• The Mathematical Education
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• v.53 no.2
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• pp.275-290
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• 2014

This study investigated the types of the 3D geometric thinking and spatial reasoning through the observation of the 2D representing activities for representing the 3D geometrical objects with preservice secondary mathematics teachers. For this purpose, the 43 sophomoric students in college of education were divided into 10 groups and observed their group task performance on the basis of the representation they used. Observed processes were all recorded and the participants were interviewed based on the task. As a result, the role of physical object that becoming the object of geometric thinking and spatial reasoning, and diverse strategies and phenomena of the process that representing the 3D geometric figures in 2D were discovered. Furthermore, these processes of representing were assumed to be influenced by experience and study practice of students, and various forms of representing process were also discovered in the process of small group activities.

• Smart Structures and Systems
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• v.29 no.6
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• pp.777-783
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• 2022

Even though several deep learning-based methods have been applied in the field of acoustic source localization, the previous works have only been conducted using the two-dimensional representation of the beamforming maps, particularly with the planar array system. While the acoustic sources are more required to be localized in a spherical microphone array system considering that we live and hear in the 3D world, the conventional 2D equirectangular map of the spherical beamforming map is highly vulnerable to the distortion that occurs when the 3D map is projected to the 2D space. In this study, a 3D deep learning approach is proposed to fulfill accurate source localization via distortion-free 3D representation. A target function is first proposed to obtain 3D source distribution maps that can represent multiple sources' positional and strength information. While the proposed target map expands the source localization task into a point-wise prediction task, a PointNet-based deep neural network is developed to precisely estimate the multiple sources' positions and strength information. While the proposed model's localization performance is evaluated, it is shown that the proposed method can achieve improved localization results from both quantitative and qualitative perspectives.

• Korean Journal of Cognitive Science
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• v.28 no.1
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• pp.27-42
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• 2017

Stroop effect(Stroop, 1935) is a reliable paradigm which has been used in various psychological research. Although classic Stroop experiment used color and color name for experimental stimuli, subsequent research reported that a color(e.g. green) and an object(e.g. grass) which displays a typical color show similar effects depending on color-object congruency(Klein, 1964). Because past research that used Stroop effect to investigate semantic representation tested association between concrete object and color, they predominantly used concrete nouns and their corresponding color names as stimuli(e.g. Dalrymple-Alford, 1968, 1972; Klein, 1964). Recently, Sherman and Clore(2009) reported that response time to white or black words is affected by moral value of words (e.g., honesty, crime) even when the words do not have specific referents. Based on this result, we tested association between thermesthesia-related adjectives(e.g., 따스한, 냉정한) and color(warm color, cold color) using Stroop task. The results showed that subjects were faster in their response to color when adjective-color was congruent than when incongruent, and there was an interaction between color and meaning of adjectives. The Stroop effect in this research is unique because, contrary to previous research that used concrete nouns, the effect was obtained even with abstract adjectives which do not have specific referents. In addition, unlike Sherman and Clore(2009) that used achromatic color, our results show that Stroop effect obtains between abstract adjectives and chromatic color.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5006-5022
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• 2017

Effect and robust detection of targets in infrared images has crucial meaning for many applications, such as infrared guidance, early warning, and video surveillance. However, it is not an easy task due to the special characteristics of the infrared images, in which the background clutters are severe and the targets are weak. The recent literature demonstrates that sparse representation can help handle the detection problem, however, the detection performance should be improved. To this end, in this text, a hybrid method based on local sparse representation and contrast is proposed, which can effectively and robustly detect the infrared targets. First, a residual image is calculated based on local sparse representation for the original image, in which the target can be effectively highlighted. Then, a local contrast based method is adopted to compute the target prediction image, in which the background clutters can be highly suppressed. Subsequently, the residual image and the target prediction image are combined together adaptively so as to accurately and robustly locate the targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than other existing alternatives.

• The Mathematical Education
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• v.44 no.4 s.111
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• pp.627-651
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• 2005

This study was conducted to attain an in-depth understanding of students' mathematical representations and to present the educational implications for teaching them. Twelve mathematical tasks were developed according to the six types of problems. A task performance was executed to 151 third graders from four classes in DaeJeon and GyeongGi. We analyzed the types and forms of representations generated by them. Then, qualitative case studies were conducted on two small-groups of five from two classes in GyeongGi. We analyzed how individuals' representations became elaborated into group representation and what patterns emerged during the collaborative small-group learning. From the results, most students used more than one representation in solving a problem, but they were not fluent enough to link them to successful problem solving or to transfer correctly among them. Students refined their representations into more meaningful group representation through peer interaction, self-reflection, etc.. Teachers need to give students opportunities to think through, and choose from, various representations in problem solving. We also need the in-depth understanding and great insights into students' representations for teaching.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.252-257
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• 2019

이 논문은 한국어 Abstract Meaning Representation (AMR; 추상 의미 표상) 가이드라인 1.0^*을 소개한다. AMR은 통합적인 의미 표상 체계로, 의미 분석(semantic parsing)의 주요 Task 중 하나로 자리매김하고 있다. 한국어 AMR 가이드라인은 현행 AMR 1.2.6을 심도 있게 분석하고 이를 한국어 상황에 맞게 로컬라이징한 것이다. 해당 가이드라인은 추후 한국어 AMR 말뭉치 구축(sembanking)에 대비하여 일관된 주석 세부 지침을 제공하기 위해 작성되었다.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.4
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• pp.42-55
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• 2014

The purpose of this research was to identify expertise in ait traffic control by using cognitive skill analysis for novices and experts in routine and non-routine situations. The result of study was to understand expertise in air traffic control tasks in terms of what cognitive processes are responsible for the expert's high performance levels. The problem solving task was difficult for novices, but performed relatively automatically by experts in a routine situation. The difficulty could indicate the presence of controlled processing. Rather than rules and strategies, novices focused more on environmental factors, which merely increase cognitive load. In a non-routine situation, novices showed that they did not categorize the information consistently and alternative resources were not available for them. Experts, however, performed automatically a task by arranging and organizing information related to problem solving components in contexts without regard to a routine and non-routine situation. Especially experts developed a stable representation and directed alternative resources for air traffic flow and efficiency. Based on the results, cognitive processes of experts could be useful to understand expert performance and analyze the learning process, which imply the necessity of developing expertise systematically.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.400-400
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• 2000

This paper purposed that verify the validity of Petri Net method for control progressive increase of system complexity, before extend the realized single robot system to multi-robot system. An autonomous mobile robot(AMR) needs decision making, motion control, path planning, tracking a path, obstacle avoidance, and sensor fusion, to complete its task. An AMR integrates and operates these technics through a consistent command system. An error in a command hierarchy which is like duplication or omission of a control command hierarchy for each module results in serious problems. This paper minimizes the error by modeling each module and whole system using Petri Net graphical representation and applies it to the exploration task of an AMR

• Korean Journal of Cognitive Science
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• v.34 no.3
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• pp.153-180
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• 2023

Previous research on the action effect had shown how simple action towards a stimulus can enhance the processing of that stimulus in subsequent visual search task (Buttaccio & Hahn, 2011; Weidler & Abrams, 2014). In four experiments, we investigated whether semantic representation of action word can induce the same attentional boost towards that stimulus and whether the type of action performed can modulate the action effect. In experiment 1, we replicated the same experimental paradigm displayed in previous studies. Participants were first shown an action word cue - "go" or "no". When the action cue was "go", participants were to press a designated key, but not to when the action cue was "no". Next, participants performed a visual search task, in which they reported the orientation of a tilted bar. The target could appear on top of the previously shown prime object (valid), or not (invalid). Reaction times (RTs) to the search task were measure for analysis and comparison, and the action effect had been replicated. In experiment 2, participants were instructed to respond with the keyboard for the action task, and to respond with the joystick for the visual search task. In experiment 3, participants were instructed not to press any key on the onset of prime, and then perform the visual search task to isolate the effect of semantic representation. Lastly, in experiment 4, participants were instructed to press separate keys for "go" and "no" on the onset of prime, and then perform the visual search task. Results indicate that semantic representation alone did not modulate the action effect, regardless of type of action and medium of action.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.633-635
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• 2016

Convolutional Networks (ConvNets) are powerful models that learn hierarchies of visual features, which could also be used to obtain image representations for transfer learning. The basic pipeline for transfer learning is to first train a ConvNet on a large dataset (source task) and then use feed-forward units activation of the trained ConvNet as image representation for smaller datasets (target task). Our key contribution is to demonstrate superior performance of multiple ConvNet layer features over single ConvNet layer features. Combining multiple ConvNet layer features will result in more complex feature space with some features being repetitive. This requires some form of feature selection. We use AdaBoost with single stumps to implicitly select only distinct features that are useful towards classification from concatenated ConvNet features. Experimental results show that using multiple ConvNet layer activation features instead of single ConvNet layer features consistently will produce superior performance. Improvements becomes significant as we increase the distance between source task and the target task.