• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.333-339
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• 2004

This Paper proposes the vector quantizer-pyramid vector quantizer(VQ-PVQ) structure. Also both predictive structure and safety-net concept are combined into the VQ-PVQ to quantize the IPC parameter of wideband speech codec. The Performance is compared to the LPC vector quantizer used in the AMR-WB(ITU-T G.722.2). demonstrating reduction in both spectral distortion and encoding memory.

• Journal of Plant Biotechnology
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• v.6 no.4
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• pp.221-227
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• 2004

Protocol was established for production of virus free healthy seeds using meristem ($0.3-0.5\;\cal{mm}$ in size) culture and field management under net house condition in tomato. The isolated meristem was found well established in MS liquid medium containing $0.1\;\cal{mg}\;1^{-1}\;of\;GA_3$. For shoot and root development either from primary meristem or from nodal segment of meristem derived plants, semisolid MS medium having $0.5\;\cal{mg}\;1^{-1}$ of IBA was found most effective. The elimination of the studied viruses (ToMV, CMV, ToLCV) in meristem-derived plants was confirmed by DAS-ELISA test. For field management of the virus eradicated meristem-derived plants, use of net house was found very effective measures to check viral vector visit and eventually infection. The meristem-derived plants were vigor and high yielder than the native seed derived plants and produced healthy seeds. Due to stop vector visit, no viral symptoms were observed in both $R_1\;and\;R_2$ plants cultivated in net house condition. Starting of viral infestation was observed in $R_2$ generation when they were planted in open house condition without control of vector visit. Therefore, for management of viral diseases, use of virus free meristem derived plantlets and their subsequent cultivation in soil under net house condition without using any vector killing insecticide can be recommended for producing healthy seeds in tomato. The developed protocol for environmentally healthy tomato seed production in Bangladesh may be used in the countries having similar tropical like environment conducive for viral vector visit.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.3
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• pp.63-70
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• 2020

Two rotational motions of the 5-axis machine tool maximize the degree of freedom of the tool axis vector, which improves tool accessibility; however, this lowers feed speed and rigidity, which impairs machining stability. In addition, cutting efficiency is lowered when compared with a flat end mill because typically, the ball-end mill is used when machining by rotational motion. This study increased cutting efficiency by using a corner radius flat end mill during impeller roughing. Furthermore, we proposed a fixed controlled machining of the rotary motion using geometric shape information to improve the feed speed and machining stability. Finally, we proposed a finishing tool path generation method using a vector net to increase the convenience and practicality of tool path generation. To verify its effectiveness, we compared the machining time, shape accuracy, and surface quality of the proposed method and an existing dedicated module.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.43-49
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• 1995

In this paper a space search technique is used to detect and locate the faults of the circuit interconnections. The circuit interconnections are represented by the tree structure and the tree space is searched to detect and locate the open faults of the circuit interconnections. The breadth search is used to detect the open faults and reduce the space size. The depth search is used to locate the open faults.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.631-636
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• 2007

An efficient obstacle avoidance algorithm is proposed in this paper to avoid dynamic obstacles using a collision vector while a tele-operated mobile robot is moving. For the verification of the algorithm, an operator watches through a monitor and controls the mobile robot with a force-reflection joystick. The force-reflection joystick transmits a virtual force to the operator through the Inter-net, which is generated by an adaptive impedance algorithm. To keep the mobile robot safe from collisions in an uncertain environment, the adaptive impedance algorithm generates the virtual force which changes the command of the operator by pushing the operator's hand to a direction to avoid the obstacle. In the conventional virtual force algorithm, the avoidance of moving obstacles was not solved since the operator cannot recognize the environment realistically by the limited communication bandwidth and the narrow view-angle of the camera. To achieve the dynamic obstacle avoidance, the adaptive virtual force algorithm is proposed based on the collision vector that is a normal vector from the obstacle to the mobile robot. To verify the effectiveness of the proposed algorithm, mobile robot navigation experiments with multiple moving obstacles have been performed, and the results are demonstrated.

• ETRI Journal
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• v.30 no.5
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• pp.738-740
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• 2008

In this paper, block constrained trellis coded vector quantization (BC-TCVQ) is presented for quantizing the line spectrum frequency parameters of the wideband speech codec. Both a predictive structure and a safety-net concept are combined into BC-TCVQ to develop the predictive BC-TCVQ. The performance of this quantization is compared with that of the linear predictive coding vector quantizer used in the AMRWB codec, demonstrating reductions in spectral distortion.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.59-59
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• 2000

In this paper, we present a framework for modeling and control of multiple mobile robots which cowork within a bounded workspace and limited resources. To achieve this goal, we adopt a formalism of discrete event system and supervisory control theory based on Petri nets. We can divide our whole story into two parts: first, we search the shortest path using the distance vector algorithm, and then we construct the control scheme from which a number of mobile robots can work within a bounded workspace without any collision. The use of Petri net modeling allows us In synthesize a controller which achieves a control specification for the desired closed-loop behavior efficiently. Finally, the usefulness of the proposed Petri net formalism is illustrated by a simulation study.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.127-135
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• 2020

This study aims to measure the effects of real interest rate (RIR), gross domestic savings (GDS), and net exports (EN) shocks on Indonesia's economic growth (EG). The focus on Indonesia is unique due to the abundant resources available in the nation, but they are unsuccessful in boosting economic growth. This study applied a quantitative method to comprehensively analyze the correlation between variables by employing Vector Autoregression Model (VAR) combined with Vector Error Correction Model (VECM). Various procedures are preformed: Augmented Dickey-Fuller test (ADF), Optimum Lag Test, Johansen Cointegration Test, Granger Causality Test, as well as Impulse Response Function (IRF) and Error Variance Decomposition Analysis (FEVD). The data were collected from the World Bank and the Asian Development Bank from 1986 to 2017. The findings of the study indicated that economic growth responded positively to real interest rate shocks, which implies that when the real interest rate experiences a shock (increase), the economy will be inclined to growth. While, economic growth responded negatively to gross domestic savings and net export shocks. Policymakers are expected to consider several matters, particularly the economic conditions at the time of formulating policy, so that the prediction effectiveness of a policy can be appropriately assessed.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.