• Korean Journal of Construction Engineering and Management
• /
• v.23 no.4
• /
• pp.3-14
• /
• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.1
• /
• pp.101-110
• /
• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• World Technopolis Review
• /
• v.5 no.1
• /
• pp.30-46
• /
• 2016

This paper is intended as an opening of a dialog on how to apply platform thinking in the development of innovation environments. It will briefly describe a new STP (Science and Technology Park) concept called 3GSP (Third Generation Science Park), which is gaining momentum in Finland. The paper explains the fundamental changes that are currently taking place in the global innovation environment and explains why platform thinking is becoming an essential element in ecosystem development. The theoretical background and classifications of platforms are described and the benefits to be gained from STP perspective are highlighted. The paper emphasizes especially the role of so called 'competence platforms' and explains the main characteristics of a fully working competence platform. The role of competence platforms in understanding serendipity and as a fundamental factor in building the team is also highlighted. The paper analyses from STP perspective several practical examples, where platform thinking supports the emergence of new innovation environments, including Urban Mill (Finland) and Meetberlage (Netherlands). The requirements for comprehensive competence platform services are presented and their potential to support community building and therefore ecosystem development is illustrated. This analysis will provide STP practitioners with new models for applying platform thinking and will help to establish co-creation, open innovation and serendipity management practices. The case studies presented will help STP management teams to evaluate the benefits of competence platforms in different contexts.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.776_777
• /
• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Structural Engineering and Mechanics
• /
• v.67 no.6
• /
• pp.617-628
• /
• 2018

This paper deals with the maximization of the critical buckling load of simply supported antisymmetric angle-ply plates resting on Pasternak foundation subjected to compressive loads using teaching learning based optimization method (TLBO). The first order shear deformation theory is used to obtain governing equations of the laminated plate. In the present optimization problem, the objective function is to maximize the buckling load factor and the design variables are the fibre orientation angles in the layers. Computer programming is developed in the MATLAB environment to estimate optimum stacking sequences of laminated plates. A comparison also has been performed between the TLBO, genetic algorithm (GA) and differential evolution algorithm (DE). Some examples are solved to show the applicability and usefulness of the TLBO for maximizing the buckling load of the plate via finding optimum stacking sequences of the plate. Additionally, the influences of different number of layers, plate aspect ratios, foundation parameters and load ratios on the optimal solutions are investigated.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.24 no.8
• /
• pp.627-635
• /
• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• Journal of The Korean Association of Information Education
• /
• v.5 no.2
• /
• pp.185-196
• /
• 2001

Elementary school students feel more difficult the mathematical word problems than the numberical formula. I think that this reason isn't the ability of mathematical calculation but the problems representation. It is demanded exactly understanding about the requirements of problem for improving ability of the mathematical word problem representation. It is necessary that we take multimedia data and communication for this, because web advances multimedia materialization and promotes mutual communication, then it gives us with the most environment for word problem representation learning. According to, this thesis is designed web-based system to improve ability of the mathematical word problem representation, applied the sixth grade it experimentally.

• The Journal of Korean Association of Computer Education
• /
• v.10 no.2
• /
• pp.115-123
• /
• 2007

Collaborative knowledge is continuously producted and modified by group individuals during collaboration and it is also fostered in a radical trust environment like Wiki. The example is Wikipedia. However I found out a big problem as difficulties of exploring when the knowledge space is extended more and more widely. To solve this problem, collaborative knowledge management systems are implemented based on wiki. The one is navigation map that supports the efficient exploring and the another is knowledge map that supports a convergent thinking in a group. In this study, we examined the effectiveness of navigation map and knowledge map.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.01a
• /
• pp.73-74
• /
• 2017

SVM(Support Vector Machine) 알고리즘은 대표적인 기계 학습 분류 알고리즘으로 감정 분석, 제스처 인식 등 다양한 분야의 문제를 해결하기 위해 사용되고 있다. SVM 알고리즘은 분리경계면(Hyper-Plane) 또는 분리경계면 집합 중 지지벡터(Support Vector)라 불리는 특정한 점들로 이루어진 두 그룹 간의 거리 차이(Margin)를 최대로 하는 분리경계면을 이용하여 데이터를 분류하는 알고리즘이다. 높은 정확도를 제공하지만 처리 속도가 느리며 학습을 위해 대량의 데이터 및 메모리가 필요하기 때문에 자원이 제한적인 IoT 환경에서 사용이 어렵다. 본 논문에서는 자원이 제한된 IoT 노드를 기반으로 효율적으로 데이터를 학습하기 위해 K-means 알고리즘을 이용하여 SVM 알고리즘의 저전력화 방안을 연구한다.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.18 no.1
• /
• pp.14-24
• /
• 2012

Purpose: This study was conducted to identify the effect of simulation-based education relevant to the care of patients with acute renal failure (ARF) for third-year nursing students. Methods: This study was a non-equivalent control pre-posttest design. Based on the clinical situation scenarios pertaining to patients with ARF, a simulation-based learning module was developed using Human Patient Simulator version 6 (HPS6) manufactured by Medical Education Technologies Inc. The pretest was conducted so as to evaluate the difference in prior knowledge and clinical competence between two groups. The control group consisted of 91 students during the 2010 academic year and the experimental group consisted of 94 students during the 2011 academic year. Data were analysed using SPSS/win 10.1. Results: In the experimental group, knowledge related to care for ARF patients was not significantly increased; however, clinical competence improved significantly for the experimental group. Conclusion: In conclusion, the simulation-based education program was effective in contributing towards the development of clinical competence. Increased development of clinical competence is vital for today's clinical environment where nursing professionals need the necessary knowledge, thinking, and performance skills to meet the needs of the hospital and their patients.