• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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• pp.350-356
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• 1997

It is desirable for autonomous robot systems to possess the ability to behave in a smooth and continuous fashion when interacting with an unknown environment. Although Q-learning requires a lot of memory and time to optimize a series of actions in a continuous state space, it may not be easy to apply the method to such a real environment. In this paper, for continuous state space applications, to solve problem and a triangular type Q-value model\ulcorner This sounds very ackward. What is it you want to solve about the Q-value model. Our learning method can estimate a current Q-value by its relationship with the neighboring states and has the ability to learn its actions similar to that of Q-learning. Thus, our method can enable robots to move smoothly in a real environment. To show the validity of our method, navigation comparison with Q-learning are given and visual tracking simulation results involving an 2-DOF SCARA robot are also presented.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.675-680
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• 2018

In this paper, we propose a hand gesture recognizer using restricted coulomb energy (RCE) neural network, and present hardware implementation results for real-time learning and recognition. Since RCE-NN has a flexible network architecture and real-time learning process with low complexity, it is suitable for hand recognition applications. The 3D number dataset was created using an FPGA-based test platform and the designed hand gesture recognizer showed 98.8% recognition accuracy for the 3D number dataset. The proposed hand gesture recognizer is implemented in Intel-Altera cyclone IV FPGA and confirmed that it can be implemented with 26,702 logic elements and 258Kbit memory. In addition, real-time learning and recognition verification were performed at an operating frequency of 70MHz.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.1
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• pp.18-22
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• 2022

To investigate effects of cognitive function improvement whether against Taegeuk ginseng on scopolamine-induced memory impairment in rats. All experiments were conducted in three groups: the control group (CTR), the scopolamine 0.4mg/kg (SCP), and the scopolamine (SCP+T) treated with Taegeuk ginseng 100 mg/kg. Taegeuk ginseng 100 mg/kg daily was orally administered for one month and treated with scopolamine was only for 7 consecutive days on the Morris water maze task. 3 weeks after oral administration of Taegeuk ginseng, subjects were performed the Morris water maze test for 8 days and then the open-field exploration test which to assessed for cognitive function improvement. After behavioral testing, subjects were sacrificed and microdissected brains for neurochemical analysis. In the cognitive-behavioral test, long-term administration of Taegeuk ginseng improved spatial navigation learning task compared with the impeded by scopolamine treatment. In neurochemistry, the expression of the synaptic marker PSD95 (postsynaptic density protein 95) was increased in the hippocampus compared to the scopolamine group. Also, brain-derived neurotrophic factor (BDNF) expression was significantly increased in the taegeuk ginseng administration group. These data suggested that long-term administration of taegeuk ginseng might improve cognitive-behavioral functions on hippocampal related spatial learning memory, and it was correlated with neurotropic and synaptic reinforcement. In conclusion, treatment with taegeuk ginseng may positive outcome on learning and memory deficit disorders.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.132-133
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• 2023

The safe evacuation of people during disasters is of utmost importance. Various life safety evacuation simulation tools have been developed and implemented, with most relying on algorithms that analyze maps to extract the shortest path and guide agents along predetermined routes. While effective in predicting evacuation routes in stable disaster conditions and short timeframes, this approach falls short in dynamic situations where disaster scenarios constantly change. Existing algorithms struggle to respond to such scenarios, prompting the need for a more adaptive evacuation route algorithm that can respond to changing disasters. Artificial intelligence technology based on reinforcement learning holds the potential to develop such an algorithm. As a fundamental step in algorithm development, this study aims to evaluate whether an evacuation algorithm developed by reinforcement learning satisfies the performance conditions of the evacuation simulation tool required by IMO MSC.1/Circ1533.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.466-471
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• 2024

Recently, the Machine Learning based Artificial Intelligence has introduced in aviation field. In most cases, safety assurance of aviation software is achieved by applying RTCA DO-178C or DO-278A or similar standards. These standards were developed for and are well-suited to software that has inherent deterministic properties and explainability. Considering the characteristics of AI software based on ML, it is not feasible to assure the integrity of those new aviation systems using traditional software assurance standards mentioned above. In this paper, we research the certification framework that is newly suggested by EASA to deal with the aviation system including ML AI functions, and discuss what should the Korean authority and related industries prepare to cope with this issue.

• Archives of design research
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• v.18 no.3 s.61
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• pp.149-160
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• 2005

The Purpose of this study is to embody self-directed learning management system and contents which are considered of learner's interactivity and usability in e-learning environment; It is based on self directed teaching and learning strategies. It divides type of interaction centering on the learners into four categories: (1) learner and instructor (2) learner and learner (3) learner and contents (4) learner and learning management system. The specific elements of learning management system is set up and embodied to present the interaction strategies according to the above-mentioned patterns, to improve the self-directed learning ability and to facilitate an online communication. The learning contents based on the self directed learning strategies, design the interface in due consideration of the learners' usability based on six strategies such as simple navigation, consistency, intuitive interface, linkage, user supports and immediate feedback. This research makes up for the weak points in the self-directed functions of learning management system and links up with learning contents, therefore it has a value to improve learner's interactivity and usability. It is expected that the research results can be helpful in quality improvement of e-learning environment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2001.10a
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• pp.177-186
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• 2001

Based upon the previous experiences and typical oases of typhoon evasion fur ships as well as tile achievement in scientific research in this detrain, we developed the Intelligent Typhoon Evasion System for Ships. It consists of five subsystems, including electronic charts, ship movement management, typhoon information query and automatic plotting, real-time calculation of ship-typhoon situation, intelligent typhoon evasion decision making. With the synthetical application of analogy theory, synoptic chart, satellite cloud picture analysis, typhoon digital forecast and other relevant technologies, we leave established the typhoon evasion data bases. model bases and knowledge bases, which make it possible to automatically track the ships and typhoon paths. The system can realize ship-typhoon situation analysis, risk levee assessment, typhoon paths correction and course synoptic forecast, and intelligent typhoon evasion decision making.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.799-805
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• 2010

In this paper, we propose an intelligent navigation system that selects a proper route for user and applies the user's preference, user's tendency and environmental state estimated by driving information of user and road state. The system uses data of sensors, navigation and intelligent transport system to evaluate conditions of roads and it considers state of user's emotion. The system also uses soft-computing method to infer and learn the user's preference and tendency. We verify the proposed algorithm by computer simulation.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.53-64
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• 2020

As online learning forms centered on video lectures become more common and constantly increasing, the video-based learning environment applying various educational methods is also changing and developing to enhance learning effectiveness. Learner's log data has emerged for measuring the effectiveness of education in the online learning environment, and various analysis methods of log data are important for learner's customized learning prescriptions. To this end, the study analyzed learner behavior data and predictions of achievement by machine learning in video-based learning environments. As a result, interactive behaviors such as video navigation and comment writing, and learner-led learning behaviors predicted achievement in common in each model. Based on the results, the study provided implications for the design of the video learning environment.