• Journal of Drive and Control
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• v.12 no.3
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• pp.11-17
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• 2015

An intelligent deburring control (IDC) device is used to control the constant force for a deburring tool mounted on the end-effector of a robotic arm. This device maintains a constant contact force between the deburring tool and the workpiece in order to provide a good deburring performance. In this paper, we build a mathematical model in Matlab/Simulink to estimate the force control mechanism of the pneumatic system for the IDC device. The Simulink blocks are built for each separate part and are linked into an integrated simulation system. Such a model also relies on the effects of the flow rate through the valve, air compressibility in the cylinder, and time delay in the pressure valve. The results of the simulation are compared to a simple experiment in which convenient math modeling is performed. These results are then used to optimize the mechanical design and to develop a force control algorithm for the pneumatic cylinder.

• Research in Mathematical Education
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• v.25 no.4
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• pp.267-284
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• 2022

In response to the current educational situation of students' heavy workload, the author constructs the precision-targeted classroom based on Precision Teaching (PT), Network Pharmacology, and Treatment Based on Syndrome Differentiation. The precision-targeted classroom can solve the current problems of PT and the phenomenon of the heavy academic burden on students, achieve the reduction of the burden and increase the efficiency of education. The precision-targeted classroom includes five key points: targeted goals, childlike thinking, precise intervention, intelligent homework, and stereoscopic evaluation, and the implementation process of the precision-targeted classroom is built from three aspects: before, during and after class. In addition, the author applied it to the actual mathematics classroom to test its teaching effect, and the experimental results showed that: the precision-targeted classroom significantly improved students' academic performance and thinking level; considerably improved students' classroom learning status, and facilitated teaching personalization and realized homework quantity control and quality improvement.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.315-335
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• 2010

The goal of this research was to study the effects of the Mathematical Problem Generating Program on problem solving ability and learning attitude. The experiment was carried out between two classes. One class was applied with the experimental program (treatment group), and the other continued with normal teaching and learning methods (comparative group). In this study, two 5th grade elementary classes participated in Seoul city. In this study, the students were tested their problem solving abilities by the IPSP test and learning attitude by the Korean Education Development Institute (KEDI) before and after use of the program. The collected results were t-tested to find any meaningful changes. The results showed the followings. First, use of the mathematical generating program showed meaningful progressive results in problem solving ability. Second, the students that used the program showed positive results in learning attitude. In conclusion, learning mathematics using the problem generating method helps students deeper understand and solve complex problems. In addition, problem solving abilities can be improved and the attitude towards mathematics can be changed while students are using an active and positive approach in problem solving processes.

• Communications of Mathematical Education
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• v.28 no.4
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• pp.573-588
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• 2014

How will the field of education react to the big data craze that has recently seeped into every aspect of society? To search for ways to use big data in mathematics education, this study first examined the concept of big data and examples of its application, and then pursued directions for future research in two ways. First, changes in the representation and acceptance of data are required because of changes in technology and the environment. In other words, the learning content and methodology of data treatment need to be changed by describing a myriad amount of data visually or by 'analyzing and inferring' data to provide data efficiently and clearly. Additionally, the mathematics education field needs to foster changes in curricula to facilitate the improvement of students' learning capacity in the 21st century. Second, it is necessary to more actively collect data on general education and not merely on teaching or learning to identify new information, pursue positive changes in the teaching and learning of mathematics, and stimulate interest and research in the field so that it can be used to make policy decisions regarding mathematics education.

• Education of Primary School Mathematics
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• v.9 no.2 s.18
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• pp.89-105
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• 2005

The purposes of this study were to develop an effective teaching material for slow learners in mathematics and to investigate its effect. To achieve the first goal, several pre-used teaching material and the 7th national curriculum for elementary school mathematics were analyzed to set up a framework fur developing new teaching material. Using these developed framework and curriculum data, 370 units of lesson were developed from the 3rd grade to the 6th grade. To investigate the effect of the material, 3 slow learners (2 from the 5th and 1 from the 6th grade) were selected through diagnostic tests. Then supplementary lessons were administered after school to relieve their disability accordingly for seven months. During the lessons(lasted about 40 minutes), teacher observed the subjects in detail and .judged the teaming sequence and the learning pace. Through this observation and the test administered after the treatment, several conclusions were drawn as follow: First, the supplementary lessons using the developed teaching material helped slow learners understand mathematics and solve problems. Especially, the test scores gained on formative evaluation became higher. This might be caused by the material that enabled to relieve the disablement and the teaching method that aimed to give a meaningful mathematical experience. Second, the supplementary lessons affected positively to the affective domain of the slow learners. They convinced themselves to their mathematical ability and became active in their mathematics class. This was observed by researcher and the class teacher in their lessons. Positive attitude toward mathematics and their ability is quite important for mathematics learning especially fur slow learners in mathematics.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Communications of Mathematical Education
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• v.31 no.1
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• pp.17-48
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• 2017

This case study examined mathematics anxiety of a public high school sophomore who was unable to perform well in mathematics but later overcame his fear of mathematics. In this study, he showed high levels of mathematics anxiety in the assessment tools that evaluate mathematical anxiety factors. Cognitive and behavior treatments were carried out to alleviate his anxiety. First, cognitive treatments that were implemented include: understanding his own problems, writing down his thoughts on a record sheet, and changing intermediate and core beliefs. This paper explored cognitive and affective changes and reactions during the treatment process. Second, behavioral treatments that were conducted include: the divided-page method and peer tutoring. The divided-page technique involves the test subject to write down and solve his problems on a note to see what kind of cognitive and affective changes occur during the process. This paper also explored how Su-chul, an overly competitive student, changed and reacted cognitively and affectively through peer tutoring. The results revealed that Su-chul's exam anxiety, as well as other factors, has decreased. Moreover, he regained his self-confidence by solving math problems that he had felt difficult. His competitive attitude also has turned into a cooperative and thoughtful one.

• Journal of the Korean Chemical Society
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• v.48 no.2
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• pp.182-188
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• 2004

In this study, the effects of a four-stage problem-solving strategy emphasizing planning and checking stages were investigated. Two high school classes (N=55) were randomly assigned to either treatment or control group, and taught about two topics, 'gas' and 'solution' for 8 class hours. Teacher used the four-stage problem-solving strategy emphasizing planning and checking stages in the treatment group, and used traditional lecture in the control group. Two-way ANCOVA results revealed that the test scores of the treatment group were significantly higher than those of the control group in the problem-solving ability, especially in the subcategories of 'conceptual knowledge' and 'mathematical execution'. There was significant interaction between the instruction and the level of prior achievement in the 'satisfaction' of the learning motivation. The lower level students in the control group were more satisfied with chemistry class than those in the treatment group. There was no significant difference between the two groups in the scores of the awareness of metacognition. Educational implications are discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1706-1709
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• 2010

Sewerage Treatment Plants(STPs) are complexes systems in which a range of physical, chemical and biological processes occur. Since Activated Sludge Model(ASM) No.1 was published, a number of new mathematical models for simulating biological processes have been developed. However, these models have disadvantages in cost and simplicity due to the laboriousness and tediousness of their procedures. One of the major difficulties of these mathematical model based tools is that the field-operators mostly don't have the time or the computer-science skills to handle there models, so it mainly remains on experts or special engineers. In order to solve these situations and help the field-operators, the $KM^2BM$(K-water & More-M Mass Balance Model) based on the dynamic-mass balance model was developed. This paper presents $KM^2BM$ as a simulation tools for STPs design and optimization. This model considers the most important microbial behavioral processes taking place in a STPs to maximize potential applicability without increasing neither model parameter estimation nor wastewater characterization efforts.