• 한국수학교육학회지시리즈A:수학교육
• /
• 제50권1호
• /
• pp.13-25
• /
• 2011

The Goals of mathematics education for the lower grades of primary school is to shape the basic concepts and the skills of mathematics. To achieve this goal, it is necessary an assessment which is able to help the students' learning activities by precisely diagnosing their basic mathematical capability. It should lend the students an assistance in diagnosing and revising their problems throughout teacher's cognitive participation in the process of mathematical problem solving. I would like to suggest the dynamic assessment as one of these kinds of approaches. In order to prove the utilities of this way, it was examined the necessity of dynamic assessment on the basis of the Vygotsky's theory after looking into the characteristics of the contents and methods of the mathematics education for the lower grades of primary school. Next, I researched the principles of the dynamic assessment and embodied the assessment tool to evaluate the mathematical achievement of the lower grades of the primary school. Lastly, it was provided the examples of the dynamic assessment tool in order to assist the practice of it.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권8호
• /
• pp.3270-3294
• /
• 2020

Some things come easily to humans, one of them is the ability to navigate around. This capability of navigation suffers significantly in case of partial or complete blindness, restricting life activity. Advances in the technological landscape have given way to new solutions aiding navigation for the visually impaired. In this paper, we analyze the existing works and identify the challenges of path selection, context awareness, obstacle detection/identification and integration of visual and nonvisual information associated with real-time assisted mobility. In the process, we explore machine learning approaches for robotic path planning, multi constrained optimal path computation and sensor based wearable assistive devices for the visually impaired. It is observed that the solution to problem is complex and computationally intensive and significant effort is required towards the development of richer and comfortable paths for safe and smooth navigation of visually impaired people. We cannot overlook to explore more effective strategies of acquiring surrounding information towards autonomous mobility.

• Smart Structures and Systems
• /
• 제22권2호
• /
• pp.231-237
• /
• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• 공학교육연구
• /
• 제18권6호
• /
• pp.70-79
• /
• 2015

This paper proposes a convergence education method that combines architecture and communication through a documentary film entitled the Talking Architecture. The purpose of this program is to propose a new teaching and learning method for architecture education and investigates what would be an effective method of communication for architects. As mentioned above, this paper proposes a teaching method and a model applicable to actual classes based on the Talking Architect. It is proved that the method can be used for various types of classes and fields such as architectural expression, architectural planning and designing, housing theory, building structure, building materials, and other subject matters. In addition, this paper explores how architects communicate as described in the film. The findings show the potential of integrity, negotiating capability, and the convergent method of thinking and communication between the humanities and architecture as a positive communication model for architects. This paper opens up the possibilities for convergence education in the field of engineering education through three key words: film, architecture, and communication. And this paper is worth in that it is a useful method for developing convergence courses and team-teaching courses.

• Earthquakes and Structures
• /
• 제18권4호
• /
• pp.493-506
• /
• 2020

In this paper, a new opposition based charged system search (CSS) is proposed to be used as a parameter identification of highly nonlinear semi-active magneto-rheological damper. By replacing the opposition particles with current solutions, the mentioned strategy is used to enhance the search space and to increase the exploration of CSS. To investigate the effectiveness of the proposed method, a nonlinear modified Bouc-Wen model of MR damper is considered to find its parameters, and compare it with those achieved from experimental model of MR damper. Also, by exploiting the sensitivity analysis and using the importance vector, the less importance parameters in the Bouc-Wen model are eliminated which makes the MR damper model simpler. Results demonstrate the new proposed algorithm (OBLCSS) has a high ability to tackle highly nonlinear problems. Based on the results of the α importance vector, a simplified model is proposed and its parameters are identified by using the presented OBLCSS algorithm. The simplified proposed model also has a high capability of estimating damper responses.

• 대한기계학회논문집A
• /
• 제24권7호
• /
• pp.1661-1672
• /
• 2000

An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this pap er, the combined optimization algorithm (Immune- Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed combined algorithm is identified by comparing the result of optimization with simple genetic algorithm for two dimensional multi-peak function which have many local optimums. Also the new combined algorithm is applied to minimize the total weight of the shaft and the transmitted forces at the bearings. The inner diameter oil the shaft and the bearing stiffness are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm and reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic conatriants.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제55권2호
• /
• pp.89-97
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 디지털산업정보학회논문지
• /
• 제8권2호
• /
• pp.145-160
• /
• 2012

The globe-trotting trend has been converted from tourism for the simple purposes of sightseeing and enjoyment to a form of learning and practicing the more specialized contents by directly participation. Traditional cultural resources have sufficient potential as culture and tourism assets that fit this kind of trend. Therefore, building proper tourist resources for their application has become a very urgent and important matter. To make masterpiece tourism packages through combined services of traditional cultural resources, it is necessary for operators to ponder diverse methods that can be used to develop various experience programs and conserve traditional cultural properties by continuously generating profits. The major results of this research are as follows. Firstly, this study proposes plans for unified tourist information and combined services of traditional cultural resources based on ubiquitous IT. Secondly, it is ascertained that the organization of combined operational consultative groups and the improvement of operator' capability are required to execute combined services of traditional cultural resources. Thirdly, we propose business plans to generate profits in both product aspect and network aspect.

• 한국기술혁신학회:학술대회논문집
• /
• 한국기술혁신학회 2000년도 추계 학술대회(The 2000 Autumn Conference of korea Technology Inovation Society)(한국기술혁신학회)
• /
• pp.227-245
• /
• 2000

Korean firms have attempted to catch up in the aircraft industry during last quarter century. Korean firms have built up their capabilities by moving from parts manufacturing through subassembly to system integration. The number of projects carried out and the intensity of technological effort undertaken by firms strongly influences market position and firm performance. However, successful catching up is not simply dependent on capability building within the firm. The national environment (Porter, 1990) in which firms are located plays a pivotal role. The Korean government has been effective in creating a favorable environ-ment in many areas, but has not been able to replicate this success in the aircraft industry. Opportunities for learning in the aircraft industry have been hampered by the small size of the Korean civilian aircraft market and the sophisticated requirements of military systems. A policy of domestic rivalry in airframe manufacture has created too many firms for such a small market. The ability of Korean firms to catch up in the aircraft industry depends on both the internal capabilities of firms as well as appropriate government policies and the involve- ment of government research institutions and universities over an extended period of time. There have been many studies about the catching up of developing countries in mass production (such as automobile, consumer electronics, and recently DRAM), but few in complex systems, such as aircraft.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권1호
• /
• pp.288-301
• /
• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.