• Journal of The Korean Society of Integrative Medicine
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• v.8 no.3
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• pp.21-31
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• 2020

Purpose : The purpose of this study was to determine the effects of a cognitive-exercise program using step on the cognitive function, gait, and depression of elderly with mild cognitive impairment. Methods : The subjects comprised 30 elderly people with mild cognitive impairment who used elderly welfare centers in the provinces A, B, and C between March 21 and June 7, 2019. They were divided into an experimental group (n=15) and a control group (n=15) before undergoing an experiment based on a pretest-posttest control group design. The lowenstein occupational therapy cognitive assessment-geriatric population was used to assess the subjects' cognitive function, while the 4-stage balance test, 30-second chair-stand test, timed up & go test, and functional reach test were employed to evaluate their gait. In addition, the beck depression inventory was used to measure their levels of depression. Results : The cognitive-exercise program using step enhanced the subjects' cognitive function and gait and reduced their levels of depression. Furthermore, statistically significant differences were found between the experimental group and the control group. Conclusion : The cognitive-exercise program using step promoted the process of sensorimotor and cognition through the learning process of various steps taking left and right steps and cognitive activities. It improves gait by activating the coordination of the musculoskeletal and nervous systems of the body and positively affecting posture control, balance, flexibility, and lower extremity muscles. It also relieved depression by performing successful step learning and cognitive activities with forward-looking (doing) that leads to pleasure and achievement. The present study confirmed the value of a cognitive-exercise program using step to treat multiple domains of functional decline in elderly patients with mild cognitive impairment. This is therefore proposed as an intervention program for this patient group.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• PNF and Movement
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• v.13 no.2
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• pp.55-72
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• 2015

Purpose: The purpose of this study was to propose a new therapy algorithm that combines motor imagery and physiotherapy as a physiotherapeutic clinical intervention technique that can stimulate the recovery of damaged physical function for patients with stroke. Methods: A variety of scientific research results related to motor imagery were reviewed and analyzed to investigate their applicability to physiotherapy in clinics. Results: As a new therapy algorithm for the therapeutic approach of motor imagery in stroke rehabilitation, a therapy algorithm that combines motor imagery with physiotherapy is proposed, which consists of three stages or steps: STEP 1 motor imagery familiarization, STEP 2 explicit learning stage, and STEP 3 implicit learning. Conclusion: The new therapy algorithm proposed in this study is expected to be a very useful clinical therapeutic approach for stimulating the recovery of damaged physical function in patients with stroke. It is believed that it will be necessary to confirm and standardize the effects of the therapeutic algorithm proposed in this study in the future by conducting diverse clinical studies.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.283-288
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• 2019

In order to overcome the students' lack of information and experience, we developed a content planning tree that utilizes a decision tree. The content planning tree consists of a tree trunk creation step in which students select a theme and a story to develop, a parent branch generation step for selecting a category that can be developed based on the story, a child branch generation step for selecting the interesting "effect" method of producing the content effectively, a leaf generation step for selecting a multimedia expression 'element' to be visualized. The educational model was applied to game planning design and information visualization lectures, and provides examples of the categories, effects, and elements used in each lecture. The model was used for 145 team projects and the efficiency was confirmed by a step-by-step learning process.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.25-33
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• 2019

Recently, universities have been strengthening software basic education to be active in the era of the fourth industrial revolution. Non-majored students need a variety of teaching methods because they have low knowledge of programming or a lack of connectivity with major courses. Therefore, in this paper, a learning model applying the step-by-step blind programming practice based on the Demonstration Modeling Making model was designed and applied to the actual lecture. As a result of analyzing the problem solving ability of the learner, it was confirmed that the learner's self - solving ratio increased as parking progressed. In the following study, it is necessary to analyze the learner's learning results in various aspects and to study effective teaching methods according to the difficulty of the learning contents.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.9-14
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• 2020

Currently, NTIS (National Technology Information Service) is building an interactive search service based on artificial intelligence technology. In order to understand users' search intentions and provide R&D information, an interactive search service is built based on deep learning models and morpheme analyzers. The deep learning model learns based on the log data loaded when using NTIS and interactive search services and understands the user's search intention. And it provides task information through step-by-step search. Understanding the search intent makes exception handling easier, and step-by-step search makes it easier and faster to obtain the desired information than integrated search. For future research, it is necessary to expand the range of information provided to users.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.5
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• pp.428-433
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• 2013

We present a fully homomorphic encryption (FHE) scheme without key switching based on ring- learning with errors (RLWE) problems and some other assumption. Previous FHE schemes based on LWE needed a step called key switching to reduce the dimension of ciphertext. The key switching step actually needs a heavy computation and severe increasement of keys. So the key switching step is a big burden for implementing FHE Schemes. We suggest a FHE scheme without key switching step by reducing the dimension of ciphertexts in other way. Instead of throwing away key switching, we need another hardness assumption of the difficulty of solving quadratic equation over rings.

• The Journal of Korean Association of Computer Education
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• v.3 no.2
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• pp.39-46
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• 2000

This courseware includes multimedia elements such as graphic, voice and provides simulation learning and animation learning environment. Therefore, a learner can have various learning opportunities, be encouraged learning motivation, and be improved learning outcome. The content of the implemented program in this thesis provides voice and text basically. In addition to simulation learning and animation learning are provided appropriately by the type of content. Specially by using simulation implemented from simple subjects to more complicate subjects, a learner can select the most appropriate learning course and learn the simulation step by step. Through this courseware, a learner can learn more easily and excitedly.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.393-401
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• 2017

This article aims to suggest a principle frame and direction for the development of the character education program using Maumgram. Maumgram is said to clearly illustrate step-by-step structures of character education, for which the human mind is classified as four types(indulged type, rage-type, competition type, selfishness type) on the basis of the existing analytical psychology and Yogacara Thought. The design of the mind type program is composed of three steps. The first step is the 'discovery' step to awaken the cause of self-consciousness through observation, and the second is the 'conversion' step. Learning process to control the negative desires in a positive desire, and the third is the 'relationship'-oriented step to promote a sense of community with other people.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.297-310
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• 2008

Since speech acts implies speakers' intentions, it is essential to determine speakers' speech acts if we want to implement an intelligent dialogue system. We propose a two-step model for effectively determining speakers' speech acts. In the first step, the proposed model returns speech act candidates by using a neural network model based on machine learning and a predictivity model based on statistics, respectively. In the second step, using speech act candidates which are returned by the predictivity model, the proposed model filters out speech act candidates which are returned by the neural network model. Then, the proposed model selects a speech act with maximum output value among the unremoved speech act candidates. In the experiment on a schedule management domain, the proposed two-step modeling method showed better precisions than the previous methods only using a machine learning model or a probability model.