• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4681-4702
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• 2016

Recently, Massive energy consumption in Cloud Storage System has attracted great attention both in industry and research community. However, most of the solutions utilize single method to reduce the energy consumption only in one aspect. This paper proposed an energy effective gear-shifting mechanism (E2GSM) in Cloud Storage System to save energy consumption from multi-aspects. E2GSM is established on data classification mechanism and data replication management strategy. Data is classified according to its properties and then be placed into the corresponding zones through the data classification mechanism. Data replication management strategies determine the minimum replica number through a mathematical model and make decision on replica placement. Based on the above data classification mechanism and replica management strategies, the energy effective gear-shifting mechanism (E2GSM) can automatically gear-shifting among the nodes. Mathematical analytical model certificates our proposed E2GSM is energy effective. Simulation experiments based on Gridsim show that the proposed gear-shifting mechanism is cost effective. Compared to the other energy-saved mechanism, our E2GSM can save energy consumption substantially at the slight expense of performance loss while meeting the QoS of user.

• Management & Information Systems Review
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• v.34 no.5
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• pp.213-231
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• 2015

In this paper, I examine whether the listed companies in Korea tend to manage operating cash flows upward via classification shifting after the adoption of K-IFRS. As proxies for cash flow management, I derive a measure of abnormal operating cash flows borrowing from Lee(2012). Alternative proxies include a series of categorical variables designed to identify the types of classification shifting of interest and dividend payments among others, in the statement of cash flows. Higher level of estimated abnormal operating cash flows, and the classification of interest/dividend payments in non-operating activity sections are considered to indicate the managerial intention to maximize reported operating cash flows. I consider several potential incentives to manage operating cash flows, which include financial distress, the credit rating proximity to investment/non-investment cutoff threshold, avoidance of negative or decreasing operating cash flows relative to previous period and so forth. In a series of empirical analyses, I do not find evidence in support of the opportunistic classification shifting explanation, inconsistent with several previous literature in Korea. In contrast, I observe negative associations between the CFO management proxies and selected incentives, which suggest that the classification is likely to represent above average cash flow performance rather than opportunistic motives exercised to maximize reported operating cash flows. I reckon that this observation is, in part, driven by the K-IFRS requirement to maintain temporal consistency in classifying interest and dividend receipts/payments in cash flow statement.

• Journal of the Korean association of regional geographers
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• v.1 no.1
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• pp.93-101
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• 1995

Nam Khane watershed, in the Northern Laos, consists of limestone plateau surrounded with steep slope(above 1000m), wide piedmont hill land(300-700m) and narrow alluvial plain. Opium on the plateau and up-land rice on the hill-side are cultivated for each, but its shifting agricultural activity, which degrades the forest and soil, has caused the serious environmental problems. MOS-1 satellite image and 40 points of soil samples are analyzed to identify the distribution of the shifting cultivation and to evaluate the environmental problems for Nam Khane watershed. The land use classification map is presented on the photo 2, and the value of each land use area by elevation level and soil property are showed on the table 2 and 3, respectively. Excessive agricultural activity of shifting cultivation in the Nam Khane watershed not only decreased the forest area, but also changed the primary forest of tree into secondary woodland of shrub. On the phase of soil property, it accelerated the soil and gully erosion, and acidification. To solve these environmental problems, the most important step is to settle the agriculture from shifting cultivation to permanent cropping.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.2
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• pp.151-162
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• 2009

This study proposes a new methodology for quantitative rock classification in unsampled rock zone, which occupies the most of tunnel design area. This methodology is to train an ANN (artificial neural network) by using results from a drilling investigation combined with electric resistivity survey in sampled zone, and then apply the trained ANN to making a prediction of grade of rock classification in unsampled zone. The prediction is made at the center point of a shifting window by using a number of electric resistivity values within the window as input reference information. The ANN training in this study was carried out by the RPROP (Resilient backpropagation) training algorithm and Early-Stopping method for achieving a generalized training. The proposed methodology is then applied to generate a rock grade distribution on a real tunnel site where drilling investigation and resistivity survey were undertaken. The result from the ANN based prediction is compared with one from a conventional kriging method. In the comparison, the proposed ANN method shows a better agreement with the electric resistivity distribution obtained by field survey. And it is also seen that the proposed method produces a more realistic and more understandable rock grade distribution.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1418-1429
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• 2014

In this paper we suggest a new two-wheelers detection algorithm using local cell features. The first, we propose new feature vector matrix extraction algorithm using the correlation two cells based on local cell histogram and shifting from the result of histogram of oriented gradients(HOG). The second, we applied new weighting values which are calculated by the modified histogram intersection showing the similarity of two cells. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• Journal of Animal Environmental Science
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• v.7 no.1
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• pp.45-56
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• 2001

The following statistics are the results of a survey that analyzed the classification of livestock area and spatial mobility based upon the number of livestock and an area of 151 towns and cities from 1975 to 1995. 1. As a results of analysis about the degree of location concentration using C.V., Korean native cattles (HanWoo) and swines are becoming more centralized while dairies and chickens are becoming decentralized. 2. 49 regions, that is 32.5%, were classified as growing regions, 30 regions (19.9%) were stagnant regions and 72 regions (47.7%) were withering regions. The classification was based upon the calculation according to the numbers of converted grown animals and growth index. Kyonggi-do and Chungchongnam-do, specifically, took up 26.6% and 24.5% of the developing regions which shows that these two regions are the dominant regions for livestock. 3. Kyongsangbuk-do and Chungchongnam-do play significant roles for overall livestock, and Chollanam-do is considering a transition from swines to Korean native cattles and Kyongsangbuk-do is shifting from Korean native cattles to swines.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Journal of Service Research and Studies
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• v.8 no.1
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• pp.89-102
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• 2018

The Industrial Classification is a systematic taxonomy of industrial activities and the Standard Industrial Classification is used in all country by their own a consistent classification method. Therefore, it is employed to analyze current status of industry affairs using statistical investigations in terms industrial activities for making industrial policies and to compare industrial activity among countries. Since the Second Industrial Revolution, the need for the homogenous standard of industrial classification among countries emerged as the economic and industrial exchanges between the countries have became more active. In 1940, Colin Clark who british economist divided the industry into the first (primitive), second (processed), and third (service) industries. Based on this, the United Nations Office for Statistics (UNSD) established International Standard Industry Classification (ISIC) in 1948, which most countries invoke it. ISIC(International Standard Industry Classification) and the standard industry classifications of countries have reached the present after several revisions since the enactment of the Act. In the 2000s, the standard industry classification is amended to reflect the emergence of new industries and changes in industrial structure, mainly featuring the creation and segmentation of sections in the tertiary industry domains. It also shows that primary and secondary sectors are shifting to tertiary industry. In this study, the causes of these common phenomena are systematically identified and the problems present classification systems have been analyzed. Also proposed is the direction of formation of the industrial classification system from a service economy point of view and the conceptual model of the new classification system. In the future, it is necessary to validate the proposed model through this study and to carry out various new classification system studies.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.221-225
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• 2008

In this paper, to avoid the frequency analysis requiring a high sampling rate, time-warped similarity measure algorithms, which are able to classify objects even with a low-rate sampling rate as time- series methods, are presented and proposed the DTW-Cosine algorithm, as the best classifier among them in wireless sensor networks. Two problems, local time shifting and spatial signal variation, should be solved to apply the time-warped similarity measure algorithms to wireless sensor networks. We find that our proposed algorithm can overcome those problems very efficiently and outperforms the other algorithms by at least 10.3% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.