• Journal of the Korean School Mathematics Society
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• v.14 no.3
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• pp.277-293
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• 2011

The purpose of mathematics education is to develop the ability of transforming various problems in general situations into mathematics problems and then solving the problem mathematically. Various teaching-learning methods for improving the ability of the mathematics problem-solving can be tried. However, it is necessary to choose an appropriate teaching-learning method after figuring out students' level of understanding the mathematics learning or their problem-solving strategies. The error analysis is helpful for mathematics learning by providing teachers more efficient teaching strategies and by letting students know the cause of failure and then find a correct way. The following subjects were set up and analyzed. First, the error classification pattern was set up. Second, the errors in the solving process of the function problems were analyzed according to the error classification pattern. For this study, the survey was conducted to 90 first grade students of ${\bigcirc}{\bigcirc}$high school in Chung-nam. They were asked to solve 8 problems in the function part. The following error classification patterns were set up by referring to the preceding studies about the error and the error patterns shown in the survey. (1)Misused Data, (2)Misinterpreted Language, (3)Logically Invalid Inference, (4)Distorted Theorem or Definition, (5)Unverified Solution, (6)Technical Errors, (7)Discontinuance of solving process The results of the analysis of errors due to the above error classification pattern were given below First, students don't understand the concept of the function completely. Even if they do, they lack in the application ability. Second, students make many mistakes when they interpret the mathematics problem into different types of languages such as equations, signals, graphs, and figures. Third, students misuse or ignore the data given in the problem. Fourth, students often give up or never try the solving process. The research on the error analysis should be done further because it provides the useful information for the teaching-learning process.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.75-80
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• 2020

Recently, the size of the IoT sensor has been decreased and the collecting direction of the IoT sensor for acquiring the data have been changed from 2D to 3D. It makes sensor structure complex. In the fabrication of the complex structure, 3D printing technology has more useful than traditional manufacturing technologies. Among 3D printing technologies, FDM (fused deposition modeling) is a candidate technology to fabricate a small IoT sensor because the price of the machine and the material is cheap. In the FDM process, a 3D shape is made by depositing the melted filament. Recently, the patent of FDM technology is expired and cheat machines are developed based on the open-source. In the FDM process, mechanical properties of a fabricated part is affected by a lots of factors such as the kind of material and process parameters. Among them, infill is affecting the mechanical properties and the production lead time as well. In this work, a new method to optimize the FDM process with the consideration of mechanical property and production lead time was proposed. To verify the method, the fabrications were performed with the different infill rates. The results of tensile tests were analyzed to verify the proposed method.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.513-524
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• 2006

Normalization transform is very useful for finding the overall trend of the time-series data since it enables finding sequences with similar fluctuation patterns. The previous subsequence matching method with normalization transform, however, would incur index overhead both in storage space and in update maintenance since it should build multiple indexes for supporting arbitrary length of query sequences. To solve this problem, we propose a single index approach for the normalization transformed subsequence matching that supports arbitrary length of query sequences. For the single index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. The inclusion-normalization transform normalizes a window by using the mean and the standard deviation of a subsequence that includes the window. Next, we formally prove correctness of the proposed method that uses the inclusion-normalization transform for the normalization transformed subsequence matching. We then propose subsequence matching and index building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to $2.5{\sim}2.8$ times over the previous method. Our approach has an additional advantage of being generalized to support many sorts of other transforms as well as normalization transform. Therefore, we believe our work will be widely used in many sorts of transform-based subsequence matching methods.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.273-282
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• 2005

Real-time Object tracking is an important field in developing vision applications such as surveillance systems and vision based navigation. mean-shift algerian and Condensation algorithm are widely used in robust object tracking systems. Since the mean-shift algorithm is easy to implement and is effective in object tracking computation, it is widely used, especially in real-time tracking systems. One of the drawbacks is that it always converges to a local maximum which may not be a global maximum. Therefore, in a cluttered environment, the Mean-shift algorithm does not perform well. On the other hand, since it uses multiple hypotheses, the Condensation algorithm is useful in tracking in a cluttered background. Since it requires a complex object model and many hypotheses, it contains a high computational complexity. Therefore, it is not easy to apply a Condensation algorithm in real-time systems. In this paper, by combining the merits of the Condensation algorithm and the mean-shift algorithm we propose a new model which is suitable for real-time tracking. Although it uses only a few hypotheses, the proposed method use a high-likelihood hypotheses using mean-shift algorithm. As a result, we can obtain a better result than either the result produced by the Condensation algorithm or the result produced by the mean-shift algorithm.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.1-12
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• 2003

Recently, there have been many indexing schemes for multimedia data such as image, video data. But recent database applications, for example data mining and multimedia database, are required to support multi-user environment. In order for indexing schemes to be useful in multi-user environment, a concurrency control algorithm is required to handle it. So we propose a concurrency control algorithm that can be applied to CBF (cell-based filtering method), which uses the signature of the cell for alleviating the dimensional curse problem. In addition, we extend the SHORE storage system of Wisconsin university in order to handle high-dimensional data. This extended SHORE storage system provides conventional storage manager functions, guarantees the integrity of high-dimensional data and is flexible to the large scale of feature vectors for preventing the usage of large main memory. Finally, we implement the web-based image retrieval system by using the extended SHORE storage system. The key feature of this system is platform-independent access to the high-dimensional data as well as functionality of efficient content-based queries. Lastly. We evaluate an average response time of point query, range query and k-nearest query in terms of the number of threads.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.337-344
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• 2006

In this paper, we describe the design and the implementation of word clustering system using a definition of an entry word in the dictionary, called a dictionary definition. Generally word clustering needs various features like words and the performance of a system for the word clustering depends on using some kinds of features. Dictionary definition describes the meaning of an entry in detail, but words in the dictionary definition are implicative or abstractive, and then its length is not long. The word clustering using only features extracted from the dictionary definition results in a lots of small-size clusters. In order to make large-size clusters and improve the performance, we need to transform the features into more general words with keeping the original meaning of the dictionary definition as intact as possible. In this paper, we propose two methods for extending the dictionary definition using ontology. One is to extend the dictionary definition to parent words on the ontology and the other is to extend the dictionary definition to some words in fixed depth from the root of the ontology. Through our experiments, we have observed that the proposed systems outperform that without extending features, and the latter's extending method overtakes the former's extending method in performance. We have also observed that verbs are very useful in extending features in the case of word clustering.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.889-900
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• 2006

Even though sensor networks are different in user requests and data characteristics depending on each application area, the existing researches on stream data transmission problem focus on the performance improvement of their methods rather than considering the original characteristic of stream data. In this paper, we introduce a hierarchical or distributed sensor network architecture and data model, and then evaluate the multivariate data reduction methods suitable for user requirements and data features so as to apply reduction methods alternatively. To assess the relative performance of the proposed multivariate data reduction methods, we used the conventional techniques, such as Wavelet, HCL(Hierarchical Clustering), Sampling and SVD (Singular Value Decomposition) as well as the experimental data sets, such as multivariate time series, synthetic data and robot execution failure data. The experimental results shows that SVD and Sampling method are superior to Wavelet and HCL ia respect to the relative error ratio and execution time. Especially, since relative error ratio of each data reduction method is different according to data characteristic, it shows a good performance using the selective data reduction method for the experimental data set. The findings reported in this paper can serve as a useful guideline for sensor network application design and construction including multivariate stream data.

• The KIPS Transactions:PartC
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• v.17C no.1
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• pp.15-26
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• 2010

Social Networks such as 'Facebook' and 'Myspace' are regarded as useful tools for people to share interests and maintain or expand relationships with other people. However, they pose the risk that personal information can be exposed to other people without explicit permission from the information owner. Therefore, we need a solution for this problem. Although existing social network sites allow users to specify the exposing range or users who can access their personal information, this cannot be a practical solution because the information can still be revealed to third parties through the permitted users albeit unintentionally. Usually, people allow the access of unknown person to personal data in online social networks and this implies the possibility of information leakage. We could use an access control method based on trust value, but this has the limitation that it cannot reflect the quantitative risk of information leakage. As a solution to this problem, this paper proposes an access control method based on a synthesized metric from trust and risk factors. Our various experiments show that the risk of information leakage can play an important role in the access control of online social networks.

• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.45-52
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• 2008

In this paper we define a vulnerable code to symbolic link exploit and propose a technique to detect this using program analysis. The existing methods to solve symbolic link exploit is for protecting it, on accessing a temporary file they should perform an investigation whether the file is attacked by symbolic link exploit. If programmers miss the investigation, the program may be revealed to symbolic link exploit. Because our technique detects all the vulnerable codes to symbolic link exploit, it helps programmers keep the program safety. Our technique add two type qualifiers to the existing type system to analyze vulnerable codes to symbolic link exploit, it detects the vulnerable codes using type checking including the added type qualifiers. Our technique detects all the vulnerable codes to symbolic link exploit automatically, it has the advantage of saving costs of modifying and of overviewing all codes because programmers apply the methods protecting symbolic link exploit to only the detected codes as vulnerable. We experiment our analyzer with widely used programs. In our experiments only a portion of all the function fopen() is analyzed as the vulnerabilities to symbolic link exploit. It shows that our technique is useful to diminish modifying codes.

• Journal of Periodontal and Implant Science
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• v.30 no.2
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• pp.347-360
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• 2000

Bone remodeling results from the combined process of bone resorption and new bone formation which is regulated in part by some of the polypeptide growth factors such as platelet derived growth factor(PDGF), which has been known to be an important local regulator of bone cell activity and participate in normal bone remodeling. This process includes strictly regulated gene expression of several bone matrix proteins such as type I collagen and osteopontin, a 44 kDa phosphorylated glycoprotein, which has important roles in bone formation. The purpose of this study is to evaluate the effecs of PDGF-BB on the mRNA expression of bone matrix protein, type I collagen and osteopontin, in MC3T3- E1 cell culture. Cells were seeded at $5{\times}10^5$ cells in 10 ml of minimum essential medium alpha(${\alpha}-MEM$) containig 10% fetal bovine serum, 10 mM beta glycerophosphate. 0.1, 1, 10 ng/ml PDGF-BB were added to the cells for the day 3, 7, 14, 21, 28 and cultured for 24 hours. Type I collagen cDNA, Hf677, and osteopontin cDNA were used as probes for northern blot analysis. Total cellular RNA was purified at indicated day and northern blot analysis was performed. The results were as follows : Type I collagen mRNA expressions were higher at the day 3 and 7, and lower in the day 14, 21 in the control groups. In the experimental groups, mRNA expressions were increased when 0.1 ng/ml PDGF-BB were added on the day 3, 7, 21, and decreased in dose-dependent manner on the day 14, decreased at all added dose on the day 28. Osteopontin mRNA expressions were highest in the day 21 groups and lowest in the day 14 groups in the control groups. Interesting results were shown in the day 14 and 21 groups. We found that osteopontin mRNA level was increased in dose dependent manner in the day 14 groups, and decreased dose dependent manner in the day 21 groups. In conclusion, PDGF-BB may have various control effects on type I mRNA expression in the growth and differentiation process of MC3T3-E1 cells and may have contrary regulatory effects on osteopontin mRNA expression. For examples, when the baseline level of osteopontin mRNA was low, as in the day 14, PDGF-BB up-regulated osteopontin mRNA expression in dose dependent manner, and when the baseline level was high as in the day 21, PDGF-BB down-regulated dose dependent manner. Thus, it may be useful for clinical application in periodontal regeneration procedure if further study were performed.