• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.3
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• pp.174-182
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• 2017

Objectives: This study was conducted in order to develop a computerized test to measure the level of mathematic achievement and related cognitive functions in children and adolescents in South Korea. Methods: The computerized Comprehensive Learning Test-Mathematic (CLT-M) consists of the whole number computation test, enumeration of dot group test, number line estimation test, numeral comparing test (magnitude/distance), rapid automatized naming test, digit span test, and working memory test. To obtain the necessary data and to investigate the reliability and validity of this test, 399 children and adolescents from kindergarten to middle school were recruited. Results: The internal consistency reliability of the CLT-M was high (Cronbach's alpha=0.76). Four factors explained 66.4% of the cumulative variances. In addition, the data for all of the CLT-M subtests were obtained. Conclusion: The computerized CLT-M can be used as a reliable and valid tool to evaluate the level of mathematical achievement and associated cognitive functions in Korean children and adolescents. This test can also be helpful to detect mathematical learning disabilities, including specific learning disorder with impairment in mathematics, in Korea.

• The Journal of Korean Physical Therapy
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• v.23 no.3
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• pp.65-70
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• 2011

Purpose: We investigated the changes in the stop-signal reaction time (SSRT) and the no-signal reaction time (NSRT) following motor sequential learning in the stop-signal task (SST). This study also determined which of the reduction0s of spatial processing time was better between blocked- and random-SST. Methods: Thirty right-handed healthy subjects without a history of neurological dysfunction were recruited. In all subjects, both the SSRT and the NSRT were measured for the SST. Tasks were classified into two categories based on the stop-signal patterns, the blocked-SST practice group and random-SST practice group. All subjects gave written informed consent. Results: In the blocked-SST group, both the SSRT and the NSRT was significantly decreased (p<0.05) but not significantly changed in the random-SST group. In the SSRT and the NSRT, the blocked-SST group was faster than the random-SST group (p<0.05). In the post-test SST after practice of each group, the SSRT was significantly decreased in the random-SST group (p<0.05), but the NSRT showed no significant changes in either group. Conclusion: These findings demonstrate that random-SST practice resulted in a decrease in internal processing times needed for a rapid stop to visual signals, indicating motor skill learning is acquired through improved response selection and inhibition.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• Korean Medical Education Review
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• v.15 no.1
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• pp.19-24
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• 2013

In medicine, rapid changes in information, technology, socio-economic interests, and globalization affect the medical education focused on the competencies of doctors, and the number of medical schools that are adopting an outcome-based curriculum (OBC) is increasing worldwide. This paper introduces the OBC model of 5 trailblazing medical schools from the UK, US, and Australia, comparing their unique features, followed by brief comment about Canada and the EU as well. On developing an OBC, the process of establishing the top outcomes for graduates is similar and the outcomes comprise knowledge, skills, and attitudes about science, patients, colleagues, society, and themselves. Implementing the outcomes down into the sub-levels of the curriculum is much more complicated and time-consuming. Assessing the achievement of every outcome is essential and requires the use of many tools in addition to the traditional written examination. From the perspective of adult learning theory, self-directed learning, team-learning, and individual and flexible achievement are tested and executed in an OBC. The gradual expansion and further innovation of an OBC is expected so that tomorrow's doctors will be able to meet the challenges of the future.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.197-209
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• 2014

The smart paradigm in the modern society is bringing about a rapid smart sensation and there are means of informational communications being developed with the smart technology in various fields. Accordingly, for an effective smart education, it is necessary to create the customized educational contents for the learners, the users of the education. In this study, the contents of smart education are categorized based on the user experiences. As a result of the analysis, the 3 types of UX are found to have a playful influence on the learning concentration and it is also deduced that such concentration of a learner positively affects his or her attitude towards learning. Moreover, by the age and gender groups, there were differences in the preferences for each of the UX type, so that, in result, gave the valid data for designing and applying the suitable UX type for creating contents of smart education for different main target groups.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.121-125
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• 2022

This paper deals with the development of a deep-learning-based robot that recognizes various types of stairs and performs a mission to go up to the target floor. The overall motion sequence of the robot is performed based on the ROS robot operating system, and it is possible to detect the shape of the stairs required to implement the motion sequence through rapid object recognition through YOLOv4 and Cuda acceleration calculations. Using the ROS operating system installed in Jetson Nano, a system was built to support communication between Arduino DUE and OpenCM 9.04 with heterogeneous hardware and to control the movement of the robot by aligning the received sensors and data. In addition, the web server for robot control was manufactured as ROS web server, and flow chart and basic ROS communication were designed to enable control through computer and smartphone through message passing.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.