• Journal of the Korean School Mathematics Society
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• v.22 no.3
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• pp.241-256
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• 2019

In this paper, we analyzed the types of tasks presented in the 'Probability and Statistics' textbooks and how the cognitive competences required to perform the tasks provide students with opportunity-to-learn. To this end, the analysis of the 9 books of the 'Probability and Statistics' test textbooks according to the 2015 revised mathematics curriculum showed that the context-based tasks(CF type, RE type) ranged from 67.5% to 78.0% of the total number of tasks in each textbook, but the ratio of relevant and essential tasks related to real life is from 0.4% to 2.0%, it was found that most of the context-based tasks presented in the textbooks were disguised as real life materials. The cognitive competences of context-based tasks ranged from 29.6% to 50.0% in reproduction category, from 33.8% to 54.3% in connection category, and from 8.8% to 20.0% in reflection category. As a result, there was not enough opportunity-to-learn for students to experience reflective cognitive processes.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.265-272
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• 2014

This study suggests a system for preventing and coping with diverse industrial accidents available for taking place in the industrial field by designing a context-aware system of the solar cell equipment with the use of multi-sensor. It installs multi-sensor in the surrounding and major positions of the solar cell equipment, acquires data on the surrounding situations and solar cell equipment from this device, and then save it into the local memory and transmits it to the server. The saved data recognizes a situation based on the context-aware algorithm and judges depending on the perceived result. An administrator comes to have environment available for monitoring the status on the production field and equipments with real time according to the judged outcome through the context-aware algorithm. The context-aware system in the industrial field, which is put today in the ubiquitous environment, will become a service of offering appropriate information for dealing with industrial accidents through real-time inspection.

• Journal of Korean Elementary Science Education
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• v.33 no.2
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• pp.217-230
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• 2014

This study explores the features of sociocultural context of experiment classes taught by a science subject teacher. Two experiment classes on electric circuit for fifth graders were observed and video recorded. The data was also collected through student interviews and teacher interviews. Using the cultural historical activity theory, we extracted the six sociocultural factors and analyzed their interactions. This study could identify that four features of the sociocultural context of the cases. First, the rules of science classes were not decided by the teacher, but formed and modified through the negotiation between the teacher and students or between the students. Second, elementary students played a game, i.e. 'Countdown game', during their electricity experiments, which had both positive and negative influences on science learning. Third, the science teacher feels a limit on life guidance because of the position as a subject teacher in an elementary school. Lastly, although the science teacher had enough time to prepare science classes, there was no guarantee of the improvement of teaching quality. Based on the results of this study, educational implications are discussed in terms of teaching science experiments and of the science subject teacher system.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.39-53
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• 2018

The purpose of this study was to investigate the conceptual structure of subjects related to matter having pre-service elementary school teachers by applying semantic network analysis (SNA). The analyzed concepts in the subjects of matter were 6 words such as 'atom', 'molecule', 'ion', 'electron', 'matter' and 'particle'. The results of SNA of the concepts are as follows : 1. In the semantic network of 'atom', words having a high betweenness centrality were linked with the words based on both the scientific context and the everyday context. 2. The network of 'molecule' was analyzed to be more organized than the network of the 'atom'. 3. In the network of 'ion', the group of words of the scientific context was distinguished from the group of words of the everyday context. 4. The network of 'electron' was analyzed to be more oriented on electricity and magnetism in the field of physics. 5. In the network of 'matter', the words related to compounds were linked with knowledge of history of science. 6. The network of 'particle' was not structured with words based on particulate nature of matter.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.174-180
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• 2013

This paper proposes a decision tree based context clustering algorithm for HMM-based speech synthesis systems using the cross likelihood ratio with a hierarchical prior (CLRHP). Conventional algorithms tie the context-dependent HMM states that have similar statistical characteristics, but they do not consider the statistical similarity of split child nodes, which does not guarantee the statistical difference between the final leaf nodes. The proposed CLRHP algorithm improves the reliability of model parameters by taking a criterion of minimizing the statistical similarity of split child nodes. Experimental results verify the superiority of the proposed approach to conventional ones.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2218-2224
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• 2016

Opportunistic routing is designed for an environment where there is no stable end-to-end routing path between source node and destination node, and messages are forwarded via intermittent contacts between nodes and routed using a store-carry-forward mechanism. In this paper, we consider PRoPHET(Probabilistic Routing Protocol using History of Encounters and Transitivity) protocol as a base opportunistic routing protocol and propose an efficient context-aware opportunistic routing protocol by using the context information of delivery predictability and node type, e.g., pedestrian, car, and tram. In the proposed protocol, the node types of sending node and receiving node are checked. Then, if either sending node or receiving node is tram, messages are forwarded by comparing the delivery predictability of receiving node with predefined delivery predictability thresholds depending on the combination of sending node and receiving node types. Otherwise, messages are forwarded if the delivery predictability of receiving node is higher than that of sending node, as defined in PRoPHET protocol. Finally, we analyze the performance of the proposed protocol from the aspect of delivery ratio, overhead ratio, and delivery latency. Simulation results show that the proposed protocol has better delivery ratio, overhead ratio, and delivery latency than PRoPHET protocol in most of the considered simulation environments.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.61-66
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• 2007

The networked home is connected to the external world using a high speed network. The devices inside the house are connected using a wired and wireless network. Acquiring the user data is an essential step for designing the user interface in user centered design. In networked home, the numbers of use cases are exponentially increased because connected use cases are considered. Because the user data for networked home are too complicated, they are acquired and analyzed by a structured methodology. We surveyed 40 people to acquire the context data home and analyzed by 5W1H (Who, Where, What, When, Why, How). We established a framework for the user data using tasks, user, time, space, objects and environment. The data for home context was structured by our framework. This framework makes simple the home context and is helpful for user interface design in home network.

• ETRI Journal
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• v.42 no.2
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• Journal of Family Resource Management and Policy Review
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• v.20 no.2
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• pp.165-184
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• 2016

This study was conducted to identify factors which predict and discriminate women' reemployment. 288 married women whose careers had been interrupted for more than 1 year and 287 married women who re-entered into the labor market within 5 years were surveyed. Collected data were analyzed by logistic regression analysis. In the personal factor(reemployment need), proximal context factors(career barriers, family support and expectation for reemployment) and background context factors(SES, family life cycle), background context factors were revealed not to predict significantly women's reemployment. Secondly, in the case of proximal context factors, it was found that 'expectation of family members for reemployment' and 'sharing family care' had strong effects on reemployment. And compared with interrupted women, reemployed women were less likely to perceive career barriers. Specifically, they showed lower expectation to their job and status which they would achieve, less perceived gender/age discrimination in labor market, and had more confidence that they could find a job. Finally, with regard to the personal factor (reemployment need), the lower women had self-actualization need, the higher economic need, and the higher social need, it was highly likely to classify into reemployed women. We discussed the way to improve reemployment of career interrupted women based on above mentioned findings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.