• Journal of the Korean Chemical Society
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• v.47 no.1
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• pp.79-86
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• 2003

Misconceptions of students for a Voltaic cell were studied and their contents described in the high school chemistry II textbooks were analyzed. This study shows that students have many misconceptions and a few of chemistry textbooks contain some false description in a Voltaic cell. In the most textbooks, the reasons why the measured cell voltage of a Voltaic cell is near 1.1 V at the initial stage and then it decreases with time are not explained clearly. The emf of a Voltaic cell at a standard state is 0.76 V but in some textbooks it is described as 1.1 V of a Daniel cell. Even after learning the Voltaic cell or performing the experiment of textbooks, most students still have some misconceptions. These may be due to at least two following facts: the first is that the measured cell voltage of a Voltaic cell at the initial stage is very similar to that of a Daniel cell. The second is that the most experiment of a Voltaic cell is not performed under the condition of a standard state. Therefore, we have suggested a model of the modified experimental setup of a Voltaic cell that could reduce misconceptions of students.

• Archives of design research
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• v.17 no.1
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• pp.309-318
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• 2004

As the mobile market has been expanded and segmented, a mobile market for kids could be possibly appeared sooner or later. Then, it is necessary to develop a new contents for the new media - a mobile game hardware for kids. These days, kids are most interested in computer games, and they do not have enough time to play with peers. Therefore, in this paper, edutainment game contents based on the wireless internet, are developed. The game could supply kids' learning, fun and especially, peer interaction. In order to develop a game contents through user centered design process, the state of art in mobile hardware and contents was examined, a secondary research and interviews and survey was conducted to understand users. Then, when ideas for game contents has suggested, behavior prototype test was done to verify and modify contents. The suggested game contents in this study, is a occupation simulation game, in which kids simulate their own future career and learn related knowledge in a unintentional way. The result of the study suggests the new direction of edutainment game contents and platform. Also, this study shows the representation of user-centered contents developing process for kids, which could be helpful for the following studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.747-752
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• 2006

In this paper, the adaptive fuzzy sliding mode controller with two systems is designed. The existing sliding mode controller used to $approximation{\^{u}}(t)$ with discrete sgn function and sat function for keeping the state trajectories on the sliding surface[1]. The proposed controller decrease the disturbance for uncertain control gain and This paper is concerned with an Adaptive Fuzzy Sliding Mode Control(AFSMC) that the fuzzy systems ate used to approximate the unknown functions of nonlinear system. In the adaptive fuzzy system, we adopt the adaptive law to approximate the dynamics of the nonlinear plant and to adjust the parameters of AFSMC. The stability of the suggested control system is proved via Lyapunov stability theorem, and convergence and robustness properties ate demonstrated. Futhermore, fuzzy tuning improve tracking abilities by changing some sliding conditions. In the traditional sliding mode control, ${\eta}$ is a positive constant. The increase of ${\eta}$ has led to a significant decrease in the rise time. However, this has resulted in higher overshoot. Therefore the proposed ${\eta}$ tuning AFSMC improve the performances, so that the controller can track the trajectories faster and more exactly than ordinary controller. The simulation results demonstrate that the performance is improved and the system also exhibits stability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.143-148
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• 2022

The subject of this paper is the practical examples of technoculture that critically thinks network technology, a strong material foundation in the era of data capitalism in the 21st century, and appropriates its socio-cultural metaphor as an artistic potential. In order to analyze its alternatives and the meaning of cultural politics, this paper examines the properties and influence of data capitalism after the 2008 global financial crisis, and the cultural and artistic context formed by its reaction. The first case considered in this paper, Furtherfield's workshop, provided a useful example of how citizens can participate in social change through learning and education in which art and technology are interrelated. The second case, Greek hackerspace HSGR, developed network technology as a tool to overcome the crisis by proposing a new progressive cultural commons due to Greece's financial crisis caused by the global financial crisis and a decrease in the state's creative support. The third case, Paolo Cirio's project, promoted a critical citizenship towards the state and community systems as dominant types of social governance. These technoculture cases can be evaluated as efforts to combine and rediscover progressive political ideology and its artistic realization tradition in the context of cultural politics, paying attention to the possibility of signifying practices of network technology that dominates the contemporary economic system.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.17-24
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• 2022

The article proves that the main goal of informatization of higher education institutions in the EU countries is to improve the quality of education of future specialists by introducing digital educational resources into the education process. The main tasks of informatization of education are defined. Digital educational resources are interpreted as a set of data in digital form that is applicable for use in the learning process; it is an information source containing graphic, text, digital, speech, music, video, photo and other information aimed at implementing the goals and objectives of modern education; educational resources on the Internet, electronic textbooks, educational programs, electronic libraries, etc. The creation of digital educational resources is defined as one of the main directions of informatization of all forms and levels of Education. Types of digital educational resources by educational functions are considered. The factors that determine the effectiveness of using digital educational resources in the educational process are identified. The use of digital educational resources in the training of future specialists in the EU countries is considered in detail. European countries note that digital educational resources in professional use allow you to implement a fundamentally new approach to teaching and education, which is based on broad communication, free exchange of opinions, ideas, information of participants in a joint project, on a completely natural desire to learn new things, expand their horizons; is based on real research methods (scientific or creative laboratories), allowing you to learn the laws of nature, the basics of techniques, technology, social phenomena in their dynamics, in the process of solving vital problems, features of various types of creativity in the process of joint activities of a group of participants; promotes the acquisition by teachers of various related skills that can be very useful in their professional activities, including the skills of using computer equipment and various digital technologies.

• The Mathematical Education
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• v.62 no.2
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• pp.211-236
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• 2023

The purpose of this study is to derive implications of preservice mathematics teacher education in Korea by analyzing the case of edTPA used in the preservice teacher training process in the United States. Recently, there has been a growing interest in promoting professional competencies considering not only the cognitive dimension related to knowledge development of preservice mathematics teachers but also the situational dimension considering reality in the classroom. The edTPA in the United States is a performance-based assessment based on lessons conducted by preservice teachers at school. This study analyzes the professional competencies required of preservice mathematics teachers by analyzing handbooks that described the case of edTPA in which preservice mathematics teachers in the United States participate. The edTPA includes planning, instruction, and assessment tasks, and continuous tasks are performed in connection with classes. Thus, the analysis is conducted on the points of linkage between the description of evaluation items and criteria in the planning, instruction, and assessment tasks, as well as the professional competencies required from that linkage. As a result of analyzing the edTPA handbooks, the professional competencies required of preservice mathematics teachers in the edTPA assessment were the competency to focus on and implement specific mathematics lessons, the competency to reflectively understand the implementation and assessment of specific mathematics lessons, and the competency to make a progressive determination of students' achievement related to their learning and their uses of language and representations. The results of this analysis can be used as constructs for competencies that can be assessed in the preservice in the organization of the preservice mathematics teacher curriculum and practice training semester system in Korea.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.