• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.242-255
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• 2006

In the recent years, Popularization of mobile devices such as Smart Phones, PDAs and MP3 Players causes rapid increasing necessity of Power management technology because it is most essential factor of mobile devices. On the other hand, despite low price, hard disk has large capacity and high speed. Even it can be made small enough today, too. So it appropriates mobile devices. but it consumes too much power to embed In mobile devices. Due to these motivations, in this paper we had suggested methods of minimizing Power consumption while playing multimedia data in the disk media for real-time and we evaluated what we had suggested. Strict limitation of power consumption of mobile devices has a big impact on designing both hardware and software. One difference between real-time multimedia streaming data and legacy text based data is requirement about continuity of data supply. This fact is why disk drive must persist in active state for the entire playback duration, from power management point of view; it nay be a great burden. A legacy power management function of mobile disk drive affects quality of multimedia playback negatively because of excessive I/O requests when the disk is in standby state. Therefore, in this paper, we analyze power consumption profile of disk drive in detail, and we develop the algorithm which can play multimedia data effectively using less power. This algorithm calculates number of data block to be read and time duration of active/standby state. From this, the algorithm suggested in this paper does optimal scheduling that is ensuring continual playback of data blocks stored in mobile disk drive. And we implement our algorithms in publicly available MPEG player software. This MPEG player software saves up to 60% of power consumption as compared with full-time active stated disk drive, and 38% of power consumption by comparison with disk drive controlled by native power management method.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Food Science and Preservation
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• v.11 no.2
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• pp.195-200
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• 2004

Microbial lethal value and nutrient retention of sous vide processed spinach were evaluated with mathematical model prediction and experimental trial for different package sizes and pasteurization temperatures. The package size covers 500 g, 1 kg and 2 kg, while the pasteurization temperature includes 80, 90 and 97$^{\circ}C$. The basic process scheme consists of filling blanched spinach into barrier plastic film pouch, sealing under vacuum, pasteurization in hot water with over pressure and final cooling to 3$^{\circ}C$. Pasteurization condition was designed based on attainment of 6 decimal inactivation of Listeria monocytogenes at geometric center of the pouch package by heating cycle, which was determined by general method. Heat penetration property of the package and thermal destruction kinetics were combined to estimate the retention of ascorbic acid and chlorophyll. Smaller packages with shorter pasteurization time gave better nutrient retention, physical and chemical qualities. Larger package size was estimated and confirmed experimentally to give higher pasteurization value at center, lower ascorbic acid and chlorophyll contents caused by longer heat process time. Lower pasteurization temperature with longer process time was predicted to give lower pasteurization value at center and lower ascorbic acid, while chlorophyll content was affected little by the temperature. Experimental trial showed better retention of ascorbic acid and chlorophyll for smaller package and higher pasteurization temperature with shorter heating time. The beneficial effect of smaller package and higher pasteurization temperature was also observed in texture, color retention and drip production.