• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.141-156
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• 2011

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.245-249
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• 2006

The aim of this study is to design and develop the gait rehabilitation equipment that judge patient's movement of his/her center of gravity using pressure sensors, and to aid hemiplegic patients to balance themselves using an automatic stepper that changes the patient's center of gravity. It is hard to bear the weight on the affected side for hemiplegic patients. The gait rehabilitation equipment detects the footing phase of hemiplegic patient during training and moves the unaffected footing side of the stepper up and moves the affected footing side down simultaneously so that the patient's center of gravity can shift from unaffected side to affected side. The gait rehabilitation system was developed and applied for hemiplegic patients during exercise. Eight hemiplegic patients and one normal adult were studied. The developed gait rehabilitation system could judge not only the normal adult's intention but also the patient's intention to move his/her center of gravity. Even though the most of hemiplegic patients exercised in automatic mode and a few hemiplegic patients exercised in manual mode, the developed gait rehabilitation system can aid the hemiplegic patients to train more easily.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.249-254
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• 2010

We propose an automatic spam filter for e-mail data using Support Vector Machines(SVM). We use a lexical form of a word and its part of speech(POS) tags as features and select features by chi square statistics. We represent each feature by TF(text frequency), TF-IDF, and binary weight for experiments. After training SVM with the selected features, SVM classifies each e-mail as spam or not. In experiment, the selected features improve the performance of our system and we acquired overall 98.9% of accuracy with TREC05-p1 spam corpus.

• Preventive Nutrition and Food Science
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• v.4 no.3
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• pp.184-187
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• 1999

Increase in fat mobilization by capsaicin(CAP) was investigated in high-fat-fed mice using an adjustable current water pool. Male ICR 7-wk-old mice were fed a high fat diet [50% total energy content in the diet(E%) fat, 20 E% protein, 30E% carbohydrate] for 2 wk and one group (HCAP) was orally administered CAP at 2 h before swimming. After being accustomed to swimming , the mice were subjected to forced swimming every 2d in the current water pol and the total swimming period until exhaustion was measured . The total swimming period was used as index of swimming capacity. Swimming time to exhaustion of treated mice was significantly longer than that of the high-fat-fed control group (100.2$\pm$10.6 vs. 58.0$\pm$8.5min, P<0.01) after 2wk of training. The concentration of serum-free fatty scids gradually increased up to 2 h in CAP -administered mice. The perirenal adipose tissue weight of CAP -administered mice (HCAP) before swimming was lower than that of the high-fat-fed mice adminstered placebo solution (HP) which had not ingested CAP during the 2 wk. These results suggest that the increase of swimming capacity of CAP-administered high-fat-fed mice was due to an increase of fat mobilization that was induced by CAP.

• Journal of muscle and joint health
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• v.28 no.1
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• pp.1-9
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• 2021

Purpose: The study evaluated the effectiveness of health promotion program on the physical fitness and quality of life of elderly women receiving home visiting health care services. Methods: There were 122 elderly women participants. The data were collected between March and December 2019. The participants were provided with the 36-weeks health promotion program consisting of health education, such as nutrition, depression, urinary incontinence, fall, oral care, and exercises, such as stretching, weight-bearing exercise, and elastic resistance training. The balance, muscle strength, flexibility, and quality of life were measured before and after the program. The results were analyzed with paired t-test using the SPSS/WIN 26.0 program. Results: The dynamic balance, muscle strength, flexibility significantly increased. Conclusion: The health promotion program positively affected elderly women in terms of physical fitness, but there are limitations to increasing the quality of life of elderly women. Through this study, it is necessary to be supplemented in improving quality of life of elderly women.

• Journal of Veterinary Science
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• v.22 no.3
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• pp.33.1-33.6
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• 2021

Very virulent infectious bursal disease virus (vvIBDV) causes high mortality in chickens but measures to reduce the mortality have not been explored. Chickens (8-9 weeks) were treated with 3 agents before and during vvIBDV inoculation. Dexamethasone treatment reduced the mortality of infected chickens (40.7% vs. 3.7%; p < 0.001), but treatment with aspirin or vitamin E plus selenium did not affect the mortality. The bursa of Fabricius appeared to have shrunk in both dead and surviving chickens (p < 0.01). The results indicate that dexamethasone can reduce mortality in vvIBDV-infected chickens and may provide therapeutic clues for saving individual birds infected by the virus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.9-15
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• 2018

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.