• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• The Journal of Advanced Prosthodontics
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• v.15 no.3
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• pp.155-170
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• 2023

PURPOSE. This study aims to clinically compare the fitness and trueness of zirconia crowns fabricated by different combinations of open CAD-CAM systems. MATERIALS AND METHODS. Total of 40 patients were enrolled in this study, and 9 different zirconia crowns were prepared per patient. Each crown was made through the cross-application of 3 different design software (EZIS VR, 3Shape Dental System, Exocad) with 3 different processing devices (Aegis HM, Trione Z, Motion 2). The marginal gap, absolute marginal discrepancy, internal gap(axial, line angle, occlusal) by a silicone replica technique were measured to compare the fit of the crown. The scanned inner and outer surfaces of the crowns were compared to CAD data using 3D metrology software to evaluate trueness. RESULTS. There were significant differences in the marginal gap, absolute marginal discrepancy, axial and line angle internal gap among the groups (P < .05) in the comparison of fit. There was no statistically significant difference among the groups in terms of occlusal internal gap. The trueness ranged from 36.19 to 43.78 ㎛ but there was no statistically significant difference within the groups (P > .05). CONCLUSION. All 9 groups showed clinically acceptable level of marginal gaps ranging from 74.26 to 112.20 ㎛ in terms of fit comparison. In the comparison of trueness, no significant difference within each group was spotted. Within the limitation of this study, open CAD-CAM systems used in this study can be assembled properly to fabricate zirconia crown.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• Applied Chemistry for Engineering
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• v.35 no.1
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• pp.54-60
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• 2024

The present study was aimed to evaluate the removal of the trace pollutants (heavy metals and microplastics) in the sewage treatment plant by using the jellyfish Extract at Immunity reaction (JEI) of Aurelia coerulea. The experiment was conducted on two different scales: the lab scale using a Jar-tester and the Pilot system scale equipped with two newly developed devices in the laboratory, the active tube connection mixed system and the concentration integrated separation device. Compared to anionic polymers currently used in the field, JEI showed similar or higher efficiency to remove the trace pollutants. When JEI was added to the effluent through the Pilot system, the combination of JEI and the trace pollutants was maximized through two mixing processes, and as a result, the removal rate of the trace pollutants was greatly improved. Based on these results, we propose the present technology as an alternative to removing trace pollutants that can reduce ecosystem risk and minimize the generation of inorganic waste, away from the existing method.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.4
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• pp.427-432
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• 2024

The polymer crystallization process, promoting the formation of ferroelectric β-phase, is essential for developing polyvinylidene fluoride (PVDF)-based high-performance piezoelectric energy harvesters. However, traditional high-temperature annealing is unsuitable for the manufacture of flexible piezoelectric devices due to the thermal damage to plastic components that occurs during the long processing times. In this study, we investigated the feasibility of introducing a flash lamp annealing that can rapidly induce the β-phase in the PVDF layer while avoiding device damage through selective heating. The flash light-irradiated PVDF films achieved a maximum β-phase content of 76.52% under an applied voltage of 300 V and an on-time of 1.5 ms, a higher fraction than that obtained through thermal annealing. The PVDF-based piezoelectric energy harvester with the optimized irradiation condition generates a stable output voltage of 0.23 V and a current of 102 nA under repeated bendings. These results demonstrate that flash lamp annealing can be an effective process for realizing the mass production of PVDF-based flexible electronics.

• Korean Journal of Psychosomatic Medicine
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• v.25 no.2
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• pp.210-217
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• 2017

Objectives : Although the Attention Network Test(ANT) has been widely used to assess selective attention including alerting, orienting, and conflict processing, data on its test-retest reliability are lacking for clinical population. The objective of the current study was to investigate test-retest reliability of the ANT in healthy controls and patients with schizophrenia. Methods : Fourteen patients with schizophrenia and 23 healthy controls participated in the study. They are tested with ANT twice with 1 week interval. Test-retest reliability was analyzed with Pearson and Intra-class correlations. Results : Patients with schizophrenia showed high test-retest correlations for mean reaction time, orienting effect, and conflict effect. Also, they showed moderate to high test-retest correlations for mean accuracy and moderate test-retest correlations for alerting effect and conflict error rate. On the other hand, healthy controls revealed high test-retest correlations for mean reaction time and moderate to high test-retest correlations for conflict error rate. In addition, they revealed moderate test-retest correlations for alert effect, orienting effect, and conflict effect. Conclusions : The mean reaction time, alerting effect, orienting effect, conflict effect, and conflict error rate of ANT showed acceptable test-retest reliabilities in healthy controls as well as patient with schizophrenia. Therefore, the analyses of these reliable measures of ANT are recommended for case-control studies in patients with schizophrenia.

• Korean Journal of Food Science and Technology
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• v.30 no.5
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• pp.1128-1133
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• 1998

Extrusion cooking treatment was compared with autoclaving/cooling treatment for formation of enzyme resistant starch of high amylose corn starch (HACS). Effects of barrel temperature $(100^{\circ}C,\;120^{\circ}C,\;140^{\circ}C)$ and feed moisture content (25%, 35%, 45%) on extrusion processing in a co-rotating twin-screw extruder under fixed screw speed (100 rpm) were investigated by measuring enzyme resistant starch (RS) yield. RS yield were estimated by in-vitro pancreatin digestion method and enzymatic-gravimetric method using termamyl. Barrel temperature and yield of RS were negatively correlated and feed moisture content and yield of RS was positively correlated as determined by in-vitro pancreatin method. The highest yield (38.4%) of RS was obtained from HACS extrudate processed at the barrel temperature of $100^{\circ}C$ and the feed moisture content of 45%, while the yield of RS by 5 times of autoclaving/cooling was 25%. The yield of RS by in vitro pancreatin digestion method was 20.7% with high amylose corn starch and 8.2% with ordinary corn starch (CS), respectively, under the same extrusion condition (barrel temperature $120^{\circ}C$, feed moisture content 35%). At the same condition, the yields of RS by enzyme-gravimetric method were 14.6% with HACS and 6.8% with CS, respectively. The yield of RS increased during the storage at $4^{\circ}C$ for 4 weeks and the highest yield (60%) was obtained by the storage of HACS extrudates extruded at $100^{\circ}C$ and 45% feed moisture content.

• Economic and Environmental Geology
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• v.50 no.1
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• pp.1-14
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• 2017

The analytical conditions including surface state, moisture effect, and device condition were investigated for applying Short Wave Infrared(SWIR) spectroscopy to the field survey. Among the three surface state of samples (exposed surface, cutting face and powder), both spectra from the exposed surface and cutting face are almost identical whereas spectral variation was detected in powder sample. Over 24-hours-dryring of the wet sample at room temperature, the samples show a similar spectrum with that of dry condition. The result suggests that outcrop samples mighty be dried for 24 ~ 48 hours depending on the wetness of outcrop. The bright minerals could produce stable spectra with 10 times measurements as default value of the device under SWIR spectroscopy but the dark minerals would require about 10 seconds, which corresponds to 100 times measurements to get the reliable spectra. The position and shape 2,160 ~ 2,330 nm and/or other spectral features of hydrothermal alteration minerals by SWIR spectroscopy could be used for a classification of hydrothermal alteration zone in the field. Absorption peaks in 2,160 ~ 2180 nm are useful for identifying (advanced) argillic zone by spectral characteristics of kaoline, dickite, pyrophyllite, and alunite. Absorption peaks in 2,180 ~ 2,230 nm are able to define muscovite, sericite, and smectite, which are key alteration minerals in phyllic zone. Absorption peaks in 2,230 ~ 2,270 nm can be used to recognize prophylitic zone where chlorite and epidote occur. Absorption peaks of other principle minerals such as talc, serpentine, amphibole, and carbonate group are mainly detected within the wave length of 2,270 ~ 2,330 nm. This result indicates that the spectra of these minerals need to be carefully interpreted.