• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.3-14
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• 2006

Music summarization means a technique which automatically generates the most importantand representative a part or parts ill music content. The techniques of music summarization have been studied with two categories according to summary characteristics. The first one is that the repeated part is provided as music summary and the second provides the combined segments which consist of segments with different characteristics as music summary in music content In this paper, we propose and evaluate two kinds of music summarization techniques. The algorithm using multi-level vector quantization which provides a repeated part as music summary gives fixed-length music summary is evaluated by overlapping ration between hand-made repeated parts and automatically generated summary. As results, the overlapping ratios of conventional methods are 42.2% and 47.4%, but that of proposed method with fixed-length summary is 67.1%. Optimal length music summary is evaluated by the portion of overlapping between summary and repeated part which is different length according to music content and the result shows that automatically-generated summary expresses more effective part than fixed-length summary with optimal length. The cluster-based algorithm using 2-D similarity matrix and k-means algorithm provides the combined segments as music summary. In order to evaluate this algorithm, we use MOS test consisting of two questions(How many similar segments are in summarized music? How many segments are included in same structure?) and the results show good performance.

• Korean journal of applied entomology
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• v.53 no.4
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• pp.375-380
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• 2014

The sweetpotato whitefly (SPW), Bemisia tabaci Gennadius, is a major pest in tomato greenhouses on Jeju Island because they transmit viral diseases. To develop practical sampling methods for adult SPWs, yellow-color sticky traps were used in commercial tomato greenhouses throughout the western part of Jeju Island in 2011 and 2012. On the basis of the size and growing conditions in the tomato greenhouses, 20 to 30 traps were installed in each greenhouse for developing a sampling plan. Adult SPWs were more attracted to horizontal traps placed 60 cm above the ground than to vertical trap placed 10 cm above the plant canopy. The spatial patterns of the adult SPWs were evaluated using Taylor's power law (TPL) and Iwao's patchiness regression (IPR). The results showed that adult SPWs were aggregated in each surveyed greenhouse. In this study, TPL showed better performance because of the coefficient of determination ($r^2$). On the basis of the fixed-precision level sampling plan using TPL parameters, more traps were required for higher precision in lower SPW densities per trap. A sequential sampling stop line was constructed using TPL parameters. If the treatment threshold was greater than 10 maximum adult SPWs on a trap, the required traps numbered 15 at a fixed-precision level of 0.25. In estimating the mean density per trap, the proportion of traps with two or more adult SPWs was more efficient than whole counting: ${\ln}(m)=1.19+0.90{\ln}(-{\ln}(1-p_T))$. The results of this study could be used to prevent the dissemination of SPW as a viral disease vector by using accurate control decision in SPW management programs.

• Economic and Environmental Geology
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• v.34 no.1
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• pp.1-12
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• 2001

The crystal structure of biotite-1M from Bancroft, Ontario, was determined by Rietveld refinement method using high-resolution neutron powder diffraction data at -26.3$^{\circ}C$, 2$0^{\circ}C$, 30$0^{\circ}C$, $600^{\circ}C$, 90$0^{\circ}C$. The crystal structure has been refined to a R sub(B) of 5.06%-11.9% and S (Goodness of fitness) of 2.97-3.94. The expansion rate of a, b, c unit cell dimensions with elevated temperature linearly increase to $600^{\circ}C$. The expansivity of the c dimension is $1.61{\times}10^{40}C^{-1}$, while $2.73{\times}10^{50}C^{-1}$ and $5.71{\times}10^{-50}C^{-1}$ for the a and b dimensions, respectively. Thus, the volume increase of the unit cell is dominated by expansion of the c axis as increasing temperature. In contrast to the trend, the expansivity of the dimensions is decreased at 90$0^{\circ}C$. It may be attributed to a change in cation size caused by dehydroxylation-oxidation of $Fe^{2+}$ to $Fe^{3+}$ in vacuum condition at such high temperature. The position of H-proton was determined by the refinement of diffraction pattern at low temperature (-2.63$^{\circ}C$). The position is 0.9103${\AA}$ from the O sub(4) location and located at atomic coordinates (x/a=0.138, y/b=0.5, z/c=0.305) with the OH vector almost normal to plane (001). According to the increase of the temperature, $\alpha$* (tetrahedral rotation angle), $t_{oct}$ (octahedral sheet thickness), mean distance increase except 90$0^{\circ}C$ data. But the trend is less clearly relative to unit cell dimension expansion because the expansion is dominant to the interlayer. Also, ${\Psi}$ (octahedral flattening angle) shows no trends as increasing temperature and it may be because the octahedron (M1, M2) is substituted by Mg and Fe.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.228-239
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• 2009

Purpose: 4DCT scans performed for radiotherapy were retrospectively analyzed to assess the possible benefits of respiratory gating in non-small cell lung cancer (NSCLC) and established the predictive factors for identifying patients who could benefit from this approach. Materials and Methods: Three treatment planning was performed for 15 patients with stage I~III NSCLC using different planning target volumes (PTVs) as follows: 1) PTVroutine, derived from the addition of conventional uniform margins to gross tumor volume (GTV) of a single bin, 2) PTVall phases (patient-specific PTV), derived from the composite GTV of all 6 bins of the 4DCT, and 3) PTVgating, derived from the composite GTV of 3 consecutive bins at end-exhalation. Results: The reductions in PTV were 43.2% and 9.5%, respectively, for the PTVall phases vs. PTVroutine and PTVgating vs. PTVall phases. Compared to PTVroutine, the use of PTVall phases and PTVgating reduced the mean lung dose (MLD) by 18.1% and 21.6%, and $V_{20}$ by 18.2% and 22.0%, respectively. Significant correlations were seen between certain predictive factors selected from the tumor mobility and volume analysis, such as the 3D mobility vector, the reduction in 3D mobility and PTV with gating, and the ratio of GTV overlap between 2 extreme bins and additional reductions in both MLD and $V_{20}$ with gating. Conclusion: The additional benefits with gating compared to the use of patient-specific PTV were modest; however, there were distinct correlations and differences according to the predictive factors. Therefore, these predictive factors might be useful for identifying patients who could benefit from respiratory-gated radiotherapy.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.31 no.3
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• pp.199-218
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• 2005

Purpose of Study: Peripheral nerve regeneration depends on neurotrophism of distal nerve stump, recovery potential of neuron, supporting cell like Schwann cell and neurotrophic factors such as BDNF. Peripheral nerve regeneration can be enhanced by the conduit which connects the both sides of transected nerve. The conduit maintains the effects of neurotrophism and BDNF produced by Schwann cells which can be made by gene therapy. In this study, we tried to enhance the peripheral nerve regeneration by using calcium phosphate coated porous conduit and BDNF-Adenovirus infected Schwann cells in sciatic nerve of rats. Materials and Methods: Microporous filter which permits the tissue fluid essential for nerve regeneration and does not permit infiltration of fibroblasts, was made into 2mm diameter and 17mm length conduit. Then it was coated with calcium phosphate to improve the Schwann cell adhesion and survival. The coated filter was evaluated by SEM examination and MTT assay. For effective allogenic Schwann cell culture, dorsal root ganglia of 1-day old rat were extracted and treated with enzyme and antimitotic Ara-C. Human BDNF cDNA was obtained from cDNA library and amplified using PCR. BDNF gene was inserted into adenovirus shuttle vector pAACCMVpARS in which E1 was deleted. We infected the BDNF-Ad into 293 human mammary kidney cell-line and obtained the virus plaque 2 days later. RT-PCR was performed to evaluate the secretion of BDNF in infected Schwann cells. To determine the most optimal m.o.i of BDNF-Ad, we infected the Schwann cells with LacZ adenovirus in 1, 20, 50, 75, 100, 250 m.o.i for 2 hours and stained with ${\beta}$-galactosidase. Rats(n=24) weighing around 300g were used. Total 14mm sciatic nerve defect was made and connected with calcium phosphate coated conduits. Schwann cells$(1{\times}10^6)$ or BDNF-Ad infected Schwann cells$(1{\times}10^6)$ were injected in conduit and only media(MEM) was injected in control group. Twelve weeks after surgery, degree of nerve regeneration was evaluated with gait analysis, electrophysiologic measurements and histomorphometric analysis. Results: 1. Microporous Millipore filter was effective conduit which permitted the adhesion of Schwann cells and inhibited the adhesion of fibroblast. We could enhance the Schwann cell adhesion and survival by coating Millipore filter with calcium phosphate. 2. Schwann cell culture technique using repeated treatment of Ara-C and GDNF was established. The mean number of Schwann cells obtained 1 and 2 weeks after the culture were $1.54{\pm}4.0{\times}10^6$ and $9.66{\pm}9.6{\times}10^6$. 3. The mRNA of BDNF in BDNF-Ad infected Schwann cells was detected using RT-PCR. In Schwann cell $0.69\;{\mu}g/{\mu}l$ of DNA was detected and in BDNF-Adenovirus transfected Schwann cell $0.795\;{\mu}g/{\mu}l$ of DNA was detected. The most effective infection concentration was determined by LacZ Adenovirus and 75 m.o.i was found the most optimal. Conclusion: BDNF-Ad transfected Schwann cells successfully regenerated the 14mm nerve gap which was connected with calcium phosphate coated Millipore filter. The BDNF-Ad group showed better results compared with Schwann cells only group and control group in aspect to sciatic function index, electrophysiologic measurements and histomorphometric analysis.

• Korean journal of applied entomology
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• v.50 no.4
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• pp.343-352
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• 2011

The developmental period of Laodelphax striatellus Fallen, a vector of rice stripe virus (RSV), was investigated at ten constant temperatures from 12.5 to $35{\pm}1^{\circ}C$ at 30 to 40% RH, and a photoperiod of 14:10 (L:D) h. Eggs developed successfully at each temperature tested and their developmental time decreased as temperature increased. Egg development was fasted at $35^{\circ}C$(5.8 days), and slowest at $12.5^{\circ}C$ (44.5 days). Nymphs could not develop to the adult stage at 32.5 or $35^{\circ}C$. The mean total developmental time of nymphal stages at 12.5, 15, 17.5, 20, 22.5, 25, 27.5 and $30^{\circ}C$ were 132.7, 55.9, 37.7, 26.9, 20.2, 15.8, 14.9 and 17.4 days, respectively. One linear model and four nonlinear models (Briere 1, Lactin 2, Logan 6 and Poikilotherm rate) were used to determine the response of developmental rate to temperature. The lower threshold temperatures of egg and total nymphal stage of L. striatellus were $10.2^{\circ}C$ and $10.7^{\circ}C$, respectively. The thermal constants (degree-days) for eggs and nymphs were 122.0 and 238.1DD, respectively. Among the four nonlinear models, the Poikilotherm rate model had the best fit for all developmental stages ($r^2$=0.98~0.99). The distribution of completion of each development stage was well described by the two-parameter Weibull function ($r^2$=0.84~0.94). The emergence rate of L. striatellus adults using DYMEX$^{(R)}$ was predicted under the assumption that the physiological age of over-wintered nymphs was 0.2 and that the Poikilotherm rate model was applied to describe temperature-dependent development. The result presented higher predictability than other conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.562-569
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• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of Korean Society of Forest Science
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• v.107 no.4
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• pp.486-496
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• 2018

Pine wilt disease (PWD) in Gyeonggi province was first detected in Gwangju in 2007, and ever since has caused extensive damage. Insect vector and host tree in Gyeonggi province are Monochamus saltuarius and Pinus koraiensis, respectively, which are different from the southern region that consist of Monochamus alternatus and Pinus densiflora. Consequently, spread and mortality characteristics may be different, but our understanding is limited. In this research, we utilized the spatial data of newly infected trees in Gyeonggi province from 2008 to 2015 to analyze how it is related to various environmental and human factors, such as elevation, forest type, and road network. We also analyzed the minimum distance from newly infected tree to last year's closest infected tree to examine the dispersal characteristics based on new outbreak locations. Annual number of newly infected trees rapidly increased from 2008 to 2013, which then stabilized. Number of administrative districts with infected trees was 5 in 2012, 11 in 2013, and 15 in 2014. Most of the infected trees was Pinus koraiensis, with its proportion close to 90% throughout the survey period. Mean distance to newly infected trees dramatically decreased over time, from 4,111 m from 2012 to 2013, to approximately 600 m from 2013 to 2014 and 2014 to 2015. Most new infections occurred in higher elevation over time. Distance to road from newly infected trees continuously increased, suggesting that natural diffusion dispersal is increasingly occurring compared to human-influenced dispersal over time.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.