• Korean Journal of Veterinary Service
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• v.36 no.3
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• pp.151-155
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• 2013

Since a large number of Akabane and bovine ephemeral fever (BEF) infection occurred in the southern part of Korea in 2010, recent information about seroprevalence of Akabane virus (AKAV) and bovine ephemeral fever virus (BEFV) has been required for preventing both diseases. In this study, serological assay against AKAV and BEFV using virus neutralization assay was conducted using 1,743 bovine sera collected from Namwon, Miryang, Yeongju and Uljin which located in Southern part of Korea from March to May in 2012. The overall seropositive rates for AKAV and BEFV were found to be 49.8% and 1.2%, respectively. The regional distribution of seroprevalence for AKAV ranged from 18.1% to 63.7%. Seroprevalences of AKAV were 63.7% in Miryang, 62.3% in Uljin, 50.7% in Namwon, and 18.1% in Yeongju. The seropositive rates for AKAV in southern part of Korea were higher than the annual average at the national level. On the other hand, seropositive rates of BEFV in four regions were from 0.3 to 3.1%. In detail, regional seroprevalences were 3.1% in Miryang, 2.0% in Uljin, and 1.7% in Yeongju, and 0.3% in Namwon. Even only one year after massive outbreaks, overall seropositive rates were very low, similar to the annual average at the nation level. This result indicates that many number of cattle infected with BEFV may be replaced by new born calf or cattle in farm may not be immunized with vaccines. To prevent another epidemic, a national wide warning should be issued and more aggressive control measure must be implied. Recent global warming phenomenon could lead to more vigorous activity of haematophagous vectors and it is possible that arboviral diseases such as AKAV and BEFV are increased. Therefore, continuous sero-monitoring and extensive vaccination combined with control of haematophagous vectors are important to effectively prevent and control diseases caused by AKAV and BEFV.

• Journal of Aquaculture
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• v.20 no.1
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• pp.26-30
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• 2007

Resources of the sea cucumber, Stichopus japonicus in the west coast of Korea are decreasing sharply due to overfishing and severe marine pollutions. Artificial seed production and release of this species need to be conducted to maintain sea cucumber stock in the region. In this study, to provide basic information for such works, reproductive cycle of sea cucumber collected from Anmyondo in the region was histologically studied for a year from October 2004 to September 2005. This species was dioecious, and its gonads was composed of a number of gametogenic follicles. The gonadosomatic index (GSI) was reached the maximum in June (7.91), and the minimum in October (0.42). The main spawning occurs between June and August. The reproductive cycle of the sea cucumber could be divided into five stages: multiplicative stage (January to March), growing stage (March to April), mature stage (April to July), spawning.

• Journal of Korean Society of Forest Science
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• v.96 no.4
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• pp.477-482
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• 2007

Forest biomass became a topic because we have growing interest in global environmental issues and environment-friendly energy resources. This study was carried out to estimate the forest biomass and develop a program for biomass information management in Korea. The total forest biomass (million ton) were 521 for gross forest, 403 for productive forest and 201 for commercial forest in 2005. Also, the annual biomass production in forest was 20 million ton which was equivalent to 94,290 Gkcal of heating value and about 9 billion won of paraffin oil. The biomass growing rate (every 10year) increased from 4.95% in 1985 to 5.30% in 1995 but turn down 4.46% in 2005. The factors that the forest stock could be converted to the forest biomass have developed according to forest type. Therefore, it is impossible to estimate the exact biomass by tree species. In this reason, the demands of the development of the factors by tree species was raised. In addition, it is on time to develop an equation for estimation of biomass by species using dbh and height as independent factors.

• The Korean Journal of Financial Management
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• v.27 no.1
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• pp.31-62
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• 2010

This paper analyzes empirically how analysts' forecasts affected by ownership structure. This study examine a sample of 1,037~1,629 the analysts' forecasts of firms registered in Korean Stock Exchange in the period from 2000 to 2006. The empirical results are summarized as follows. First, from the analysis, companies which have higher major shareholder's holdings tend to increase earnings forecast errors and earnings forecast accuracy. Meanwhile, companies which have higher institution shareholder's holdings tend to decrease earnings forecast errors and earnings forecast accuracy. This result is in line with the view of previous works that companies with higher major shareholder's holdings look towards more of analysts' optimistic forecasts in order to maintain friendly relations with major shareholders. Because of analysts' private information use from major shareholders, earnings forecast accuracy is higher in high major shareholder's holdings firm than in high institution shareholder's holdings it. Second, this analysis is whether the minimal required selection condition of outside directors, audit committee adoption and audit quality affect the relation between ownership structure and analysts' forecasts. This result is that variables related corporate governance do not affect statically the relation between ownership structure and analysts' forecasts. The meanings of this paper is to suggest the positive relations between ownership structure and analysts' forecasts. After this, if analysts will notice forecasts of more many firms, capital market will be more efficient and this field works are plentiful. Also it will need monitoring systems not to distort market efficiency by analysts' dishonest forecasts.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.357-372
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• 2001

In this paper, we present the concept of generalization in constructing windows for subsequence matching and propose a new subsequence matching method. GeneralMatch, based on the generalization. The earlier work of Faloutsos et al.(FRM in short) causes a lot of false alarms due to lack of the point-filtering effect. DualMatch, which has been proposed by the authors, improves performance significantly over FRM by exploiting the point filtering effect, but it has the problem of having a smaller maximum window size (half that FRM) given the minimum query length. GeneralMatch, an improvement of DualMatch, offers advantages of both methods: it can use large windows like FRM and, at the same time, can exploit the point-filtering effect like DualMatch. GeneralMatch divides data sequences into J-sliding windows (generalized sliding windows) and the query sequence into J-disjoint windows (generalized disjoint windows). We formally prove that our GeneralMatch is correct, i.e., it incurs no false dismissal. We also prove that, given the minimum query length, there is a maximum bound of the window size to guarantee correctness of GeneralMatch. We then propose a method of determining the value of J that minimizes the number of page accesses, Experimental results for real stock data show that, for low selectivities ($10^{-6}~10^{-4}$), GeneralMatch improves performance by 114% over DualMatch and by 998% iver FRM on the average; for high selectivities ($10^{-6}~10^{-4}$), by 46% over DualMatch and by 65% over FRM on the average.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.555-560
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• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.2
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• pp.33-42
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• 1993

In order to obtain the basic information for the optimal settlement planning in the reclaimed area, various existing settlement conditions on reclaimed farming village were analysed through a case study of in-and-round farming villages in Deuk Ryang Reclaimed Area. By comparison study between in-and-around villages, the allocation policy of farming settlement in the samll and medium scale area was proposed. The results obtained in this study were as follows ; 1.The population structure of the inner village(Ansim village) had become worse than the around village during 1976~1981 period from the viewpoint of total and female emigration ratios. Especially, the originally unbalanced sex ratio of Ansim village had worsen to the unacceptable ratio of 1.5 : 1(male : female). Therefore, the structural stability of the population should be carefully improved in the newly reclaimed farming villages by the systematic long-term controlling machanism.2.The farming status of Ansim village was much lower than the around villages viewing of the cultivated acreage and agricultural machinery stock Therefore, the continuous and intensive governmental support different from the existing farming area should be required for the settling of reclaimed farming. 3. The regularity of the village site as a planned village, though pursed in the first phase of village construction, had been severely disturbed inflowing of squatters. Recently, there are increasing vaccant houses in the village, and the level of living amenities and personal mobility of Ansim village are much lower than the around village. So, now, the physical structure of the village should be improved to the level of village reconstruction or new village construction. 4. The cultivated area of Ansim village leans to the south side which would be unfavoured by the farmers of the around villages, while its distributing pattern should be concentric around the village site. This phenomenon disturbs the central fuction of the planned farming village. In this respect, role assignment, relationship and balance of competence in farming activities should be planned between in-and-around villages in the reclaimed area from the initial stage. 5. Putting together the above results in this study, the farming and living conditions of Ansim village threaten its existence as a planned new farming village in the reclaimed area. And, so, the full scale reconstruction of village structure, including moving to the new site, should be designed as soon as possible. Although further many studies should be carried out, it would be sure that the new village planning should be reconsidered in the small and-medium scale reclamation works.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.643-654
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• 2001

This paper discusses an effective processing of similarity search that supports time warping in large sequence database. Time warping enables finding sequences with similar patterns even when they are of different length, Previous methods fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. They have to scan all the database, thus suffer from serious performance degradation in large database. Another method that hires the suffix tree also shows poor performance due to the large tree size. In this paper we propose a new novel method for similarity search that supports time warping Our primary goal is to innovate on search performance in large database without false dismissal. to attain this goal ,we devise a new distance function $D_{tw-Ib}$ consistently underestimates the time warping distance and also satisfies the triangular inequality, $D_{tw-Ib}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping, For efficient processing, we employ a distance function, We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments . The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.