• Korean Journal of Optics and Photonics
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• v.33 no.1
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• pp.28-34
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• 2022

We study through computational simulation the focal performance of an infrared (IR) Fresnel lens, composed of a multilayer-graphene zone plate formed under a graphene electrode. Here the Fermi level E_F of the patterned multilayer graphene is adjusted by the overlying graphene electrode. The Fresnel lens effect, with respect to the reflectance contrast between the graphene electrode and the 8-layer graphene zone plate placed on a glass substrate, has been analyzed over a broad wavelength range from 4 to 30 ㎛. As the optimal wavelength of 8 ㎛ (considering the reflectance and the reflectance-contrast ratio) is incident upon the Fresnel lens with a focal length of 240 ㎛, the focal intensity is enhanced by a factor of 4.3 as the E_F of multilayer graphene increases from 0.4 eV to 1.6 eV, and is improved by a factor of 5.8 as the number of graphene layers increases from two to eight. As a result, an all-graphene-based IR Fresnel zone-plate lens, exhibiting multifocal function (240 ㎛ and 360 ㎛) according to the selected E_F, is proposed as an ultrathin lens platform.

• Journal of Life Science
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• v.32 no.8
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• pp.611-621
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• 2022

Dimethyl sulfoxide (DMSO) is commonly used as control or vehicle solvent in preclinical research of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) due to its ability to dissolve lipophilic compounds and cross the blood brain barrier. However, the biochemical effects of DMSO on the outcomes of preclinical research are often overlooked. In the present study, we investigated whether the long-term oral administration of 5% DMSO affects the neurological, functional, and histological disease phenotype of the copper/zinc superoxide dismutase glycine 93 to alanine mutation (SOD1-G93A) mouse model of amyotrophic lateral sclerosis. SOD1-G93A transgenic mice showed shortened survival time and reduced motor function. We found that administration with DMSO led to increased mean survival time, reduced neurological scores, and improved motor performance tested using the rotarod and grip strength tests. On the other hand, DMSO treatment did not attenuate motor neuron loss in the spinal cord and denervation of neuromuscular junctions in the skeletal muscle. These results suggest that DMSO administration could improve the quality of life of the SOD1-G93A mouse model of ALS without affecting motor neuron denervation. In conclusion, the use of DMSO as control or vehicle solvent in preclinical research may affect the behavioral outcomes in the SOD1-G93A mouse model. The effect of the vehicle should be thoroughly considered when interpreting therapeutic efficacy of candidate drugs in preclinical research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.36-42
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• 2023

The development of autonomous vehicles are currently being actively carried out by various companies and research institutes. Expectations for commercialization in daily life as well as specific industries are also rising. Simulators for autonomous vehicles are an essential element in algorithm development and execution considering stability and cost. In this need, various simulators and platforms for simulators are emerging, but research on simulations that reflect various meteorological environmental factors in the real world is still insufficient. This paper proposes a traffic simulation for autonomous vehicles that can consider the weather environment. The weather environment that can be set is largely classified into four categories, and an improved collision prevention algorithm to apply them is presented. Simulation development was conducted through Carla's Python API, a development tool for autonomous driving, and the performance results were compared with existing collision algorithms. Through this, we tried to propose improvements for the development of advanced self-driving vehicle simulations that can reflect various weather environmental factors in real life.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Membrane Journal
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• v.33 no.4
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• pp.181-190
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• 2023

Wastewater purification is one of the most important techniques for controlling environmental pollution and fulfilling the demand for freshwater supply. Various technologies, such as different types of distillations and reverse osmosis processes, need higher energy input. Capacitive deionization (CDI) is an alternative method in which power consumption is deficient and works on the supercapacitor principle. Research is going on to improve the electrode materials to improve the efficiency of the process. A reverse electrodialysis (RED) is the most commonly used desalination technology and osmotic power generator. Among many studies conducted to enhance the efficiency of RED, MXene, as an ion exchange membrane (IEM) and 2D nanofluidic channels in IEM, is rising as a promising way to improve the physical and electrochemical properties of RED. It is used alone and other polymeric materials are mixed with MXene to enhance the performance of the membrane further. The maximum desalination performances of MXene with preconditioning, Ti₃C₂T_x, Nafion, and hetero-structures were respectively measured, proving the potential of MXene for a promising material in the desalination industry. In terms of osmotic power generating via RED, adopting MXene as asymmetric nanofluidic ion channels in IEM significantly improved the maximum osmotic output power density, most of them surpassing the commercialization benchmark, 5 Wm^-2. By connecting the number of unit cells, the output voltage reaches the point where it can directly power the electronic devices without any intermediate aid. The studies around MXene have significantly increased in recent years, yet there is more to be revealed about the application of MXene in the membrane and osmotic power-generating industry. This review discusses the electrodialysis process based on MXene composite membrane.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• Restorative Dentistry and Endodontics
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• v.48 no.1
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• pp.3.1-3.14
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• 2023

Objectives: This study evaluated the effect of different nanoparticulated zinc oxide (nano-ZnO) and conventional-ZnO ratios on the physicochemical properties of calcium aluminate cement (CAC). Materials and Methods: The conventional-ZnO and nano-ZnO were added to the cement powder in the following proportions: G1 (20% conventional-ZnO), G2 (15% conventional-ZnO + 5% nano-ZnO), G3 (12% conventional-ZnO + 3% nano-ZnO) and G4 (10% conventional-ZnO + 5% nano-ZnO). The radiopacity (R_ad), setting time (S_et), dimensional change (D_c), solubility (S_ol), compressive strength (C_st), and pH were evaluated. The nano-ZnO and CAC containing conventional-ZnO were also assessed using scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. Radiopacity data were analyzed by the 1-way analysis of variance (ANOVA) and Bonferroni tests (p < 0.05). The data of the other properties were analyzed by the ANOVA, Tukey, and Fisher tests (p < 0.05). Results: The nano-ZnO and CAC containing conventional-ZnO powders presented particles with few impurities and nanometric and micrometric sizes, respectively. G1 had the highest Rad mean value (p < 0.05). When compared to G1, groups containing nano-ZnO had a significant reduction in the S_et (p < 0.05) and lower values of D_c at 24 hours (p < 0.05). The C_st was higher for G4, with a significant difference for the other groups (p < 0.05). The S_ol did not present significant differences among groups (p > 0.05). Conclusions: The addition of nano-ZnO to CAC improved its dimensional change, setting time, and compressive strength, which may be promising for the clinical performance of this cement.

• Applied Chemistry for Engineering
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• v.34 no.4
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• pp.357-364
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• 2023

Selective catalytic reduction (SCR) is the most effective method for reducing nitrogen oxide emissions, but the operating temperature range of V₂O₅-WO₃/TiO₂ catalysts is narrow (300~400℃). In this study, a new catalyst with an operating temperature range of 200~450℃ was developed. The catalyst poison, ammonium bisulfate, generated during the SCR process can be removed by heating above 350℃. To increase the number of active sites and promote the dispersion of active materials, titanium isopropoxide (TTIP) treatment was performed on the TiO₂ support with various TTIP/TiO₂ mass ratios. Among them, the 5 wt% TTIP loaded catalyst showed improved performance due to higher thermal stability caused by high W dispersion and the formation of V⁵⁺. In addition, the 5 wt% TTIP-loaded catalyst prepared by a one-step co-precipitation method showed greater V-OH and W-OH dispersion and enhanced interactions in contrast to conventional methods, resulting in higher catalytic activity at lower temperatures. This review article aims to provide an accessible explanation for researchers investigating how to improve the surface properties of TiO2 catalysts using TTIP.