• Science of Emotion and Sensibility
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• v.21 no.2
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• pp.125-136
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• 2018

Although atypical sensory processing is a core feature of autism spectrum disorder (ASD), there is considerable heterogeneity among ASD individuals in the modality and symptoms of atypical sensory processing. The present study examined visual processing of children with ASD, focusing on the complexity and orientation of visual information. Age- and -IQ-matched Korean children (14 ASD and 14 typically-developing (TD) children) received an orientation discrimination task involving static spatial gratings varied in complexity (simple versus complex) and orientation (horizontal versus vertical). The results revealed that ASD children had difficulty perceiving complex information regardless of orientation, whereas TD children had more difficulty with vertical gratings than horizontal gratings. Thus, group-level differences between ASD and TD children appeared greater when gratings were presented horizontally. Unlike ASD adult literature, however, ASD children did not show superior performance on simple gratings. Our findings on typical and atypical processing of ASD children have implications for both understanding the characteristics of ASD children and developing diagnostic tools for ASD.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.5
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• pp.77-86
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• 1995

In this paper in order to reduce the encoding complexity required in the full search vector quantization(VQ), a new classified vector quantization(CVQ) technique is described employing the minimum-distance classifier. The determination of the optimal subcodebook sizes for each class is an important task in CVQ designs and is not an easy work. Therefore letting the subcodebook sizes be equal. A CVQ technique. Which satisties the optimal CVQ condition approximately, is proposed. The proposed CVQ is a kind of the partial search VQ because it requires a search process within each subcodebook only, and the minimum encoding complexity since the subcodebook sizes are the same in each class. But simulation results reveal while the encoding complexity is only O(N$^{1/2}$) comparing with O(N) of the full-search VQ. A simple systolic array, which has the through-put of k, is also proposed for the implementation of the VQ. Since the operation of the classifier is identical with that of the VQ, the proposed array is applied to both the classifier and the VQ in the proposed CVQ, which shows the usefulness of the proposed CVQ.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.39-47
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• 1997

There are four parameters (i.e. overall density, local density, grouping, layout complexity) to consider in designing screen of a visual display terminal. Among these, only the optimum level of overall density is known to be about 25~30% by some studies. Therefore, the present experiment is conducted to define the optimum levels of the other parameters to achieve the user's best performance in visual search task. The results are as follows; (1) The function related to the levels of local density and user's search times is shown to be U -shaped. When the level of local density is about 40%, the search time is shorter than those of any other levels. (2) In the experiment of grouping, user's performance is best when the number of group is 5, and the size of group does not exceed visual angle $5^{\circ}$ (0,088rad). (3) The user performance is improved as the layout becomes less complex.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.6
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• pp.62-68
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• 1983

We suggest a Quasi- dual graph model in consideration of dynamic module assignment and relocation to assign task optimally to two processors that have different processing abilities. An optimal module partitioning and allocation to minimize total processing cost can be achieved by applying shortest-path algorithm with time complexity 0(n2) on this graph model.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.314-317
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• 2000

Cluster system provides attractive scalability in terms of computation power and memory size. With the advances in high speed computer network technology, cluster systems are becoming increasingly competitive compared to expensive parallel machines. In parallel processing program, each task load is difficult to predict before running the program and each task is interdependent each other in many ways. Load imbalancing induces an obstacle to system performance. Most of researches in load balancing were concerned with distributed system but researches in cluster system are few. In cluster system, the dynamic load balancing algorithm which evaluates each processor's load in runtime is purpose that the load of each node are evenly distributed. But, if communication cost or node complexity becomes high, it is not effective method for all nodes to attend load balancing process. In that circumstances, it is good to reduce the number of node which attend to load balancing process. We have modeled cluster systems and proposed marginal dynamic load balancing algorithms suitable for that circumstances.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2371-2377
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• 2010

As the substantial increment in program complexity and appearance of mega program, the programs need to be divided to small tasks with multiple partitions and performed with a priority based scheduling. And also, a program development has to be progressed according to diversify of development environment. For instance, there are some restrictions upon O/S environment such as embedded O/S or windows. Therefore, the adaptive scheduling technique which performs multiple task partitioning process, regardless environment or O/S, is suggested. In this study, In this study, the adaptive scheduling technique algorithm and its applied examples are described.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1559-1566
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• 2017

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.252-255
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• 2020

Unsupervised deep learning methods have shown impressive results for the challenging monocular depth estimation task, a field of study that has gained attention in recent years. A common approach for this task is to train a deep convolutional neural network (DCNN) via an image synthesis sub-task, where additional views are utilized during training to minimize a photometric reconstruction error. Previous unsupervised depth estimation networks are trained within a fixed depth estimation range, irrespective of its possible range for a given image, leading to suboptimal estimates. To overcome this suboptimal limitation, we first propose an unsupervised adaptive depth estimation method guided by minimum and maximum (min-max) depth priors for a given input image. The incorporation of min-max depth priors can drastically reduce the depth estimation complexity and produce depth estimates with higher accuracy. Moreover, we propose a novel network architecture for adaptive depth estimation, called the AdaMM-DepthNet, which adopts the min-max depth estimation in its front side. Intensive experimental results demonstrate that the adaptive depth estimation can significantly boost up the accuracy with a fewer number of parameters over the conventional approaches with a fixed minimum and maximum depth range.

• Research in Mathematical Education
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• v.26 no.3
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• pp.167-202
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• 2023

A central problem of mathematics teaching worldwide is probably the insufficient adaptive handling of tasks-especially in computational practice phases and modeling tasks. All students in a classroom must often work on the same tasks. In the process, the high-achieving students are often underchallenged, and the low-achieving ones are overchallenged. This publication uses different modeling of the tennis serve as an example to show a possible solution to the problem and develops and discusses one adaptive task each for middle school, high school, and college using three mathematical models of the tennis serve each time. From model to model within the task, the complexity of the modeling increases, the mathematical or physical demands on the students increase, and the new modeling leads to more realistic results. The proposed models offer the possibility to address heterogeneous learning groups by their arrangement in the surface structure of the so-called parallel adaptive task and to stimulate adaptive mathematics teaching on the instructional topic of mathematical modeling. Models A through C are suitable for middle school instruction, models C through E for high school, and models E through G for college. The models are classified in the specific modeling cycle and its extension by a digital tool model, and individual modeling steps are explained. The advantages of the presented models regarding teaching and learning mathematical modeling are elaborated. In addition, we report our first teaching experiences with the developed parallel adaptive tasks.