• 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 Advanced Technology Convergence
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• v.2 no.3
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• pp.17-24
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• 2023

I am designing a research paper with the aim of studying hybrid vehicles. Hybrid vehicles, as the next-generation automobiles, feature a combination of internal combustion engines and battery engines, resulting in a revolutionary reduction in fuel consumption and harmful gas emissions compared to conventional vehicles. The electric motor in hybrid cars derives power from a high-voltage battery installed within the vehicle, which is recharged during vehicle motion. In contrast to traditional cars, which often experience energy losses due to idling caused by traffic congestion, hybrid systems optimize efficiency by skillfully managing the interplay between the internal combustion engine and the electric motor. This approach effectively addresses the inherent drawbacks of gasoline or diesel engines.Hybrid cars offer an array of benefits, including improved fuel efficiency, environmental friendliness, cost-effectiveness, and reduced noise emission. Consequently, they are progressively becoming a favored alternative among a growing number of individuals. This research endeavor has the potential to contribute towards curbing environmental pollution and dedicating efforts to future automotive research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.621-631
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• 2023

Adversarial training improves the robustness of deep neural networks for adversarial examples. However, the previous adversarial training method focuses only on the adversarial loss function, ignoring that even a small perturbation of the input layer causes a significant change in the hidden layer features. Consequently, the accuracy of a defended model is reduced for various untrained situations such as clean samples or other attack techniques. Therefore, an architectural perspective is necessary to improve feature representation power to solve this problem. In this paper, we apply an attention module that generates an attention map of an input image to a general model and performs PGD adversarial training upon the augmented model. In our experiments on the CIFAR-10 dataset, the attention augmented model showed higher accuracy than the general model regardless of the network structure. In particular, the robust accuracy of our approach was consistently higher for various attacks such as PGD, FGSM, and BIM and more powerful adversaries. By visualizing the attention map, we further confirmed that the attention module extracts features of the correct class even for adversarial examples.

• Fashion & Textile Research Journal
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• v.25 no.1
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• pp.104-112
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• 2023

This study attempted to investigate the characteristics of beauty influencers' social media contents and examine their influence on color cosmetics purchase intention. For this, female millennials who have shown an interest or subscribed beauty contents on social media platforms as followers were selected by convenience sampling. In terms of a research method, a self-administered questionnaire was performed from September 19 to 30, 2022. Among a total of 220 questionnaires distributed, 200 copies excluding poorly answered ones were used for final analysis. The collected data were analyzed by frequency analysis, descriptive statistics, factor analysis, reliability analysis, correlation analysis and multiple regression analysis, using SPSS 24.0, and the results found the followings: First, concerning characteristics of beauty influencers' beauty contents, five factors were derived: reliability, professionalism, social attractiveness, attractive appearance, sympathy In purchase intention, on the contrary, two factors were obtained: base makeup, point makeup. Second, regarding the effects of characteristics of beauty contents on color cosmetics purchase intention, 'professionalism (β = -.170 p = .015)' and 'physical attractiveness (β = -.148, p = .037)' revealed a negative influence with statistical significance. Through the result, by demonstrating the effect on the intention to purchase color cosmetics based on the beauty contents feature of the beauty influencer, it is considered that the purchasing power of the color cosmetics industry will continue to increase and help to suggest more effective color cosmetics promotion ways and indicators which companies can utilize.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of English Language & Literature
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• v.58 no.6
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• pp.1143-1165
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• 2012

Tinted with ontological concern, Ted Hughes passes through an existential climate, eventually confirms death( or nothingness) as the new foundation of his poetry, and explores the various paradoxical effects of nothingness. Nihilism, fraught with rather negative and traumatic themes such as death, melancholy, and despair can, however, generate being (even in multiple modes), animalistic vitality, and insubstantial specters. Among these new functions of nothingness animality and spectrality are the most notable in Hughes's poetry. A considerable number of animals and bioorganisms that Hughes introduces exhibit the enormous energy derived from the dignity of death, from subversive challenges against the established hierarchy, and from new and dynamic multifaceted sources of nothingness. In other words, Hughes's animals, yield surplus power beyond themselves, as if they are demi-gods; in short, they feature the sublime as unidentified terrifying effects of nothingness. In a sense, animality means allowing some level of violence without legal sanction. Hughes inaugurates this kind of all bigotry-eradicating violence and attempts to subvert higher beings such as humans and gods, and existing doctrines: thrushes rise up against the animal and human worlds; a rush of ghostly crabs at night press through the human world. Hughes also resists the highest being, God, employing the technique of rewriting God's theology. Dirty, anomalous crows attack, subvert, and dismember the delicate, indurate, and thorough system of logos. Hughes, of course, does not place the animals merely in lofty regard, aware of the ulterior deprivation of the sublime animality, the trace of existential negativity. Thus, a seemingly omnipotent crow can become a mere beggar guzzling ice cream from the garbage bin on the beach. In addition, the violent and dignified aspects of nothingness can be transformed to reveal the thin and trivial traits as unreliable specters. Dark, heavy, and terrible nullity lessens its own volume and mass, and exposes the airy waves of shadows or specters. However, owing to nullity's untraceable track, the scarcity and unfamiliarity of the phantoms inversely display their foreign gigantic effects such as fantasy and violence.

• Transactions on Semiconductor Engineering
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• v.2 no.1
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• pp.17-20
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• 2024

This paper presents a novel lightweight object detection model tailored for low-powered edge devices, addressing the limitations of traditional resource-intensive computer vision models. Our proposed detector, inspired by the Single Shot Detector (SSD), employs a compact yet robust network design. Crucially, it integrates an 'enhancer block' that significantly boosts its efficiency in detecting smaller objects. The model comprises two primary components: the Light_Block for efficient feature extraction using Depth-wise and Pointwise Convolution layers, and the Enhancer_Block for enhanced detection of tiny objects. Trained from scratch on the Udacity Annotated Dataset with image dimensions of 300x480, our model eschews the need for pre-trained classification weights. Weighing only 5.5MB with approximately 0.43M parameters, our detector achieved a mean average precision (mAP) of 27.7% and processed at 140 FPS, outperforming conventional models in both precision and efficiency. This research underscores the potential of lightweight designs in advancing object detection for edge devices without compromising accuracy.

• Computers and Concrete
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• v.33 no.1
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• pp.27-39
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• 2024

In this study, the authors investigate the free vibration behavior of three-phases functionally graded sandwich plates using a novel nth-order shear deformation theory. These plates are composed of a homogeneous core and two face-sheet layers made of different functionally graded materials. This is the novel type of the sandwich structures that can be applied in many fields of mechanical engineering and industrial. The proposed theory only requires four unknown displacement functions, and the transverse displacement does not need to be separated into bending and shear parts, simplifying the theory. One noteworthy feature of the proposed theory is its ability to capture the parabolic distribution of transverse shear strains and stresses throughout the plate's thickness while ensuring zero values on the two free surfaces. By eliminating the need for shear correction factors, the theory further enhances computational efficiency. Equations of motion are established using Hamilton's principle and solved via Navier's solution. The accuracy and efficiency of the proposed theory are verified by comparing results with available solutions. The authors then use the proposed theory to investigate the free vibration characteristics of three-phases functionally graded sandwich plates, considering the effects of parameters such as aspect ratio, side-to-thickness ratio, skin-core-skin thicknesses, and power-law indexes. Through careful analysis of the free vibration behavior of three-phases functionally graded sandwich plates, the work highlighted the significant roles played by individual material ingredients in influencing their frequencies.

• Current Optics and Photonics
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• v.8 no.4
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• pp.406-415
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• 2024

The major error factors that degrade the efficiency of coherent beam combining (CBC) are numerically studied in a comprehensive manner, paying particular attention to phase, tip-tilt, polarization angle, and beam quality. The power in the bucket (PIB), normalized to the zero-error PIB, is used as a figure of merit to quantify the effect of each error factor. To maintain a normalized PIB greater than or equal to 95% in a 3-channel CBC configuration, the errors in phase, tip-tilt, and polarization angle should be less than 1.06 radians, 1.25 ㎛, and 1.06 radians respectively, when each of the three parameters is calculated independently with the other two set to zero. In a worst-case scenario of the composite errors within the parameter range for the independent-95%-normalized-PIB condition, the aggregate effect would reduce the normalized PIB to 83.8%. It is noteworthy that the PIB performances of a CBC system, depending on phase and polarization-angle errors, share the same characteristic feature. A statistical approach for each error factor is also introduced, to assess a CBC system with an extended number of channels. The impact of the laser's beam-quality factor M² on the combining efficiency is also analyzed, based on a super-Gaussian beam. When M² increases from 1 to 1.3, the normalized PIB is reduced by 2.6%, 11.8%, 12.8%, and 13.2% for a single-channel configuration and 3-, 7-, and 19-channel CBC configurations respectively. This comprehensive numerical study is expected to pave the way for advances in the evaluation and design of multichannel CBC systems and other related applications.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.4
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• pp.1-14
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• 2024

Otters, designated as Class I endangered wildlife due to population declines resulting from urban development and stream burial, have seen increased appearances in freshwater environments since the nationwide ban on stream filling in 2020 and the implementation of urban stream restoration projects. There is a pressing need for scientific and strategic conservation measures for otters, an umbrella and vulnerable species in aquatic ecosystems. Therefore, this study predicts potential otter habitats using the species distribution model MaxEnt, focusing on Hwangguji Stream in Suwon, and proposes conservation strategies. Otter signs were surveyed over three years from 2019 to 2021 with citizen scientists, serving as presence data for the model. The model's outcomes were enhanced by analyzing 'river nature map' as a boundary. MaxEnt compared the performance of 60 combinations of feature classes and regularization multipliers to prevent model complexity and overfitting. Additionally, unmanned sensor cameras observed otter density for model validation, confirming correlations with the species distribution model results. The 'LQ-5.0' parameter combination showed the highest explanatory power with an AUC of 0.853. The model indicated that the 'adjacent land use' variable accounted for 31.5% of the explanation, with a preference for areas around cultivated lands. Otters were found to prefer shelter rates of 10-30% in riparian forests within 2 km of bridges. Higher otter densities observed by unmanned sensors correlated with increasing model values. Based on these results, the study suggests three conservation strategies: establishing stable buffer zones to enhance ecological connectivity, improving water quality against non-point source pollution, and raising public awareness. The study provides a scientific basis for potential otter habitat management, effective conservation through governance linking local governments, sustainable biodiversity goals, and civil organizations.