and it shows improved abilities to describe flow dynamics comparing past DNL models.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.123-126
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• 2012

This paper proposed a method to separate a liver into left and right liver lobes for simple and exact volumetry of the river graft at abdominal MDCT(Multi-Detector Computed Tomography) image before the living donor liver transplantation. A medical team can evaluate an accurate river graft with minimized interaction between the team and a system using this algorithm for ensuring donor's and recipient's safe. On the image of segmented liver, 2 points(PMHV: a point in Middle Hepatic Vein and PPV: a point at the beginning of right branch of Portal Vein) are selected to separate a liver into left and right liver lobes. Middle hepatic vein is automatically segmented using PMHV, and the cutting line is decided on the basis of segmented Middle Hepatic Vein. A liver is separated on connecting the cutting line and PPV. The volume and ratio of the river graft are estimated. The volume estimated using 2 points are compared with a manual volume that diagnostic radiologist processed and estimated and the weight measured during surgery to support proof of exact volume. The mean ${\pm}$ standard deviation of the differences between the actual weights and the estimated volumes was $162.38cm^3{\pm}124.39$ in the case of manual segmentation and $107.69cm^3{\pm}97.24$ in the case of 2 points method. The correlation coefficient between the actual weight and the manually estimated volume is 0.79, and the correlation coefficient between the actual weight and the volume estimated using 2 points is 0.87. After selection the 2 points, the time involved in separation a liver into left and right river lobe and volumetry of them is measured for confirmation that the algorithm can be used on real time during surgery. The mean ${\pm}$ standard deviation of the process time is $57.28sec{\pm}32.81$ per 1 data set ($149.17pages{\pm}55.92$).