• International Journal of Fluid Machinery and Systems
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• v.10 no.1
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• pp.63-75
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• 2017

Many axial fans have circular arc blades with constant thickness. It is still a challenging task to calculate their performance, i.e. to predict how large their pressure rise and pressure losses are. For this task a need for cascade data exists. Therefore, the designer needs a method which works quickly for design purposes. In the present contribution a design method for such cascades consisting of circular arc blades with constant thickness is described. It is based on a singularity method which is combined with a CFD-data-based flow loss model. The flow loss model uses CFD-data to predict the total pressure losses. An interpolation method for the CFD-data are applied and described in detail. Data of measurements are used to validate the CFD-data and parameter variations are conducted. The parameter variations include the variation of the camber angle, pitch chord ratio and the Reynolds number. Additionally, flow patterns of two dimensional cascades consisting of circular arc blades with constant thickness are shown.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.63-72
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• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.781-790
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• 2020

This study's primary purpose is to explore the psychological capital roles and personality traits of Big-Five in predicting OCB (organizational citizenship behavior) and performance of task in Indonesia's electricity sector. The data were gathered from the employees of four major cities in Indonesia, in Southeast Sulawesi, comprising 246 employees. The data were analyzed utilizing a PLS (partial least squares) based SEM (structural equation modeling) technique. The findings indicate that the psychological capital and personality traits of Big-Five relate significantly to OCB and the performance of task. Nevertheless, against our expectations, OCB does not significantly relate to the performance of task. This study also discusses the findings' further implications. In terms of practical implications, the findings of this research stipulate that psychological capital and Big-Five personality traits aimed to improve employee performance and can be most effective if specifically targeted at OCB. Given that both variables play an important role to promote OCB, caring training initiatives that focus on mutual help can be very valuable for organizational improvement. In a managerial perspective, organizations can increase OCB by conducting open communication strategies between managers and employees to further stimulate and strengthen the ability of employees to display extra-role behaviors.

• Journal of Korean Academy of Nursing
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• v.33 no.2
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• pp.179-188
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• 2003

Purpose: This study was designed to create the job description of Korean transplantation nurse practitioner and examine performance frequencies, criticality, and difficulties of task elements. Method: The sample consisted of 63 nurses and coordinators who performed duties related to transplantation at medical center in Korea. A survey method was used, and the questionnaire included frequencies, criticality, and difficulties of task elements in job description by the DACUM method. Using SPSS WIN 10.0, descriptive statistics such as frequency distribution, means, and standard deviations were conducted to examine the subject's general characteristics, the frequencies, criticality, and difficulties of task performance. Result: The job description of transplantation nurse practitioners revealed 5 duties, 22 tasks, and 85 task elements. On the all five duties, the averages of the performance frequency, criticality, and difficulty were 2.41, 3.38, and 2.78, meaning that the respondents rarely perform the 5 duties, but consider them critical and easy to perform. Conclusion: The job description of the transplantation nurse practitioner included duty, task, and task element and definition of job completed. Thus we recommended a data based trial to confirm and validate the information gathered.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.23-30
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• 2013

In this paper, we propose pre-arrangement based task scheduling scheme to reduce MapReduce job processing time. If a task and data to be processed do not locate in same node, the data should be transmitted to node where the task is allocated on. In that case, a job processing time increases owing to data transmission time. To avoid that case, we schedule tasks into two steps. In the first step, tasks are sorted in the order of high data locality. In the second step, tasks are exchanged to improve their data localities based on a location information of data. In performance evaluation, we compare the proposed method based Hadoop with a default Hadoop on a small Hadoop cluster in term of the job processing time and the number of tasks sorted to node without data to be processed by them. The result shows that the proposed method lowers job processing time by around 18%. Also, we confirm that the number of tasks allocated to node without data to be processed by them decreases by around 25%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.229-240
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• 2012

Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

• Journal of Home Health Care Nursing
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• v.15 no.1
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• pp.14-21
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• 2008

Purpose: The aim of this study was to analyze the effects of job characteristics on emotional empowerment in visiting health personnel. Methods: Subjects were recruited in ten community health care centers in one directorial area. Data collection was done using a self-report questionnaire. Job characteristics of visiting healthcare personnel were measured using the questionnaire developed by Kang (2006), based on Hackman & Oldham (1975). Emotional empowerment was measured using the questionnaire developed by Kang (2006), based on Spreitzer (1995). Results: First, the score of job characteristics was revealed to be 3.51 points the task significance was high, and the feedback was low. Second, the level of emotional empowerment was revealed to be 3.78 points the meaning was high, and the impact was low. Third, the prediction power of job characteristics on emotional empowerment was 34% autonomy, task identity, and task significance were identified as statistically significant predictive factors. Conclusion: The job characteristics of visiting healthcare personnel are highly correlated with emotional empowerment. Autonomy, task identity, and task significance are predictive factors of emotional empowerment. These results can be used to develop more effective job planning for increasing organizational effectiveness in visiting healthcare personnel.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.75-86
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• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• English Language & Literature Teaching
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• v.15 no.3
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• pp.171-198
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• 2009

The current study explored a pedagogical possibility of utilizing magic as a source of communicative tasks for young learners in developing their English speaking proficiency. Fifteen primary school students participated in the study, which consisted of a 17-week period of task-based English instruction and data collection. The participants were instructed to accomplish various types of magic task through collaborative group interaction. The data collected for the study pertained to the students' linguistic outputs, interactions in group and attitudes to English learning. They were analyzed for how magic tasks affect the students' English proficiency developments and group interactions. The study results suggested the significant improvement in the students' English speaking proficiencies. They revealed that magic tasks contributed to a) enhancing the motivation to speak in English, b) stimulating the creative and problem-solving processes, and c) providing the sufficient opportunity to repeat and internalize the target expressions. The study results also indicated that the students' satisfaction with their group members and tasks seemed to have positive influences on their interactions in group and English proficiency development. Further discussion and pedagogical implications are provided as well as the study limitations.