• Journal of Korean Elementary Science Education
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• v.29 no.2
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• pp.124-133
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• 2010

The study presents preliminary research on how science activities focusing on problem recognition worked and affected students' perception of their learning environment in a sixth science classroom. The science activities were based on the Science Writing Heuristic (SWH) which was suggested by Keys, Hand, Prain & Collins (1999), where problem recognition was an important part of scientific inquiry. For developing the working sheets for the modified SWH in this study, analyses of target units of 6th grade science curriculum in the aspects of problem recognition were conducted. After consecutive 6 classes with the developed working sheets for sixth graders, the student working sheets for each lesson were collected and analyzed. In order to investigate the developed units' affect on student learning, students' perceptions of their learning environment were administered before and after the applied classes. Students working sheets and questionnaires on their perceptions of learning environment indicated that students perceived that the science activities were more student-centered classes where students had active discussion and dialogue with one another giving them more chances to actively take part in the class as well as they used more properly recognized their inquiry problem.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• BMB Reports
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• v.48 no.6
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• pp.313-318
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• 2015

Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases that degrade the extracellular matrix (ECM) and regulate the extracellular microenvironment. Despite the significant role that MMP activity plays in cell-cell and cell-ECM interactions, migration, and differentiation, analyses of MMPs in vitro and in vivo have relied upon their abundance using conventional immunoassays, rather than their enzymatic activities. To resolve this issue, diverse nanoprobes have emerged and proven useful as effective activity-based detection tools. Here, we review the recent advances in luminescent nanoprobes and their applications in in vitro diagnosis and in vivo imaging of MMP activity. Nanoprobes with the purpose of sensing MMP activity consist of recognition and detection units, which include MMP-specific substrates and luminescent (fluorescent or bioluminescent) nanoparticles, respectively. With further research into improvement of the optical performance, it is anticipated that luminescent nanoprobes will have great potential for the study of the functional roles of proteases in cancer biology and nanomedicine. [BMB Reports 2015; 48(6): 313-318]

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.205-208
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• 2003

Voice activity detection is a important problem in the speech recognition and speech communication. This paper introduces new feature parameter which are reconstructed by spectral entropy of information theory for robust voice activity detection in the noise environment, then analyzes and compares it with energy method of voice activity detection and performance. In experiments, we confirmed that spectral entropy and its reconstructed parameter are superior than the energy method for robust voice activity detection in the various noise environment.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• Journal of KIISE
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• v.43 no.5
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• pp.531-540
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• 2016

Development of various sensors attached to mobile and wearable devices has led to increasing recognition of current context-based service to the user. In this study, we proposed a probabilistic model for recognizing user's food intake context, which can occur in a great variety of contexts. The model uses low-level sensor data from mobile and wrist-wearable devices that can be widely available in daily life. To cope with innate complexity and fuzziness in high-level activities like food intake, a context model represents the relevant contexts systematically based on 4 components of activity theory and 5 W's, and tree-structured Bayesian network recognizes the probabilistic state. To verify the proposed method, we collected 383 minutes of data from 4 people in a week and found that the proposed method outperforms the conventional machine learning methods in accuracy (93.21%). Also, we conducted a scenario-based test and investigated the effect contribution of individual components for recognition.

• Toxicological Research
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• v.31 no.3
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• pp.313-319
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• 2015

3-Monochloro-1,2-propanediol (3-MCPD) and 1,3-dichloro-2-propanol (1,3-DCP) are not only produced in the manufacturing process of foodstuffs such as hydrolyzed vegetable proteins and soy sauce but are also formed by heat processing in the presence of fat and low water activity. 3-MCPD exists both in free and ester forms, and the ester form has been also detected in various foods. Free 3-MCPD and 1,3-DCP are classified as Group 2B by the International Agency for Research on Cancer. Although there is no data confirming the toxicity of either compound in humans, their toxicity was evidenced in animal experimentation or in vitro. Although few studies have been conducted, free 3-MCPD has been shown to have neurotoxicity, reproductive toxicity, and carcinogenicity. In contrast, 1,3-DCP only has mutagenic activity. The purpose of this study was to analyze 3-MCPD and 1,3-DCP in various foods using gas chromatography-mass spectrometry. 3-MCPD and 1,3-DCP were analyzed using phenyl boronic acid derivatization and the liquid-liquid extraction method, respectively. The analytical method for 3-MCPD and 1,3-DCP was validated in terms of linearity, limit of detection (LOD), limit of quantitation, accuracy and precision. Consequently, the LODs of 3-MCPD and 1,3-DCP in various matrices were identified to be in the ranges of 4.18~10.56 ng/g and 1.06~3.15 ng/g, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.147-154
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• 2008

A variety of activity happens around the sidewalk in the city. Particularly, a large variety of activity happens in shopping area, but it causes an obstruction of economical revitalization since the pedestrians require time and cost to find what they want. So, this study will develop the path searching method to minimize the economical loss of shoppers by providing the significant path and supporting the walking movement. Firstly, consider existing network expression techniques and approach three points which are physical and environmental factor, the recognition of the pedestrians' space when changing the direction, and the recognition of restriction of vision and accessibility. Try to design the network DB and simulate the algorithm. As a result, it is now possible to do the path searching that considers variety of recognition factors and show the method how to make the path-searching algorithm for pedestrian.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.991-998
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• 2010

In realization of wireless home network system using speech recognition in indoor voice recognition environment, background noise and reverberation are two main causes of digression in voice recognition system. In this study, the home network system resistant to reverberation and background noise using voice section detection method based on spectral entropy in indoor recognition environment is to be realized. Spectral subtraction can reduce the effect of reverberation and remove noise independent from voice signal by eliminating signal distorted by reverberation in spectrum. For effective spectral subtraction, the correct separation of voice section and silent section should be accompanied and for this, improvement of performance needs to be done, applying to voice section detection method based on entropy. In this study, experimental and indoor environment testing is carried out to figure out command recognition rate in indoor recognition environment. The test result shows that command recognition rate improved in static environment and reverberant room condition, using voice section detection method based on spectral entropy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.45-52
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• 2016

The purpose of this study was to investigate the meaning of conflict experience in cooperative learning activities of pre-service early childhood teachers. The subjects were 85 pre-service early childhood teachers in W university. The data were collected through unstructured interviews and journal writings of the participants. The study results were as follow. First, pre-service early childhood teacher's conflict experiences in cooperative activity are team meeting, fair participation and evaluation, and conflict of the personal relations. Second, pre-service early childhood teacher's conflict resolution experiences in cooperative activity are autonomy of the team meeting time, reflective thinking, sentimental support, recognition of others, and solving problems by the time spending together. Third, the meanings of conflict experience in cooperative activity are formation of felt responsibility, self-growth through consideration of others, reciprocity, and recognition of the meaning of cooperation.