• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.69-75
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• 2021

Limitation of the TCP/IP network technology included in the vehicle communication is due to the frequent mobility of the vehicle, the increase in intermittent connection requirements, and the constant presence of the possibility of vehicle hacking. VNDN technology enables the transfer of the name you are looking for using textual information without the need for vehicle identifiers like IP/ID. In addition, intermittent connectivity communication is possible rather than end-to-end connection communication. The data itself is the subject of communication based on name-based forwarding using two types of packets: Interest packet and Data packet. One of the issues to be solved for the realization of infotainment services under the VNDN environment is the traffic explosion caused by data broadcasting. In this paper, we analyze and compare the existing technologies to reduce the data broadcast storm. Through this, we derive and analyze the requirements for presenting the best data mitigation technique for solving the data explosion phenomenon in the VNDN environment. We expect this paper can be utilized as prior knowledge in researching improved forwarding techniques to resolve the data broadcast explosion in connected vehicles over NDN.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.143-151
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• 2011

When a real-time data transfer is not possible between a reader and a back-end server in the mobile environment, the reader should support a capability to search a certain tag without communicating with a back-end server. Some recent papers related to the mobile reader-based tag search protocol have addressed privacy concerns for the reader and the tags. However, to our best knowledge, there are no papers addressing the problem arising from reusing tag lists existed in the mobile reader. In other words, there arise a problem that a mobile reader which has lost an right to access to a specific tag is able to search that tag by reusing a tag list for searching a particular tag. If mobile reader having an unauthorized tag list, the mobile reader can reuse a particular tag list. Our protocol provides the enhanced secure tag lists preventing the reuse of the tag lists and an efficient tag search protocol based on dynamic identity in the mobile reader-based RFID environments.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.21-30
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• 2021

Economic transformations have led to an increase in the role of creative assets and their central role in public life. Changes in creative activity have led to a change in the organization of the work of institutes engaged in the training of specialists, in particular teachers of labor education. Methods and approaches to training determine the development of creative industries, being the basis for models of professional training of future teachers of labor training. The purpose of an article was to develop a modern model of professional training of future teachers of labor training based on the concept of creative economy. The methodology is based on the concepts of holistic craft and creative economy. Based on the integration of pedagogical learning models "Craft as design and problem-solving", "Craft as skill and knowledge building", "Craft as product-making" and "Craft as self-expression" developed and experimentally confirmed the conceptual model of professional training of future teachers of labor training. The proposed model forms a practitioner with professional, technical, digital and creative skills who is able to transfer the experience to students. The training course "Creativity and creative thinking" has been developed. The model provided for the development of a course based on the strategy of developing professional creativity, flexibility, improvisation, openness, student activity, joint practice, student-oriented approach. The practical value implies the adaptation of the developed model of professional training of future teachers of labor education during the training of teachers in higher education, which is confirmed in the experiment.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.356-363
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• 2021

It was common knowledge in textbooks that images acquired using mid-infrared ray were not suitable for measuring temperature near room temperature. But a recent satellite image using a mid-infrared sensor show the possibility that the result measured using the mid-infrared sensor can also measure the temperature near room temperature. In this paper, the possibility and accuracy of extraction room temperature information from satellite images with mid-infrared sensors are reviewed. The mid-infrared satellite image reviewed in this paper showed the temperature of room temperature well, and regarding the reliability as an absolute value of the measured temperature, the effect of the heat transfer amount due to the direct reflection of sunlight on the surface and the effect of the infrared absorption amount absorbed in the atmosphere can be seen as a relatively small or constant value. However, the problem of uncertainty in the radiation coefficient due to physical properties, which is the limit of the non-contact thermometer, remained a problem to be solved.

• Advances in nano research
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• v.13 no.1
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• pp.97-111
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• 2022

In the present article, silica nanoparticles (SNPs) were exploited to improve the tribological and mechanical properties of vinyl ester/glass fiber composites. To the best of our knowledge, there hasn't been any prior study on the wear properties of glass fiber reinforced vinyl ester SiO₂ nanocomposites. The wear resistance is a critical concern in many industries which needs to be managed effectively to reduce high costs. To examine the influence of SNPs on the mechanical properties, seven different weight percentages of vinyl ester/nano-silica composites were initially fabricated. Afterward, based on the tensile testing results of the silica nanocomposites, four wt% of SNPs were selected to fabricate a ternary composite composed of vinyl ester/glass fiber/nano-silica using vacuum-assisted resin transfer molding. At the next stage, the tensile, three-point flexural, Charpy impact, and pin-on-disk wear tests were performed on the ternary composites. The fractured surfaces were analyzed by scanning electron microscopy (SEM) images after conducting previous tests. The most important and interesting result of this study was the development of a nanocomposite that exhibited a 52.2% decrease in the mean coefficient of friction (COF) by augmenting the SNPs, which is beneficial for the fabrication/repair of composite/steel energy pipelines as well as hydraulic and pneumatic pipe systems conveying abrasive materials. Moreover, the weight loss due to wearing the ternary composite containing one wt% of SNPs was significantly reduced by 70%. Such enhanced property of the fabricated nanocomposite may also be an important design factor for marine structures, bridges, and transportation of wind turbine blades.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.511-518
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• 2022

Supervised learning based on deep learning has made a leap forward in various application fields. However, many supervised learning methods work under the common assumption that training and test data are extracted from the same distribution. If it deviates from this constraint, the deep learning network trained in the training domain is highly likely to deteriorate rapidly in the test domain due to the distribution difference between domains. Domain adaptation is a methodology of transfer learning that trains a deep learning network to make successful inferences in a label-poor test domain (i.e., target domain) based on learned knowledge of a labeled-rich training domain (i.e., source domain). In particular, the unsupervised domain adaptation technique deals with the domain adaptation problem by assuming that only image data without labels in the target domain can be accessed. In this paper, we explore the unsupervised domain adaptation techniques.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.33-41
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• 2022

Infrastructure procurement has been a major engagement route between China and Africa. This contributes immensely to the gradual infrastructure development seen on the continent. However, maturing discourse purports that these infrastructure collaborations lack intentionality in the continuous development of strategic guidelines and policies for effective implementation despite their uniqueness and criticality. This study proposes that an efficient approach to policy recommendations is through the political and economic analysis (PEA) of these partnerships using public-private partnership (PPP) optics. Unquestionably, these partnerships are representative of the concept of diplomatic transnational public-private partnership (DT-PPP) where infrastructure is procured through the collaboration of public (African governments) and private sector (Chinese state-owned corporations) who provide the managerial, financial, and technical resources for the project implementation. Given the quest for sustainable win-win, this study identifies strategies towards the realization of win-win in the implementation (i.e enablers of win-win) such that fairness and co-benefit, as well as interests, will be achieved. Thus, based on the PEA framework, case scenarios from Ghana and Nigeria using expert interviews identify the criticalities and best practices for the realization of these enablers at the development phase. Findings indicate more effort is required of the public sector (African host countries) in terms of people, structure/institutions, and the implementation processes. Recommendations include improvement of environmental management structures, contract administration procedures, external stakeholders/local community engagement mechanisms, knowledge and technology transfer procedures, and sector-based project operation and maintenance culture and systems. Additionally, actors must have emotional intelligence, good problem-solving abilities, and overall ensure cordial relationships for continued bilateral cooperation.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.13-21
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• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.