• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.125-136
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• 2008

Statement of problem: Since the concept of osseointegration in dental implants was introduced by $Br{{\aa}}nemark$ et al, high long-term success rates have been achieved. Though the use of dental implants have increased dramatically, there are few studies on domestic implants with clinical and objective long-term data. Purpose: The aim of this retrospective study was to provide long-term data on the $Neoplan^{(R)}$ implant, which features a sandblasted and acid-etched surface and external connection. Material and methods: 96 $Neoplan^{(R)}$ implants placed in 25 patients in Yonsei University Hospital were examined to determine the effect of the factors on marginal bone loss, through clinical and radiographic results during 18 to 57 month period. Results: 1. Out of a total of 96 implants placed in 25 patients, two fixtures were lost, resulting in 97.9% of cumulative survival rate. 2. Throughout the study period, the survival rates were 96.8% in the maxilla and 98.5% in the mandible. The survival rates were 97.6% in the posterior regions and 100% in the anterior regions. 3. The mean bone loss for the first year after prosthesis placement and the mean annual bone loss after the first year for men were significantly higher than that of women (P<0.05). 4. The group of partial edentulism with no posterior teeth distal to the implant prosthesis showed significantly more bone loss compared to the group of partial edentulism with presence of posterior teeth distal to the implant prosthesis in terms of mean bone loss for the first year and after the first year (P<0.05). 5. The mean annual bone loss after the first year was more pronounced in posterior regions compared to anterior regions (P<0.05). 6. No significant difference in marginal bone loss was found in the following factors: jaws, type of prostheses, type of opposing dentition, and submerged /non-submerged implants (P<0.05). Conclusion: On the basis of these results, the factors influencing marginal bone loss were gender, type of edentulism, and location in the arch, while the factors such as arch, type of prostheses, type of opposing dentition, submerged / non- submerged implants had no significant effect on bone loss. In the present study, the cumulative survival rate of the $Neoplan^{(R)}$ implant with a sandblasted and acid-etched surface was 97.9% up to a maximum 57-month period. Further long-term investigations for this type of implant system and evaluation of other various domestic implant systems are needed in future studies.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.647-656
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• 2007

In order to investigate the effects of supplemental ionic surfactants in in vitro ruminal fermentation, N-Lauroylsarcosine sodium salt(N-LSS) and sodium dodecyl sulfate(SDS) for negative(－) ionic surfactant, and hexadecylpyridinium chloride monohydrate(HPCM) and hexadecyltrimethyl ammonium bromide(HTAB) for positive (+) ionic surfactant were supplemented by 0.05% and 0.1% into the Dehority’s artificial medium containing rice straw(1mm) as a substrate. In vitro DM digestibility, the growth of rumen mixed microbes, pH, cumulative gas production and SEM(Scanning Electron Microscopy) observation of microbial attachment on rice straw particle were investigated through the experiment composing 9 treatments (two supplemental levels of two positive ionic(+) surfactant, two supplemental levels of two negative(－) ionic surfactant) including the control. The sample collection was at 6, 12, 24, 48 and 72 h post fermentation with 3 replications per treatments. DM digestibility in treatments supplemented (+) or (－) surfactants almost stopped afterward 12 h fermentation, in vitro DM digestibility at 72 h post fermentation in the ionic surfactants was at half level of that of the control(P<0.05). Accumulative gas production in in vitro was less(P<0.05) with addition of ionic surfactants compared to the control. The amount of rumen mixed microbes recovered from in vitro incubation fluid pleateaued at 12 h post fermentation for the positive (+) ionic surfactants, but steadily increased as fermentation time elapsed for the control. Rumen microbial growth rate was significantly(P<0.05) low in the negative(－) ionic surfactant compared to the control. pH of the incubation fluid was ranged from 6.02 to 7.20, and was the highest in the negative(－) ionic surfactants, and was the lowest in the control(P<0.05). In SEM observation, rumen microbial population attached on rice straw particle was less with addition of ionic surfactants than the control. In conclusion we could not found any positive effects of negative- and positive- charged surfactants on rumunal fermentation characteristics and rumen microbial growth rates.

• The Journal of Korean Academy of Prosthodontics
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• v.50 no.1
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• pp.61-66
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• 2012

Purpose: This study was to compare the cumulative survival rate (CSR) of Br${\aa}$nemark machined surface implants and TiUnite$^{TM}$ imlants and to analyze association between risk factors and the CSR of the implants. Materials and methods: A retrospective study design was used to collect long-term follow-up clinical data from dental records of 156 patients treated with 541 Br${\aa}$nemark machined and TiUnite$^{TM}$ implants at Korea University Guro hospital in South Korea from 1993 through 2008. Machined implant and TiUnite$^{TM}$ implant were compared by CSR. Exposure variables such as gender, systemic disease, location, implant length, diameter, prosthesis type, opposing occlusion type, date of implant placement, type of edentulous space, abutment type, existence of splinting with natural teeth, and existence of cantilever were collected. Life table analysis was undertaken to examine the CSR. Cox regression method was conducted to assess the association between potential risk factors and overall CSR (${\alpha}$=.05). Results: Patient ages ranged from 16 to 75 years old (mean age, 51 years old). Implants were more frequently placed in men than women (94 men versus 63 women). Since 1993, 264 Br${\aa}$nemark machined implants were inserted in 79 patients and since 2001, 277 TiUnite$^{TM}$ implants were inserted in 77 patients. A total survival rate of 86.07% was observed in Br${\aa}$nemark and Nobel Biocare TiUnite$^{TM}$ during 15 years. A survival rate of machined implant during 15 years was 82.89% and that of TiUnite$^{TM}$ implant during 5 years was 98.74%. The implant CSR revealed lower rates association with several risk factors such as, systemic disease, other accompanied surgery, implant location, and Kennedy classification. Conclusion: Clinical performance of Br${\aa}$nemark machined and TiUnite$^{TM}$ implant demonstrated a high level of predictability. In this study, TiUnite$^{TM}$ implant was more successful than machined implant. The implant CSR was associated with several risk factors.

• Radiation Oncology Journal
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• v.20 no.2
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• pp.123-129
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• 2002

Purpose : The purpose of this study 띤as to determine the potential role of three-dimensional conformal radiotherapy (3D-CRT) in the treatment of primary unresectable hepatocellular carcinoma. The preliminary results on the efficacy and the toxicity of 3D-CRT are reported. Materials and Methods : Seventeen patients were enrolled in this study, which was conducted prospectively from January 1995 to June 1997. The exclusion criteria included the presence of extrahepatic metastasis, liver cirrhosis of Child-Pugh classification C, tumors occupying more than two thirds of the entire liver, and a performance status of more than 3 on the ECOG scale. Two patients were treated with radiotherapy only while the remaining 15 were treated with combined transcatheter arterial chemoembolization. Radiotherapy was given to the field including the tumor plus a 1.5 cm margin using a 3D-CRT technique. The radiation dose ranged from $36\~60\;Gy$ (median; 59.4 Gy). Tumor response was based on a radiological examination such as the CT scan, MR imaging, and hepatic artery angiography at $4\~8$ weeks following the completion of treatment. The acute and subacute toxicities were monitored. Results : An objective response was observed in 11 out of 17 patients, giving a response rate of $64.7\%$. The actuarial survival rate at 2 years was $21.2\%$ from the start of radiotherapy (median survival; 19 months). Six patients developed a distant metastasis consisting of a lung metastasis in 5 patients and bone metastasis in one. The complications related to 30-CRT were gastro-duodenitis $(\geq\;grade\;2)$ in 2 patients. There were no treatment related deaths and radiation induced hepatitis. Conclusion : The preliminary results show that 3D-CRT is a reliable and effective treatment modality for primary unresectable hepatocellular carcinoma compared to other conventional modalities. Further studies to evaluate the definitive role of the 3D-CRT technique in the treatment of primary unresectable hepatocellular carcinoma are needed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.1
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• pp.13-34
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• 2022

Soil moisture data have been collected at 11 agrometeorological stations operated by The Korea Meteorological Administration (KMA). This study aimed to verify the accuracy of soil moisture data of KMA and develop a correction formula to be applied to improve their quality. The soil of the observation field was sampled to analyze its physical properties that affect soil water content. Soil texture was classified to be sandy loam and loamy sand at most sites. The bulk density of the soil samples was about 1.5 g/cm³ on average. The content of silt and clay was also closely related to bulk density and water holding capacity. The EnviroSCAN model, which was used as a reference sensor, was calibrated using the self-manufactured "reference soil moisture observation system". Comparison between the calibrated reference sensor and the field sensor of KMA was conducted at least three times at each of the 11 sites. Overall, the trend of fluctuations over time in the measured values of the two sensors appeared similar. Still, there were sites where the latter had relatively lower soil moisture values than the former. A linear correction formula was derived for each site and depth using the range and average of the observed data for the given period. This correction formula resulted in an improvement in agreement between sensor values at the Suwon site. In addition, the detailed approach was developed to estimate the correction value for the period in which a correction formula was not calculated. In summary, the correction of soil moisture data at a regular time interval, e.g., twice a year, would be recommended for all observation sites to improve the quality of soil moisture observation data.

• Journal of Animal Environmental Science
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• v.18 no.2
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• pp.99-110
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• 2012

The purpose of the study is to collect basis data for to establish standard administrative processes of liquid fertilizer treatment. From this survey we could make out the key point of each step through a case of effective liquid manure treatment system in pig house. It is divided into six step; 1. piggery slurry management step, 2. Solid-liquid separation step, 3. liquid fertilizer treatment (aeration) step, 4. liquid fertilizer treatment (microorganism, recirculation and internal return) step, 5. liquid fertilizer treatment (completion) step, 6. land application step. From now on, standardization process of liquid manure treatment technologies need to be develop based on the six steps process.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.93-104
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• 2017

CCS (Carbon Capture and Storage) is a technical process to capture $CO_2$ from industrial and energy-based sources, to transfer and sequestrate impressed $CO_2$ in geological formations, oceans, or mineral carbonates. However, potential $CO_2$ leakage exists and causes environmental problems. Thus, this study was conducted to analyze the spatial and temporal variations of $CO_2$ fluxes and concentrations after artificial $CO_2$ release. The Environmental Impact Evaluation Test Facility (EIT) was built in Eumseong, Korea in 2015. Approximately 34kg $CO_2$ /day/zone were injected at Zones 2, 3, and 4 among the total of 5 zones from October 26 to 30, 2015. $CO_2$ fluxes were measured every 30 minutes at the surface at 0m, 1.5m, 2.5m, and 10m from the $CO_2$ releasing well using LI-8100A until November 13, 2015, and $CO_2$ concentrations were measured once a day at 15cm, 30cm, and 60cm depths at every 0m, 1.5m, 2.5m, 5m, and 10m from the well using GA5000 until November 28, 2015. $CO_2$ flux at 0m from the well started increasing on the fifth day after $CO_2$ release started, and continued to increase until November 13 even though the artificial $CO_2$ release stopped. $CO_2$ fluxes measured at 2.5m, 5.0m, and 10m from the well were not significantly different with each other. On the other hand, soil $CO_2$ concentration was shown as 38.4% at 60cm depth at 0m from the well in Zone 3 on the next day after $CO_2$ release started. Soil $CO_2$ was horizontally spreaded overtime, and detected up to 5m away from the well in all zones until $CO_2$ release stopped. Also, soil $CO_2$ concentrations at 30cm and 60cm depths at 0m from the well were measured similarly as $50.6{\pm}25.4%$ and $55.3{\pm}25.6%$, respectively, followed by 30cm depth ($31.3{\pm}17.2%$) which was significantly lower than those measured at the other depths on the final day of $CO_2$ release period. Soil $CO_2$ concentrations at all depths in all zones were gradually decreased for about 1 month after $CO_2$ release stopped, but still higher than those of the first day after $CO_2$ release stared. In conclusion, the closer the distance from the well and the deeper the depth, the higher $CO_2$ fluxes and concentrations occurred. Also, long-term monitoring should be required because the leaked $CO_2$ gas can remains in the soil for a long time even if the leakage stopped.