• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.99-104
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• 2022

Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect, age and gender classification has been one of the hot topics among computer vision researchers in recent decades. Deployment of deep Convolutional Neural Network(: CNN) models achieved state-of-the-art performance. However, the most of CNN based architectures are very complex with several dozens of training parameters so they require much computation time and resources. For this reason, we propose a new CNN-based classification algorithm with significantly fewer training parameters and training time compared to the existing methods. Despite its less complexity, our model shows better accuracy of age and gender classification on the UTKFace dataset.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.33 no.2
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• pp.35-40
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• 2022

Objectives: Restricted and repetitive behaviors (RRBs) are a core symptom in the diagnosis of autism spectrum disorder (ASD). The complexity of behavioral patterns has called for the creation of phenotypically homogeneous subgroups among individuals with ASD. The purpose of this study was 1) to investigate the different types of RRBs and 2) to explore whether subgroups created by RRBs would show unique levels of functioning in toddlers and young children with ASD. Methods: A total of 313 children with ASD, aged 12-42 months were included in the analysis. The Autism Diagnostic Interview-Revised was used to obtain information on the different types of RRBs by grouping 15 items into six categories. The Vineland Adaptive Behaviors Scale, a parent-reported questionnaire, was used to measure adaptive functioning. A portion of the children were analyzed separately for verbal-related RRBs based on their expressive language level. Two-step cluster analysis using RRB groups as features was used to create subgroups. Analysis of covariance while covarying for age and language was performed to explore the clinical characteristics of each cluster group. Results: Sensory-related RRBs were the most prevalent, followed by circumscribed interests, interest in objects, resistance to change, and repetitive body movements. A subset of the children was analyzed separately to explore verbal-related RRBs. Four cluster groups were created based on reported RRBs, with multiple RRBs demonstrating significant delays in adaptive functioning. Conclusion: Heterogeneity of RRBs emerges at a young age. The different patterns of RRBs can be used as valuable information to determine developmental trajectories with better implications for treatment approaches.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.316-322
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• 2021

The problem of corruption and the spread of corruption crime today is not only one of the main social problems, but also an obstacle to the implementation of reforms in Ukraine. Given the complexity, scale and diversity of the impact of corruption, it is an undisputed threat to national security. At the state level, corruption threatens, firstly, state security as a result of its spread in public authorities and the combination of political and business spheres; secondly, in the domestic political sphere as a result of non-compliance and violation by officials of public authorities and local governments of the laws of Ukraine; thirdly, in the economic sphere as a result of the dominance of personal interests of civil servants over national ones; fourthly, in other spheres, namely, military, social, ecological, informational, foreign policy, etc. The origins of corruption are diverse and are formed not only in the country but also abroad. The current corruption threat is the result of the country's ineffective domestic and foreign anticorruption policies. Acceleration of the spread and manifestation of external corruption threats is associated with a number of unresolved foreign policy issues against the background of the development of globalization and integration processes, in particular: economic and financial dependence of the country on international financial institutions and organizations; as well as from foreign countries that pose a potential threat due to their ambitious plans to expand our country; unresolved issues regarding the international legal consolidation of borders, etc. It is noted that the current conditions for the development of state security, due to new challenges and threats, need to improve and implement new measures to prevent corruption as a negative impact of the main threats to national economic security. As a result of the study, the main measures to counter the main threats to the economic security of the state were identified.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.303-308
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• 2022

Purpose: This technical report aims to describe and detail the use of micro-computed tomography for a reliable evaluation of the bulk-fill composite/tooth interface. Materials and Methods: Bulk-fill composite restorations in tooth cavities were scanned using micro-computed tomography to obtain qualitatively and quantitatively valuable information. Two-dimensional information was processed using specific algorithms, and ultimately a 3-dimensional (3D) specimen reconstruction was generated. The 3D rendering allowed the visualization of voids inside bulk-fill composite materials and provided quantitative measurements. The 3D analysis software VG Studio MAX was used to perform image analysis and assess gap formation within the tooth-restoration interface. In particular, to evaluate internal adaptation, the Defect Analysis addon module of VG Studio Max was used. Results: The data, obtained with the processing software, highlighted the presence and the shape of gaps in different colours, representing the volume of porosity within a chromatic scale in which each colour quantitatively represents a well-defined volume. Conclusion: Micro-computed tomography makes it possible to obtain several quantitative parameters, providing fundamental information on defect shape and complexity. However, this technique has the limit of not discriminating materials without radiopacity and with low or no filler content, such as dental adhesives, and hence, they are difficult to visualise through software reconstruction.

• Journal of Korea Trade
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• v.27 no.3
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• pp.87-102
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• 2023

Purpose - Although the importance and necessity of "sustainable supply chain management (SCM)" is emphasized, it is often not realized due to conflicting results, the long time required, and large-scale changes brought about by sustainability. This study used the innovation resistance model to confirm the influence of sustainable SCM innovation resistance factors and dynamic capabilities on adoption intentions. This approach made it possible to understand the factors that hinder adoption of sustainability practices and to identify the relationships among influencing factors. It should also help to establish effective policies or strategies. Design/methodology - Through a literature review, the characteristics of sustainable SCM were classified into relative advantage, compatibility, perceived risk, and complexity. The effects of these innovation characteristics on innovation resistance in sustainable SCM and the effects of innovation resistance on adoption intentions were confirmed. In addition, the effects of SCM capabilities on innovation resistance and adoption intentions were analyzed, and the mediating effect of innovation resistance was analyzed. Findings - Compatibility, perceived risk, and flexibility had significant effects on innovation resistance. In turn, innovation resistance had a significant effect on adoption intention, and flexibility had a significant effect on intention to adopt. A partial mediating effect of resistance to innovation was confirmed. Originality/value - Although many previous studies have acknowledged trade-offs with sustainability, most sustainable SCM studies dealt with the correlations among positive drivers of adoption, practices, and performance. This study confirmed the process of accepting sustainable SCM innovation in a single model and is expected to serve as a cornerstone for future sustainable SCM adoption studies. In addition, our findings should help establish effective policies or strategies to activate SSCM adoption by identifying the factors that hinder the adoption of sustainable SCM.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.301-310
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• 2017

Quantum-dot cellular automata(QCA) is an alternative technology for implementing various computation, high performance, and low power consumption digital circuits at nano scale. In this paper, we propose a new universal gate in QCA. By using the universal gate, we propose a novel XOR gate which is reduced time/hardware complexity. The universal gate can be used to construct all other basic logic gates. Meanwhile, the proposed universal gate is designed by basic cells and a rotated cell. The rotated cell of the proposed universal gate is located at the central of 3-input majority gate structure. In this paper, we propose an XOR gate using three universal gates, although more than five 3-input majority gates are used to design an XOR gate using the 3-input majority gate. The proposed XOR gate is superior to the conventional XOR gate in terms of the total area and the consumed clock because the number of gates are reduced.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.4
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• pp.329-340
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• 2010

Mechanical behavior of underground cavity construction such as tunnel is very difficult to estimate due to complexity and uncertainty of ground. Prediction of behavior according to excavation of tunnel mainly uses method utilized of model test or numerical analysis. But scale model test is difficult to reappear field condition, numerical analysis is also very hard to seek choice of suitable constituent model and input data. To solve this problem, this paper forecasted the deformation of tunnel that applied to information of crown settlement and convergence, RMR in weathered granite by using the regression analysis. The result of the analysis shows that the crown settlement according to excavation occurs approximately 70~80% of total displacements within about 20 days. As a result of the prediction of crown settlement and convergence, an exponential function becomes more accurate at measurements than an algebraic function. Also this paper got a correlation in comparison of RMR and displacements of 6 sections.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.285-294
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• 2023

As software grows continuously in scale and complexity, the role of software architecture has become increasingly important across various industries. Although software architects often rely on their experience and intuition when designing such architecture, there is a variety of methodologies being researched for architecture design. However, these methodologies do not address the specific effects of applying multiple architectural patterns to a system or the sequence in which they should be applied. In this study, we explain the variation in architectural design results depending on the order in which the same set of architectural patterns is applied to a single system. Based on this phenomenon, we identify requirements for applying architectural patterns and propose a method of classifying the patterns to be applied. We also propose a prioritization process for requirements to efficiently apply the classified patterns in a specific order. Finally, we show a case study that prioritizing requirements based on architectural pattern types is beneficial for efficient software architecture design in terms of quality attributes.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.