• Journal of Technologic Dentistry
• /
• v.46 no.2
• /
• pp.36-41
• /
• 2024

Purpose: This study aimed to generate virtual mandibular left first molar teeth using deep convolutional generative adversarial networks (DCGANs) and analyze their matching accuracy with actual tooth morphology to propose a new paradigm for using medical data. Methods: Occlusal surface images of the mandibular left first molar scanned using a dental model scanner were analyzed using DCGANs. Overall, 100 training sets comprising 50 original and 50 background-removed images were created, thus generating 1,000 virtual teeth. These virtual teeth were classified based on the number of cusps and occlusal surface ratio, and subsequently, were analyzed for consistency by expert dental technicians over three rounds of examination. Statistical analysis was conducted using IBM SPSS Statistics ver. 23.0 (IBM), including intraclass correlation coefficient for intrarater reliability, one-way ANOVA, and Tukey's post-hoc analysis. Results: Virtual mandibular left first molars exhibited high consistency in the occlusal surface ratio but varied in other criteria. Moreover, consistency was the highest in the occlusal buccal lingual criteria at 91.9%, whereas discrepancies were observed most in the occusal buccal cusp criteria at 85.5%. Significant differences were observed among all groups (p<0.05). Conclusion: Based on the classification of the virtually generated left mandibular first molar according to several criteria, DCGANs can generate virtual data highly similar to real data. Thus, subsequent research in the dental field, including the development of improved neural network structures, is necessary.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.8 no.6
• /
• pp.73-79
• /
• 2008

Economic growth, industrialization and urbanization have made society more vulnerable than ever to seismic hazard in Korea. Although Korea has not experienced severe damage due to earthquakes during the last few decades, there is little doubt of the potential for large earthquakes in Korea as documented in the historical literature. As we see no immediate promise of short-term earthquake prediction with current science and technology, earthquake early warning systems attract more and more attention as a practical measure to mitigate damage from earthquakes. Earthquake early warning systems provide a few seconds to tens of seconds of warning time before the onset of strong ground shaking. To achieve rapid earthquake location, we propose to take full advantage of information from existing seismic networks; by using P wave arrival times at two nearest stations from the earthquake hypocenter and also information that P waves have not yet arrived at other stations. Ten earthquakes in the Korean peninsula and its vicinity are selected for the feasibility study. We observed that location results are not reliable when earthquakes occur outside of the seismic network. Earthquakes inside the seismic network, however, can be located very rapidly for the purpose of earthquake early warning. Seoul metropolitan area may secure $10{\sim}50$ seconds of warning time before any strong shaking starts for certain events. Carefully orchestrated actions during the given warning time should be able to reduce hazard and mitigate damages due to potentially disastrous earthquakes.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.39-54
• /
• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.44 no.8
• /
• pp.52-59
• /
• 2007

In this paper, we analyzed and measured implant isolation characteristics for a 1.25 Gbps monolithic integrated hi-directional (M-BiDi) optoelectronic system-on-a-chip, which is a key component to constitute gigabit passive optical networks (PONs) for a fiber-to-the-home (FTTH). Also, we derived an equivalent circuit of the implant structure under various DC bias conditions. The 1.25 Gbps M-BiDi transmit-receive SoC consists of a laser diode with a monitor photodiode as a transmitter and a digital photodiode as a digital data receiver on the same InP wafer According to IEEE 802.3ah and ITU-T G.983.3 standards, a receiver sensitivity of the digital receiver has to satisfy under -24 dBm @ BER=10-12. Therefore, the electrical crosstalk levels have to maintain less than -86 dB from DC to 3 GHz. From analysed and measured results of the implant structure, the M-BiDi SoC with the implant area of 20 mm width and more than 200 mm distance between the laser diode and monitor photodiode, and between the monitor photodiode and digital photodiode, satisfies the electrical crosstalk level. These implant characteristics can be used for the design and fabrication of an optoelectronic SoC design, and expended to a mixed-mode SoC field.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.1
• /
• pp.76-83
• /
• 2011

The IEEE WAVE-based communication systems do not provide handover services since most of the application layer messages of a small amount containing text data that are related to safe driving. Multimedia data service such as web pages and CCTV video clips, however, require a seamless handover for continuation of a session via multiple RSUs. In this paper, we propose a new proactive handover protocol based on IEEE WAVE. According to the proposed handover protocol, the OBU notifies the old RSU of its departure from the coverage such that the old RSU forwards to the new RSU the data heading towards the OBU to be cached for the further delivery upon its entry into the new RSU's coverage. The simulation results are presented which shows the performance of the proposed protocol in terms of throughput, delivery ratio and handover delay.

• The Journal of Information Technology
• /
• v.6 no.1
• /
• pp.1-10
• /
• 2003

In this paper, a public key exponential encryption algorithm for data security of computer network is proposed. This is based on the security to a difficulty of polynomial factorization. For the proposed public key exponential encryption, the public key generation algorithm selects two polynomials f(x,y,z) and g(x,y,z). The enciphering first selects plaintext polynomial W(x,y,z) and multiplies the public key polynomials, then the ciphertext is computed. In the proposed exponential encryption system of public key polynomial, an encryption is built by exponential encryption multiplied thrice by the optional integer number and again plus two public polynomials f(x,y,z) and g(x,y,z). This is an encryption system to enforce the security of encryption with help of prime factor added on RSA public key. The propriety of the proposed public key exponential cryptosystem algorithm is verified with the computer simulation.

• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.22 no.6
• /
• pp.527-535
• /
• 2019

Purpose: There is increasing prevalence of psychiatric disorders among inflammatory bowel Disease (IBD) population. Further, presence of psychiatric disorders has been shown as an independent predictor of quality of life among patients with IBD. We intended to explore the prevalence of various psychiatric disorders among pediatric and young adult population with IBD as a population-based analysis. Methods: We did a retrospective case control analysis using a deidentified cloud-based database including health care data across 26 health care networks comprising of more than 360 hospitals across USA. Data collected across different hospitals were classified and stored according to Systematized Nomenclature of Medicine-Clinical Terms. We preidentified 10 psychiatric disorders and the queried the database for the presence of at least one of the ten psychiatric disorders among IBD patients between 5 and 24 years of age and compared with controls. Results: Total of 11,316,450 patients in the age group between 5 and 24 years and the number of patients with a diagnosis of IBD, Crohn's disease or ulcerative colitis were 58,020. The prevalence of psychiatric disorders was 21.6% among IBD mainly comprising of depression and anxiety disorder. Multiple logistic regression analysis showed, IBD is 5 times more likely associated with psychiatric disorders than controls, p<0.001). We showed a steady increasing trend in the incidence of psychiatric disorders among IBD patients (2% in 2006 to 15% in 2017). Conclusion: Largest population-based analysis demonstrated an increased prevalence of psychiatric disorders among IBD patients. Our study emphasizes the need for psychological and mental health services to be incorporated as a part of the routine IBD clinic.

• Fisheries and Aquatic Sciences
• /
• v.26 no.6
• /
• pp.406-422
• /
• 2023

Sarcopenia is an age-related, progressive skeletal muscle disorder involving the loss of muscle mass and strength. Previous studies have shown that γ-aminobutyric acid (GABA) from fermented oysters aids in regulatory T cells (Tregs) cell expansion and function by enhancing autophagy, and concomitantly mediate muscle regeneration by modulating muscle inflammation and satellite cell function. The fermentation process of oysters not only increases the GABA content but also enhances the content of branched amino acids and free amino acids that aid the level of protein absorption and muscle strength, mass, and repair. In this study, the effect of GABA-enriched fermented sarco oyster extract (FSO) on reduced muscle mass and functions via Treg modulation and enhanced autophagy in aged mice was investigated. Results showed that FSO enhanced the expression of autophagy markers (autophagy-related gene 5 [ATG5] and GABA receptor-associated protein [GABARAP]), forkhead box protein 3 (FoxP3) expression, and levels of anti-inflammatory cytokines (interleukin [IL]-10 and transforming growth factor [TGF]-β) secreted by Tregs while reducing pro-inflammatory cytokine levels (IL-17A and interferon [IFN]-γ). Furthermore, FSO increased the expression of IL-33 and its receptor IL-1 receptor-like 1 (ST2); well-known signaling pathways that increase amphiregulin (Areg) secretion and expression of myogenesis markers (myogenic factor 5, myoblast determination protein 1, and myogenin). Muscle mass and function were also enhanced via FSO. Overall, the current study suggests that FSO increased autophagy, which enhanced Treg accumulation and function, decreased muscle inflammation, and increased satellite cell function for muscle regeneration and therefore could decrease the loss of muscle mass and function with aging.

• Smart Structures and Systems
• /
• v.32 no.3
• /
• pp.135-151
• /
• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Asian-Australasian Journal of Animal Sciences
• /
• v.31 no.1
• /
• pp.138-148
• /
• 2018

Objective: Fusarium mycotoxins deoxynivalenol (DON) and zearalenone (ZEN), common contaminants in the feed of farm animals, cause immune function impairment and organ inflammation. Consequently, the main objective of this study was to elucidate DON and ZEN effects on the mRNA expression of pro-inflammatory cytokines and other immune related genes in the kidneys of piglets. Methods: Fifteen 6-week-old piglets were randomly assigned to three dietary treatments for 4 weeks: control diet, and diets contaminated with either 8 mg DON/kg feed or 0.8 mg ZEN/kg feed. Kidney samples were collected after treatment, and RNA-seq was used to investigate the effects on immune-related genes and gene networks. Results: A total of 186 differentially expressed genes (DEGs) were screened (120 upregulated and 66 downregulated). Gene ontology analysis revealed that the immune response, and cellular and metabolic processes were significantly controlled by these DEGs. The inflammatory stimulation might be an effect of the following enriched Kyoto encyclopedia of genes and genomes pathway analysis found related to immune and disease responses: cytokine-cytokine receptor interaction, chemokine signaling pathway, toll-like receptor signaling pathway, systemic lupus erythematosus (SLE), tuberculosis, Epstein-Barr virus infection, and chemical carcinogenesis. The effects of DON and ZEN on genome-wide expression were assessed, and it was found that the DEGs associated with inflammatory cytokines (interleukin 10 receptor, beta, chemokine [C-X-C motif] ligand 9, CXCL10, chemokine [C-C motif] ligand 4), proliferation (insulin like growth factor binding protein 4, IgG heavy chain, receptor-type tyrosine-protein phosphatase C, cytochrome P450 1A1, ATP-binding cassette sub-family 8), and other immune response networks (lysozyme, complement component 4 binding protein alpha, oligoadenylate synthetase 2, signaling lymphocytic activation molecule-9, ${\alpha}$-aminoadipic semialdehyde dehydrogenase, Ig lambda chain c region, pyruvate dehydrogenase kinase, isozyme 4, carboxylesterase 1), were suppressed by DON and ZEN. Conclusion: In summary, our results indicate that high concentrations of DON and ZEN suppress the inflammatory response in kidneys, leading to potential effects on immune homeostasis.