• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Journal of the Korean Institute of Educational Facilities
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• v.16 no.2
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• pp.67-77
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• 2009

This study was made in order to provide the groundwork for the revised 7th education curriculum of primary and secondary education's facility criterion. Throughout this study, in which we may accomodate the new education curriculum, we prepare the guide for the standard criterion of the school systems in order to reflect the flexibility of various dictinctive regional education conditions and school qualities considering the purpose of new standard criterion of school facilities. Below is the summary of the study. Regular classroom's standard size, which is the basic module for the scale of the educational institution, accomodates current standards. Number of students per class is aimed to fit the standard number of the level of OECD member countries' in order to prepare for the future ; that is, 30 students in primary, secondary and high school to be the standard number of student per class, depending on the district conditions and construction point of time. It is advised that the number of extracurricular classrooms, according to the standard criterion of subject and hour allotment, to reflect the regional and institution's distinctive qualities by indicating the number of hours and classrooms including decimal points. That is to be done so that the founder and the interested parties of the institution, along with the architect can put to practical use when planning and designing the institution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.137-143
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• 2009

Through the development of techniques, robots are becomes smaller, and many of robots needed for application are greater and greater. Method of coordinating large number of autonomous robots through local interactions has becoming an important research issue in robot community. Swarm Robot System is a system that independent autonomous robots in the restricted environment infer their status from preassigned conditions and operate their jobs through the coorperation with each other. Within the SRS,a robot contains sensor part to percept the situation around them, communication part to exchange information, and actuator part to do a work. Specially, in order to cooperate with other robots, communicating with other robot is one of the essential elements. In such as Bluetooth has many adventages such as low power consumption, small size module package, and various standard procotols, it is rated as one of the efficent communcating system for autonomous robot is developed in this paper. and How to construct and what kind of procedure to develop the communicatry system for group behavior of the SRS under intelligent space is discussed in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Korean Institute of Interior Design Journal
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• v.25 no.3
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• pp.70-81
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• 2016

Nowadays the preschool children spend much of the day playing indoors. Therefore, it needs the indoor environment that helps a variety of fun activities and physical development, and it requires the space configuration and playable furniture considering the emotional development for mental health. However, the furniture for fast growing preschoolers has not various types because the domestic furniture market for children is mostly baby beds and the furniture for the education of children. Therefore, this study presents the concepts and types of the playable furniture reflecting emotional design for preschool children's healthy emotion and suggests the most appropriate type of playable furniture considering play behavior by age. In this study, we investigated the physical, cognitive, social, emotional and linguistic development characteristics and play behavior of preschoolers, and derived the right type of playground equipment and furniture, and examined the types of playable furniture to help the emotional development. We derived the items to be checked for developing the playable furniture by age for emotional development, and classified preschoolers' playable furniture into the use of learning, relaxation and storage, and suggested its basic type focusing on the cases of various playable furniture developed at home and abroad. As a result, the playable furniture was divided into three types. The first is the self-play type making possible self amusement, the second is module built-up-type that consists of furniture and modules or units and creates various patterns and can be modified through the self-assembly and disassembly, and the third is IT game type grafting IT skills and a variety of electronic games to furniture. We sorted these types into three classes (1-3 years old, 4-5, 6-7) according to age and presented the type of play for each age, the play element and representative image that can be introduced to this furniture. In this study, we provided the basic design types of age-specific emotional playable furniture by analyzing these results.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.377-380
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• 2018

인간의 추론 능력이란 문제에 주어진 조건을 보고 문제 해결에 필요한 것이 무엇인지를 논리적으로 생각해 보는 것으로 문제 상황 속에서 일정한 규칙이나 성질을 발견하고 이를 수학적인 방법으로 법칙을 찾아내거나 해결하는 능력을 말한다. 이러한 인간인지 능력과 유사한 인공지능 시스템을 개발하는데 있어서 핵심적 도전은 비구조적 데이터(unstructured data)로부터 그 개체들(object)과 그들간의 관계(relation)에 대해 추론하는 능력을 부여하는 것이라고 할 수 있다. 지금까지 딥러닝(deep learning) 방법은 구조화 되지 않은 데이터로부터 문제를 해결하는 엄청난 진보를 가져왔지만, 명시적으로 개체간의 관계를 고려하지 않고 이를 수행해왔다. 최근 발표된 구조화되지 않은 데이터로부터 복잡한 관계 추론을 수행하는 심층신경망(deep neural networks)은 관계추론(relational reasoning)의 시도를 이해하는데 기대할 만한 접근법을 보여주고 있다. 그 첫 번째는 관계추론을 위한 간단한 신경망 모듈(A simple neural network module for relational reasoning) 인 RN(Relation Networks)이고, 두 번째는 시각적 관찰을 기반으로 실제대상의 미래 상태를 예측하는 범용 목적의 VIN(Visual Interaction Networks)이다. 관계 추론을 수행하는 이들 심층신경망(deep neural networks)은 세상을 객체(objects)와 그들의 관계(their relations)라는 체계로 분해하고, 신경망(neural networks)이 피상적으로는 매우 달라 보이지만 근본적으로는 공통관계를 갖는 장면들에 대하여 객체와 관계라는 새로운 결합(combinations)을 일반화할 수 있는 강력한 추론 능력(powerful ability to reason)을 보유할 수 있다는 것을 보여주고 있다. 본 논문에서는 관계 추론을 수행하는 심층신경망(deep neural networks) 중에서 Sort-of-CLEVR 데이터 셋(dataset)을 사용하여 RN(Relation Networks)의 성능을 재현 및 관찰해 보았으며, 더 나아가 파라미터(parameters) 튜닝을 통하여 RN(Relation Networks) 모델의 성능 개선방법을 제시하여 보았다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.547-554
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• 2019

With the curriculum revision in 2015, informatics for secondary high schools was designated as mandatory. As a result, there is an increasing interest in programming in elementary and junior high schools as well as in universities. Arduino is one of the famous tools for programming education, and the usefulness of it has been proven through various case studies. However, existing Arduino-based kits have hardware-dependent drawbacks such as complicated wiring, poor scalability, etc. To overcome these problems, we proposed a kit design, which has a module-based structure, can be extended through one common interface, and can be used for learning at various levels. In this paper, we describe the implementation details of FRUTO kit and a software to use it, which satisfies the proposed design criteria. FRUTO kit has been determined in its current form through several design changes, and is under pre-test before launching.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.79-85
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• 2021

A standard engineering model that reflects the current organization system and engineering operation process of domestic nuclear power plants was developed based on the Standard Nuclear Performance Model developed by the American Nuclear Energy Association. The level 0 screen, which is the main screen of the engineering model computer system, consisted of an object tree structure, which provided information that is phased down from a higher structure level to a lower structure level (i.e., level 3). The level 1 screen provided information related to the sub-process of the engineering operation, whereas the Level 2 screen provided information related to each engineering operation activity. In addition, the Level 2 screen provided additional functions, such as linking electronic procedures/guidelines, providing electronic performance forms, and connecting legacy computer systems (such as total equipment reliability monitoring system, configuration management systems, technical information systems, risk monitoring systems, regulatory information, and electronic drawing system). This screen level increased the convenience of user's engineering tasks by implementing them. The computerization of an engineering model that connects the entire engineering tasks of an establishment enables the easy understanding of information related to the engineering process before and after the operation, and builds a foundation for the enhancement of the work efficiency and employee capacity. In addition, KHNP developed an online training module, which operates as an e-learning process, on the overview and utilization of a standard engineering model to expand the understanding of standard engineering models by plant employees and to secure competitiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.100-106
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• 2020

In this paper smart shoes for dementia care using embedded boards and Low Power Wide Area technology and their application software are implemented. The communication board composed of Cortex-M3 board and LoRa module is embedded into groove made in outsole of smart shoes. Including the mold, the shoe outsole was manufactured by hand. By using application software and embedded board, caregiver can track the position of dementia patient using GPS and LoRa network. The location tracking and data transmission operations of smart shoes have been successfully verified in the outdoor environment. The smart shoes of this paper are applicable to a safety device to prevent the disappearance of demented patients through results of experiments and if bigdata is collected and analyzed by deep-learning, it may be helpful to analyze the predictive path of dementia patients or the pattern of dementia.