• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.99-105
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• 2009

Computer-based learning with the access to World Wide Web has become a fundamental base for adopting beneficial education. It provides significant facilities such as animation and interactive processes that are not possible with textbooks. Web/Internet-enabled applications which is fully controlled and monitored from remote locations are extensively used by a number of Universities, national laboratories and companies for different kinds of applications all over the world. Continuous advances in computers and electronics coupled with drooping prices of hardware have made Web/Internet-based technologies less costly than before, particularly for educational organizations. Thus, it is more affordable to invest in these technologies that are essential for both expanding education over Web and further improving and advancing such technologies the application of remote virtual instruments will be demonstrated in this context along with experiments that can be adopted to be educational experimental lab for Engineering Education students.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1319-1323
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• 2004

In this paper, a problem of Thai sign language recognition using a neural network is considered. The paper addresses the problem in classifying certain signs conveying quantitative meaning, e.g., large or small. By treating those signs corresponding to different quantities as derived from different classes, the recognition error rate of the standard multi-layer Perceptron increases if the precision in recognizing different quantities is increased. This is due the fact that, to increase the quantitative recognition precision of those signs, the number of (increasingly similar) classes must also be increased. This leads to an increase in false classification. The problem is due to misinterpreting the amount of quantity the quantitative signs convey. In this paper, instead of treating those signs conveying quantitative attribute of the same quantity type (such as 'size' or 'amount') as derived from different classes, here they are considered instances of the same class. Those signs of the same quantity type are then further divided into different subclasses according to the level of quantity each sign is associated with. By using this two-level classification, false classification among main gesture classes is made independent to the level of precision needed in recognizing different quantitative levels. Moreover, precision of quantitative level classification can be made higher during the recognition phase, as compared to that used in the training phase. A standard multi-layer Perceptron with a back propagation learning algorithm was adapted in the study to implement this two-level classification of quantitative gesture signs. Experimental results obtained using an electronic glove measurement of hand postures are included.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.629-641
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• 2016

This study analyzed the use of the phenomenological method proposed by Colaizzi (1978) in order to understand the meaning of block system class experiences in nursing students. Furthermore, this study provides primal data to improve curriculum efficiency. The data were collected from 18 participants who were students at a nursing university in the city of G from 20th of June to 10th of July 2015. This study investigated nursing student's block system class experience. The study results show six theme clusters consisting of 'Discomfort', 'Unsystematic study', 'Holding back an urge', 'Ever intensifying relationship', 'New discovery', and 'Chance for development', 17 themes, and 42 sub themes, which are included in those clusters in 58 configured meanings. This demonstrates that nursing students could reestablish their resolve for personal betterment and realize the importance of managing time by finishing the procedure with their best efforts although they suffer from physical and psychological inconvenience due to massive amounts of learning and assignment. These results indicate the need for development of stress management and an emotional support program based on the experience of nursing students.

• Genomics & Informatics
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• v.18 no.2
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• pp.15.1-15.6
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• 2020

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity "type 1 diabetes" in the phrase "type 1 and type 2 diabetes." This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE's existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.792-798
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• 2007

In this paper, we propose a hierarchical fuzzy-rough classification method based on statistical information for maximizing the performance of pattern classification and reducing the number of rules without learning approaches such as neural network, genetic algorithm. In the proposed method, statistical information is used for extracting the partition intervals of antecedent fuzzy sets at each layer on hierarchical fuzzy-rough classification systems and rough sets are used for minimizing the number of fuzzy if-then rules which are associated with the partition intervals extracted by statistical information. To show the effectiveness of the proposed method, we compared the classification results(e.g. the classification accuracy and the number of rules) of the proposed with those of the conventional methods on the Fisher's IRIS data. From the experimental results, we can confirm the fact that the proposed method considers only statistical information of the given data is similar to the classification performance of the conventional methods.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.181-186
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• 2020

In this study, an attempt was made to investigate the clinical reasoning of nursing students and to identify the factors that influence it. This study was held to grasp the learning ability related to core nursing competency for 297 3^rd and 4^rd grade nursing students. Results indicated that there was a significant difference when comparing clinical competence by grade(p=0.001). Also variables effecting clinical reasoning competence were Problem solving ability, Clinical Decision making ability, Clinical practice performance, and grade of nursing students were found to be significant (p,.001). Outcome of this study can be used as the basis for the development of various curriculums to enhance the clinical reasoning competence, in the future, research for this education development is expected to be needed.

• Biomolecules & Therapeutics
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• v.21 no.5
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• pp.405-410
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• 2013

Codonopsis lanceolata (Campanulaceae) traditionally have been used as a tonic and to treat patients with lung abscesses. Recently, it was proposed that the extract and some compounds isolated from C. lanceolata reversed scopolamine-induced memory and learning deficits. The purpose of this study was to evaluate the improvement of cognitive enhancing effect of C. lanceolata by steam and fermentation process in scopolamine-induced memory impairment mice models by passive avoidance test and Morris water maze test. The extract of C. lanceolata or the extract of steamed and fermented C. lanceolata (SFCE) was orally administered to male mice at the doses of 100 and 300 mg/kg body weight. As a result, mice treated with steamed and fermented C. lanceolata extract (SFCE) (300 mg/kg body weight, p.o.) showed shorter escape latencies than those with C. lanceolata extract or the scopolamine-administered group in Morris water maze test. Also, it exerted longer step-through latency time than scopolamine treated group in passive avoidance test. Furthermore, neuroprotective effect of SFCE on glutamate-induced cytotoxicity was assessed in HT22 cells. Only SFCE-treated cells showed significant protection at 500 ${\mu}g/ml$. Interestingly, steamed C. lanceolata with fermentation contained more phenolic acid including gallic acid and vanillic acid than original C. lanceolata. Collectively, these results suggest that steam and fermentation process of C. lanceolata increased cognitive enhancing activity related to the memory processes and neuroprotective effect than original C. lanceolata.