• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.129-136
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• 2010

With mobile computing, wireless sensor network and sensor technologies, ubiquitous computing services are being realized and could satisfy the feasibility of ubiquitous healthcare to everyone. This u-Healthcare service can improve life quality of human since medical service can be provided to anyone, anytime, and anywhere. To confirm the vision of u-Healthcare service, we've implemented a healthcare system for heart disease patient which is composed of two components. Front-end collects various signals such as temperature, blood pressure, SpO2, and electrocardiogram, etc. As a backend, medical information server accumulates sensing data and performs back-end processing. To simply transfer these sensing values to a medical team may be too trivial. So, we've designed a model based on context awareness for more improved medical service which is based on artificial neural network. Through rigid experiments, we could confirm that the proposed system can provide improved medical service.

• Spatial Information Research
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• v.19 no.5
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• pp.87-96
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• 2011

Recently mobile users can access to internet using smart phone at any place and any time, through which they can search and share information. In addition, as the sensors with high-tech functions become cheaper and miniaturized along with the development of MEMS (micro-electo mechanical systems) technology, the extent to utilize smart phone is increasing. Smart phone is equipped with various sensors such as high-resolution camera, GPS, gyroscope and magnetic sensor, which is an appropriate system configuration for remote monitoring research using camera. The remote monitoring system requires camera for video and internet network to send video, for which it has a limitation that it is influenced by the monitoring location. This study is aimed to design and develop the monitoring app. which can be remotely monitored using smart phone technology. The developed monitoring app was designed to take images of ROI (region of interest) within the specified time and to automatically send the images to the server. The developed app. is also possible to be remotely controlled by SMS (short message service). The monitoring proposed in this study can take high-resolution images using CMOS built in the smart phone and send the images and information to the server automatically at any place and any time using 3G and Wi-Fi networks.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1161-1170
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• 2016

In this paper, we provide an authentication technology for verifying dynamic signature made by finger on smart phone. In the proposed method, we are using the Auto-Encoder-based 1 class model in order to effectively distinguish skilled forgery signature. In addition to the basic dynamic signature characteristic information such as appearance and velocity of a signature, we use accelerometer value supported by most of the smartphone. Signed data is re-sampled to give the same length and is normalized to a constant size. We built a test set for evaluation and conducted experiment in three ways. As results of the experiment, the proposed acceleration sensor value and 1 class model shows 6.9% less EER than previous method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.