• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.56-63
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• 2015

Several fields of science have demanded large-scale workflow support, which requires thousands of CPU cores or more. In order to support such large-scale scientific workflows, high capacity parallel systems such as supercomputers are widely used. In order to increase the utilization of these systems, most schedulers use backfilling policy: Small jobs are moved ahead to fill in holes in the schedule when large jobs do not delay. Since an estimate of the runtime is necessary for backfilling, most parallel systems use user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, we propose a novel system for the runtime prediction based on workload-aware clustering with the goal of improving prediction performance. The proposed method for runtime prediction of parallel applications consists of three main phases. First, a feature selection based on factor analysis is performed to identify important input features. Then, it performs a clustering analysis of history data based on self-organizing map which is followed by hierarchical clustering for finding the clustering boundaries from the weight vectors. Finally, prediction models are constructed using support vector regression with the clustered workload data. Multiple prediction models for each clustered data pattern can reduce the error rate compared with a single model for the whole data pattern. In the experiments, we use workload logs on parallel systems (i.e., iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing with other techniques, experimental results show that the proposed method improves the accuracy up to 69.08%.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.457-464
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• 2009

The weak classifier of AdaBoost algorithm is a central classification element that uses a single criterion separating positive and negative learning candidates. Finding the best criterion to separate two feature distributions influences learning capacity of the algorithm. A common way to classify the distributions is to use the mean value of the features. However, positive and negative distributions of Haar-like feature as an image descriptor are hard to classify by a single threshold. The poor classification ability of the single threshold also increases the number of boosting operations, and finally results in a poor classifier. This paper proposes a weak classifier that uses multiple criterions by adding a probabilistic criterion of the positive candidate distribution with the conventional mean classifier: the positive distribution has low variation and the values are closer to the mean while the negative distribution has large variation and values are widely spread. The difference in the variance for the positive and negative distributions is used as an additional criterion. In the learning procedure, we use a new classifier that provides a better classifier between them by selective switching between the mean and standard deviation. We call this new type of combined classifier the "Mixed Weak Classifier". The proposed weak classifier is more robust than the mean classifier alone and decreases the number of boosting operations to be converged.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.47-56
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• 2020

The purpose of this study is to comprehensively analyze elementary school teachers' perceptions of the use of artificial intelligence in education. Recently, interest in the use of artificial intelligence has increased in the field of education. However, there is a lack of research on the perceptions of elementary school teachers using AI in education. Using descriptive statistics, multiple linear regression analysis, and semantic differential meaning scale, 69 elementary school teachers' perceptions of using AI in education were analyzed. As a results, artificial intelligence technology was perceived as most suitable method for assisting activities in class and for problem-based learning. Factors which influence the use of AI in education were learning contents, learning materials, and AI tools. AI in education had the features of personalized learning, promoting students' participation, and provoking students' interest. Further, instructional strategies or models that enable optimized educational operation should be developed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.1-17
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• 2001

Nowadays, spatiotemporal data models deal with objects which can be potentially useful for wide range applications in order to describe complex objects with spatial and/or temporal facilities. However, the information needed by each application usually varies, specially in the geographic information which depends on the kind of time oriented views, as defined in the modeling phase of the spatiotemporal geographic data design. To be able to deal with such diverse needs, geographic information systems must offer features that manipulate geometric, space-dependent(i.e, thematic), and spatial relationship positions with multiple time oriented views. This paper addresses problems of the formal definition of relationships among spatiotemporal objects and their properties on geographic information systems. The geographical data are divided in two main classes : geo-objects and geo-fields, which describe discrete and continuous representations of the spatial reality. I study semantics and syntax about the temporal changes of attributes and the relationship roles on geo-objects and non-geo-objects, This result will contribute on the design of object oriented spatiotemporal data model which is distinguishied from the recent geographic information system of the homogeneously anchored spatial objects

• Journal of Agricultural Extension & Community Development
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• v.3 no.1
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• pp.67-81
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• 1996

Our agricultural extension system with all its success and failures, rewards and punishment was introduced 50 years ago with particular reference from United States. Some of the established principles and policies of effective extension work were shaken off for immediate result. But the results were not praiseworthy. The purpose of this study is to throw some light on the question of systems and approaches on agricultural extension that can adequately meet the challenges of the future. Our extension system is `special government type` which administers a nation-wide network of extension and training services in close collaboration with that of the experiment station. This type, however, has innate weakness which inclined to be standardized, inflexible, and irrelevant to actual needs of farming communities and problems of farmers. In this regard, it is necessary to consider another approaches of agricultural extension: `Government Type`, `Agricultural University`, `Farmers Organization`. The characteristics features, advantages and disadvantages of these models have been discussed. Each model has been found wanting in one way or another to meet the needs and interest of the present Korean situation. In view of the agricultural situation, and considering the expected changes of farmers and technologies in the years ahead, the `modification (especially to the direction of provincial government) of preset system` was expected which would be operationally flexible and organizationally unified and decentralized. The modification of present system should include the following characteristics: 1) universal contact with client system, 2) local planning based on the needs of clients, 3) using multiple method of nonformal education, 4) fitting with both general and specialized farming systems, 5) accommodating variable clients, technologies and educational objects.

• Annals of Clinical Neurophysiology
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• v.14 no.2
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• pp.64-71
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• 2012

Background: It is generally accepted that upper motor neuron (UMN) lesion can alter lower motor neuron (LMN) function by the plasticity of neural circuit. However there have been only few researches regarding the axonal excitability of LMN after UMN injury especially during the acute stage. The aim of this study was to investigate the nerve excitability properties of the LMNs following an acute to subacute supratentorial corticospinal tract lesion. Methods: An automated nerve excitability test (NET) using the threshold tracking technique was utilized to measure multiple excitability indices in median motor axons of 15 stroke patients and 20 controls. Testing of both paretic and non-paretic side was repeated twice, during the acute stage and subacute stage. The protocols calculated the strength-duration time constant from the duration-charge curve, parameters of threshold electrotonus (TE), the current-threshold relationship from sequential sub-threshold current, and the recovery cycle from sequential supra-threshold stimulation. Results: On the paretic side, compared with the control group, significant decline of superexcitablity and increase in the relative refractory period were observed during the subacute stage of stroke. Additionally, despite the absence of statistical significance, a mildly collapsing in ('fanning in') of the TE was found. Conclusions: Our results suggest that supratentorial brain lesions can affect peripheral axonal excitability even during the early stage. The NET pattern probably suggests background membrane depolarization of LMNs. These features could be associated with trans-synaptic regulation of UMNs to LMNs as one of the "neural plasticity" mechanisms in acute brain injury.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.1-10
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• 2002

This paper presents the new template based human face recognition methods to improve the recognition performance against scale and in-plane rotation variations of face images. To enhance the recognition performance, the templates are generated by linear or nonlinear operation on multiple images including different scales and rotations of faces. As the invariant features to allow for scale and rotation variations of face images, we adopt the affine transformation, the log-polar mapping, and the log-polar image based FFT. The proposed recognition methods are evaluated in terms of the recognition rate and the processing time. Experimental results show that the proposed template based methods lead to higher recognition rate than the single image based one. The affine transformation based face recognition method shows marginally higher recognition rate than those of the log-polar mapping based method and the log-polar image based FFT, while, in the aspect of processing time, the log-polar mapping based method is the fastest one.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.31-39
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• 2020

In this paper, we introduce a technique for generating synthetic benchmarks based on time series data. Many of the data measured on IoT devices have a time series characteristic that measures numerical changes over time. However, there is a problem that it is difficult to model the data measured over a long period as generalized time series data. To solve this problem, this paper introduces the BST-IGT model. The BST-IGT model separates the entire data into sections that can be easily time-series modeled, collects the generated data into templates, and produces new synthetic benchmarks that share or modify characteristics based on them. As a result of making a new benchmark using the proposed modeling method, we could create a benchmark with multiple aspects by mixing the composite benchmark with the statistical features of the existing data and other benchmarks.

• Clinical and Experimental Reproductive Medicine
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• v.21 no.3
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• pp.227-232
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• 1994

In order to define the clinical characteristics of Korean women with polycystic ovary syndrome, clinical symptoms, biochemical features and ultrasonographic findings were determined in ninety PCO patients. Frequency of clinical manifestations were as follows:acne 42%, overweight 31 %, oily skin 14 %, hirsutism 10%. Relatively low frequencies of hirsutism is recognized in patients with Korean PCO syndrome. Mean(${\pm}$S.E.) of each hormone was:testosterone 1.18${\pm}$1.07ng/ml, LH 21.47${\pm}$${\pm}$2.6mIU/ml, FSH 7.26${\pm}$2.67mIU/ml, LH/FSH ratio 2.94${\pm}$1.29, prolactin 25.48${\pm}$46.33ng/ml, DHEA-S 333.78${\pm}$309.60ng/dl, 17-0HP 1.72${\pm}$1.74ng/ml. Mean 17-OHP after ACTH stimulation test was 5.07${\pm}$12.01ng/ml. Ultrasonographically measured mean ovarian volume were $11.02{\pm}5.92cm^3$ in right and $9.23{\pm}5.64cm^3$ in left and small sized multiple subcapsular follicular cysts were noted in 43 patients (47.9%) with PCO syndrome.