• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Communications of Mathematical Education
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• v.26 no.1
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• pp.51-69
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• 2012

The cognition of an intrinsic attribute play an important role in the process of theoretical generalization. It is the aim of this paper to study how the theoretical generalization is made. First of all, we suggest the What-if-only-strategy(WIOS) which is the strategy helping the cognition of an intrinsic attribute. And we propose the process of the theoretical generalization that go on the cognitive stage, WIOS stage, conjecture stage, justification stage and insight into an intrinsic attribute in order. We propose the process of generalization adding the concrete process cognizing an intrinsic attribute to the existing process of generalization. And we applied the proposed process of generalization to two mathematical theorem which is being managed in middle school. We got a conclusion that the what-if-only strategy is an useful method of generalization for the proposition. We hope that the what-if-only strategy is helpful for both teaching and learning the mathematical generalization.

• Journal of Veterinary Science
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• v.22 no.1
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• pp.7.1-7.13
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• 2021

Background: Niemann-Pick disease type C (NPC) is caused by the mutation of NPC genes, which leads to the abnormal accumulation of unesterified cholesterol and glycolipids in lysosomes. This autosomal recessive disease is characterized by liver dysfunction, hepatosplenomegaly, and progressive neurodegeneration. Recently, the application of induced neural stem cells (iNSCs), converted from fibroblasts using specific transcription factors, to repair degenerated lesions has been considered a novel therapy. Objectives: The therapeutic effects on NPC by human iNSCs generated by our research group have not yet been studied in vivo; in this study, we investigate those effects. Methods: We used an NPC mouse model to efficiently evaluate the therapeutic effect of iNSCs, because neurodegeneration progress is rapid in NPC. In addition, application of human iNSCs from NPC patient-derived fibroblasts in an NPC model in vivo can give insight into the clinical usefulness of iNSC treatment. The iNSCs, generated from NPC patientderived fibroblasts using the SOX2 and HMGA2 reprogramming factors, were transplanted by intracerebral injection into NPC mice. Results: Transplantation of iNSCs showed positive results in survival and body weight change in vivo. Additionally, iNSC-treated mice showed improved learning and memory in behavior test results. Furthermore, through magnetic resonance imaging and histopathological assessments, we observed delayed neurodegeneration in NPC mouse brains. Conclusions: iNSCs converted from patient-derived fibroblasts can become another choice of treatment for neurodegenerative diseases such as NPC.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.4
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• pp.7-34
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• 2021

The purpose of the study was to explore the uses and gratification of social media among first-year student groups at a satellite campus of a public university in Ghana. The study employed a descriptive survey design. The study involved all 1061 first-year university students in six academic departments of the College. A total of 680 (64%) participants returned validly completed copies of the questionnaire. Descriptive statistics and thematic analysis were employed for data analysis. The findings indicate that WhatsApp was the most popular application for social media groups, while a need for information-sharing, peer-tutoring and learning, and finding and keeping friends were the primary motivations for joining social media groups. First-year students are involved mainly in reactive activities, as most engage when solving an academic assignment through group discussions. Though challenges persist, such as posting of unwanted images, inadequate participation, and ineffective and irrelevant communication, most are willing to continue their social media groups' membership in the long term. This study provides valuable insight into transitioning students' lived experiences on social media from the group perspective. These insights are valuable conceptually and practically to academic counsellors, librarians and student affairs officers who are expected to provide on-going education on (social) media literacy to first-year students to enhance the adjustment process. The study is the first of its kind in Ghana that investigates social media group participants' exit intentions.

• Phonetics and Speech Sciences
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• v.13 no.4
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• pp.47-53
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• 2021

Autoregressive Text-to-Speech (TTS) models suffer from inference instability and slow inference speed. Inference instability occurs when a poorly predicted sample at time step t affects all the subsequent predictions. Slow inference speed arises from a model structure that forces the predicted samples from time steps 1 to t-1 to predict the sample at time step t. In this study, an end-to-end non-autoregressive fast text-to-speech model is suggested as a solution to these problems. The results of this study show that this model's Mean Opinion Score (MOS) is close to that of Tacotron 2 - WaveNet, while this model's inference speed and stability are higher than those of Tacotron 2 - WaveNet. Further, this study aims to offer insight into the improvement of non-autoregressive models.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.59-66
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• 2022

Students can successfully connect with one another thanks to the introduction of Web 2.0 and the tools and technology linked with it. The fact that rising digital tools are systematically influencing the education system is not a secret. The purpose of the research article efficiently evaluates the influence of incorporation of media in the activities of the scientific library of the higher education institution. The research Methodology is the Concepts, techniques, and procedures to effectively inculcate primary and secondary data to conduct the research effortlessly. It's worth noting that in this case, quantitative primary research was provided in the form of a survey. The researchers have proposed a survey in order to successfully instil a comprehensive view on the "incorporation of media in the operations of the scientific library of higher education institutions." As a result, fifty-one higher education institution principals were asked to attend this session. This is necessary to understand that they are both well-educated and cognizant of the impact of technology innovation on schooling. As a result, the researchers were able to gain a comprehensive view of this situation thanks to this survey. The results effectively showed that most of the participants believe that social media plays a vital role in shaping up higher education and at the same time they believe that the libraries of famous educational institutions must adapt as per the new educational trend so that teachers and students both can tap into its benefit.The practical significance of the result is manoeuvred by the efficient survey analysis and at the same time, peer-reviewed journals have been employed to put forward authentic information. Therefore, efficient insight regarding this topic has been gathered by the researchers.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.427-436
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• 2022

Korea Trade-Investment Promotion Agency (KOTRA) annually publishes the trade data in South Korea under the guidance of the Ministry of Trade, Industry and Energy in South Korea. The trade data usually contains Gross domestic product (GDP), a custom tariff, business score, and the price of export items in previous and this year, with regards to the trading items and the countries. However, it is challenging to figure out the meaningful insight so as to predict the future price on trading items every year due to the significantly large amount of data accumulated over the several years under the limited human/computing resources. Within this context, this paper proposes a multi layer perception that can predict the future price of potential trading items in the next year by training large amounts of past year's data with a low computational and human cost.

• Structural Engineering and Mechanics
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• v.85 no.4
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• pp.469-484
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• 2023

A deep recursive bidirectional Cuda Deep Neural Network Long Short Term Memory (Bi-CuDNNLSTM) layer is recruited in this paper to predict the entire force time histories, and the corresponding hysteresis and backbone curves of reinforced concrete (RC) bridge piers using experimental fast and slow cyclic tests. The proposed stacked Bi-CuDNNLSTM layers involve multiple uncertain input variables, including horizontal actuator displacements, vertical actuators axial loads, the effective height of the bridge pier, the moment of inertia, and mass. The functional application programming interface in the Keras Python library is utilized to develop a deep learning model considering all the above various input attributes. To have a robust and reliable prediction, the dataset for both the fast and slow cyclic tests is split into three mutually exclusive subsets of training, validation, and testing (unseen). The whole datasets include 17 RC bridge piers tested experimentally ten for fast and seven for slow cyclic tests. The results bring to light that the mean absolute error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the experimentally measured values of the force time histories for all the datasets, more than 90%. It can be concluded that the maximum mean of the normalized error, obtained through Box-Whisker plot and Gaussian distribution of normalized error, associated with unseen data is about 10% and 3% for the fast and slow cyclic tests, respectively. In recapitulation, it brings to an end that the stacked Bi-CuDNNLSTM layer implemented in this study has a myriad of benefits in reducing the time and experimental costs for conducting new fast and slow cyclic tests in the future and results in a fast and accurate insight into hysteretic behavior of bridge piers.

• Journal of English Language & Literature
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• v.55 no.3
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• pp.383-398
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• 2009

Based on the Greek myth of Pygmalion and the fairy tale of Cinderella, Shaw's Pygmalion demonstrates a masterful coalescence of these two narrative motifs into a coherent plot scheme. Even more significant is his keen insight into the conflicts created at the tripartite intersection of human activity concerning language/class/culture, which, as the leitmotif, revolves around lessons in language learning. This play basically deals with human transformation and by its very nature, Higgins's experimentation with transforming Eliza cannot stop at language alone. Her cultural transformation ripples over into the realms of gesture and even a unique way of living (modus vivendi) intimately associated with taste and manners, which Bourdieu terms as habitus. By acquiring a new fashion and language, Eliza is reborn as a new lady aspiring to be filled with a newly acquired habitus. While separating her from her old Cockney style, Higgins inculcates Queen's English in Eliza, in which process her changed speech styles gradually transforms and restructures her deportment and manners, finally generating new practices, perceptions and attitudes. The gist of Pygmalion is however less Eliza's ascent into the middle class than her battle for symbolic capital waged at the level of language. By problematizing his contemporary practice of habitus conventionalized and warped by class distinctions based on economic, social and cultural capitals, Shaw creates a new humanist model of man founded on spiritual and rational virtues. In conclusion, Eliza is not a frigid Galatea but a dynamic character that goes through a brilliant transformation of three stages: 1) linguistic; 2) cultural, and 3) humanist. Finally she is built into a "consort battleship" on an equal standing with her sculptor. The process of her character-building cannot be illuminated without resorting to the dynamic notion of habitus, which highlights the process of inculcation, structuring, generation and transposing. Given the overwhelming weight of the heroine's role and the dynamic process of her transformation as the major plot scheme, this play should be christened Galatea in lieu of Pygmalion.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.