• Journal of Information Processing Systems
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• v.7 no.4
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• pp.613-626
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• 2011

In this paper, we focus on the pinwheel task model with a variable voltage processor with d discrete voltage/speed levels. We propose an intra-task DVS algorithm, which constructs a minimum energy schedule for k tasks in O(d+k log k) time We also give an inter-task DVS algorithm with O(d+n log n) time, where n denotes the number of jobs. Previous approaches solve this problem by generating a canonical schedule beforehand and adjusting the tasks' speed in O(dn log n) or O($n^3$) time. However, the length of a canonical schedule depends on the hyper period of those task periods and is of exponential length in general. In our approach, the tasks with arbitrary periods are first transformed into harmonic periods and then profile their key features. Afterward, an optimal discrete voltage schedule can be computed directly from those features.

• The Plant Pathology Journal
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• v.33 no.2
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• pp.118-124
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• 2017

Bell pepper is an economically important crop worldwide; however, production is restricted by a number of fungal diseases that cause significant yield loss. Chemical control is the most common approach adopted by growers to manage a number of these diseases. Monitoring for the development to resistance to fungicides in pathogenic fungal populations is central to devising integrated pest management strategies. Two fungal species, Fusarium incarnatum-equiseti species complex (FIESC) and Colletotrichum truncatum are important pathogens of bell pepper in Trinidad. This study was carried out to determine the sensitivity of 71 isolates belonging to these two fungal species to fungicides with different modes of action based on in vitro bioassays. There was no significant difference in log effective concentration required to achieve 50% colony growth inhibition ($LogEC_{50}$) values when field location and fungicide were considered for each species separately based on ANOVA analyses. However, the $LogEC_{50}$ value for the Aranguez-Antracol locationfungicide combination was almost twice the value for the Maloney/Macoya-Antracol location-fungicide combination regardless of fungal species. $LogEC_{50}$ values for Benomyl fungicide was also higher for C. truncatum isolates than for FIESC isolates and for any other fungicide. Cropping practices in these locations may explain the fungicide sensitivity data obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.541-542
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• 2017

In this paper, we intend to develop an integrated management solution that can be easily operated by integrating complex and various cloud environments. This has the advantage that users and administrators can conveniently solve problems by collecting and analyzing fixed log data and unstructured log data based on big data and realizing integrated monitoring in real time. Hypervisor log pattern analysis technology will be able to manage existing complex and various cloud environment more efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.5
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• pp.125-132
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• 2010

Web browser is essential application for using internet. If suspect use a web browser for crime, evidence related crime is stored in log file. Therefore, we obtain the useful information related crime as investigating web browser log file. In this paper, we look at the related work and tools for web browser log file. And we introduce analysis methodology of web browser log file focus on the digital forensics. In addition, we apply to our tool at real case.

• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.35-42
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• 2024

The Japanese larch (Larix kaempferi [Lamb.] Carriere) is a major timber species in Korea. However, studies on bucking rates and merchantable logs of this species are insufficient in South Korea. To bridge these gaps, in this study, the bucking rate of Japanese larch (Larix kaempferi [Lamb.] Carriere) was computed and the number of long logs and merchantable log volumes were analyzed. Sample trees were bucked according to the log grade for trading, and collected from a forest field in Gangwon Province. The bucking rate of all Japanese larch logs was >89%. The highly profitable 2-4 logs of 3.6 m length from trees with ≤30 cm diameter at breast height (DBH) and 5-6 logs with ≥34 cm DBH were produced. The bucking rate of long logs was >84%; thus, Japanese larch was found to be suitable for the supply of high-grade timber. Additionally, to follow reasonable wood supply plans, merchantable volume tables were offered based on 3.6 m-long number of logs and small-end diameter classes. Understanding the proportion of merchantable log volumes, bucking rates, and the number of long and short logs has large-scale applications in practical forestry.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.51-56
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• 2009

Flash memory has rapidly deployed as data storage. However, the flash memory has a major disadvantage that recorded data cannot be dynamically overwritten. In order to solve this "erase-before-write" problem, the log block buffer scheme used Flash memory file system. however, the current managements of the log buffer, in case random write pattern, BAST technique have problem of frequent merge operation, but FAST technique don't consider merge operation by frequently updated data. Previous methods not consider merge operation cost and frequently updated data. In this paper, we propose a new log buffer management scheme, called JBB. Our proposed method evaluates the worth of the merge of log blocks, so we conducts the merge operation between infrequently updated data and its data blocks, and postpone the merge operation between frequently updated data and its data blocks. Through the method, we prevent the unnecessary merge operations, reduce the number of the erase operation, and improve the utilization of the flash memory storage. We show the superiority of our proposed method through the performance evaluation with BAST and FAST.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.380-384
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• 2009

The log data collected on a mobile device contain diverse and continuous information about the user. From the log data, the location, pictures, running functions and services of the user can be obtained. It has interested in the research inferring the contexts and understanding the everyday-life of mobile users. In this paper, we have studied the methods for real-time collection of log data from mobile devices, analysis of the data, map based visualization and effective management of the personal everyday-life information. We have developed an application for sharing the contexts. The proposed application infers the personal contexts with Bayesian network probabilistic model. In the experiments, we confirm that the usability of visualization and information sharing functions based on the real world log data.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.1-12
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• 2014

Recently, the need for non-stop service is increasing by the business mission-critical Internet banking, e-payment, e-commerce, home shopping, securities trading, and petition business increases, clustered in a single database of existing, redundant research on high-availability technologies related to technique, etc. is increasing. It provides an API based on the Active Log in addition to the technique of redundancy, ALTIBASE$^{TM}$ Log Analyzer (below, ALA), provides scalability and communication of the same model or between heterogeneous. In this paper, we evaluated the performance of ALA by presenting the design of the database system that you can use the ALA, to satisfy all the synchronization features high scalability and high availability, real-time.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.