• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.6
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• pp.535-539
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• 2005

The thermoacidophilic archaeon Thermoplasma acidophilum has long been known to utilize D-glucose via the non-phosphorylated Entner-Doudoroff (nED) pathway. We now report the identification of a gene encoding 2-keto-3-deoxy-D-gluconate (KDG) kinase. The discovery of this gene implies the presence of a glycolysis pathway, other than the nED pathway. It was found that Ta0122 in the T. acidophilum genome corresponded to KDG kinase. This enzyme shares no similarity with known KDG kinases, and belongs to a novel class of sugar kinases. Of the five sugars tested only KDG was utilized as a substrate.

• Structural Engineering and Mechanics
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• v.6 no.8
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• pp.857-870
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• 1998

This paper deals with a special class of inverse problems in discrete structural plasticity involving the identification of elastic limits and hardening moduli on the basis of information on displacements. The governing equations lead naturally to a special and challenging optimization problem known as a Mathematical Program with Equilibrium Constraints (MPEC), a key feature of which is the orthogonality of two sign-constrained vectors or so-called "complementarity" condition. We investigate numerically the application of two simple algorithms, both based on the use of the general purpose nonlinear programming code CONOPT accessed via the GAMS modeling language, for solving the suitably reformulated problem. Application is illustrated by means of two numerical examples.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.30-35
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• 2023

This study is an example of a class of bibliotherapy through Lee Chung-joon's novel Snowy Road. Bibliotherapy proceeds through the process of identification, catharsis, output, insight, and application through reading. Through research, students objectively examine the situation of the character in the novel and compare it with their own situation. Students evaluate the situation of the character in the novel, experience the various life positions of the character in the novel by answering "What would you do if I were a character in the novel," and express their willingness to live differently from their lives. At the same time, I look into my relationship with my parents and seriously think about whether there is a problem and how to solve it if there is one. Through this process, students specifically suggest ways to think about and solve their emotions and problems. In the end, students' hurt feelings can be partially or sufficiently healed through reading. Through this study, it is expected that the method of bibliotherapy will be more concrete and develop in a positive direction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• Journal of Science Education
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• v.36 no.2
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• pp.251-262
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• 2012

In this study, we develop 15 learning programs to enhance the variable identification and control abilities for the middle school students and analyze the effect of the programs applied to the class. To increase the learning effect of the variable identification and control abilities, we design the programs so that the students can monitor their thinking processes and also they can evaluate the results from their cognitive activities objectively. We analyze the effect of the programs applied to the class and the results show that the test group, which uses the program, marks higher scores in the variable identification abilities compared to the control group. Also, the test group has the increased level of logic to control the variables. Especially, the effect is higher with the students who do not have any logic to control the variables. From the results, we know that it is possible to improve the variable identification and control abilities of the students through the meta-cognitive learning programs developed by us. Furthermore, the results show that the score of variable control abilities positively correlate with that of meta-cognitive level. It means that the meta-cognitive strategy meaningfully increases the variable control abilities of middle school students.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1B
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• pp.17-26
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• 2005

RFID(Radio Frequency IDentification) is a technology that automatically identifies objects attached with electronic tags by using radio wave. For the implementation of an RFID system, an anti-collision algorithm is required to identify several tags within the RFID reader's range. Few researches report the performance trade-off among anti-collision algorithms in terms of the communications traffic between the reader and tags, the identification speed, and so on. In this paper, we analyze both tree based memoryless algorithms and slot aloha based algorithms that comprise of almost every class of existing anti-collision algorithms. To compare the performance, we evaluated each class of anti-collision algorithms with respect to low-cost RFID system with 96-bit EPC(Electronic Product Code). The results show that the collision tracking tree algorithm outperforms current tree based and aloha based algorithms by at least 2 times to 50 times.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.441-447
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• 2022

In this paper, we propose a pill identification model using engraved text feature and image feature such as shape and color, and compare it with an identification model that does not use engraved text feature to verify the possibility of improving identification performance by improving recognition rate of the engraved text. The data consisted of 100 classes and used 10 images per class. The engraved text feature was acquired through Keras OCR based on deep learning and 1D CNN, and the image feature was acquired through 2D CNN. According to the identification results, the accuracy of the text recognition model was 90%. The accuracy of the comparative model and the proposed model was 91.9% and 97.6%. The accuracy, precision, recall, and F1-score of the proposed model were better than those of the comparative model in terms of statistical significance. As a result, we confirmed that the expansion of the range of feature improved the performance of the identification model.

• MALSORI
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• no.54
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• pp.73-83
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• 2005

The goal of our research is to build a text-independent speaker identification system that can be used in any condition without any additional adaptation process. The performance of speaker recognition systems can be severely degraded in some unknown mismatched microphone and noise conditions. In this paper, we show that PCA(principal component analysis) can improve the performance in the situation. We also propose an augmented PCA process, which augments class discriminative information to the original feature vectors before PCA transformation and selects the best direction for each pair of highly confusable speakers. The proposed method reduced the relative recognition error by 21%.

• Journal of the Korean Mathematical Society
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• v.47 no.5
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• pp.1077-1095
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• 2010

The paper considers the identifiability (i.e., the unique identification) of a composite string in the class of piecewise constant parameters. The 1-D string vibration is measured at finitely many observation points. The observations are processed to obtain the first eigenvalue and a constant multiple of the first eigenfunction at the observation points. It is shown that the identification by the Marching Algorithm is continuous with respect to the mean convergence in the admissible set. The result is based on the continuous dependence of eigenvalues, eigenfunctions, and the solutions on the parameters. A numerical algorithm for the identification in the presence of noise is proposed and implemented.