• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9731-9737
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• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• Journal of KIISE:Information Networking
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• v.31 no.1
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• pp.81-90
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• 2004

A Mobile Ad Hoc Network (MANET) is a collection of wireless mobile nodes dynamically self-organizing in arbitrary and temporary network topologies without the use of any existing network infrastructure. The AODV (Ad Hoc On-Demand Distance Vector) Protocol is one of the typical reactive routing protocols, in that mobile nodes initiate routing activities only in the presence of data packets in need of a route. In this paper, we focus upon the local repair mechanism of AODV. When a link is broken, the upstream node of the broken link repairs the route to the destination by initiating a local route discovery process. The process involves the flooding of AODV control messages in every node within a radius of the length from the initiating node to the destination. In this paper, we propose an efficient local repair scheme for AODV called AFLRS (AODV-based Fast Local Repair Scheme). AFLRS utilizes the existing routing information in the intermediate nodes which have been on the active route to the destination before a link break occurs. AFLRS can reduce the flooding range of AODV control messages and the route recovery time because it can repair route through the intermediate nodes. For the performance evaluation of the proposed AFLRS, we have simulated the local repair mechanisms by using NS2 and AODV-UU. The performance results show that AFLRS can achieve faster route recovery than the local repair mechanism of AODV.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.730-736
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• 2019

The demand for natural gas, which is considered an environmentally friendly energy source, is increasing, and at the same time, the market share of large pipelines for natural gas supply is increasing continuously. On the other hand, the corrosion of such large pipelines reduces the efficiency of natural gas transportation. Therefore, this study aims to establish a strategy for securing the patent rights of related technologies through quantitative analysis of patents on energy-independent high-efficiency corrosion prevention technology for large-scale pipelines for natural gas supply. In this patent technology trend study, Korean, US, Japanese, and European patents filed, published, and registered by June 2018 were analyzed, and a technical classification system and classification criteria were prepared through expert discussion. To use fuel cells as an external power source to prevent the corrosion of natural gas large-scale pipelines, it is believed that rights can be claimed using an energy control system and methods having 1) branch structures of pipeline and facility designs (decompressor/compressor/heat exchanger) and 2) decompression/preheating and pressurization/cooling technology of high pressure natural gas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6303-6309
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• 2014

In this thesis, PBL was applied to the subject for improving students' many skills that modern industrial society demands. Our engineering school developed PBL problems for PBL use, applied the problems to classes and confirmed the effectiveness of PBL. The study subjects were 63 freshman students in H University who took the 'Introduce of computer engineering'. We applied 5 PBL problems for 15 weeks. They wrote and submitted a reflective journal when they finished the every given PBL activity. In addition, they completed a class evaluation form after the activity of 5th PBL Problem ended. The study showed that the students experienced the effectiveness of PBL, such as the comprehension of the studied contents, the comprehension of the cooperative learning, authentic experience, creative problem-solving skills, presentation skills, communication ability, self-directed study ability and confidence. Some difficulties in gathering together and spending much time were also encountered. The students realized that the PBL learning activities were important methods because the students could develop into future intelligent engineers that modern industrial society demands through PBL learning activities. The main goal of an engineering school is to produce specialists with creative problem solving ability so that the effects of this study are quite promising for our engineering school.

• Journal of Sericultural and Entomological Science
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• no.12
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• pp.37-45
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• 1970

The processing management forms of our country's filature factories in 1969 are summarized as follows. (1) About 80％ of total cocoon collection is made within 5 days involving peak day, and 10％ of cocoon collection is finished until 3 days before and after the peak day, (2) About 92％ of alive cocoons transported on unpaved road, and about 40％ of the cocoons purchased by all factories are loaded on trucks from common selling station which is far beyond 40km, therefore a new packing system of alive cocoons to drop the damage of cocoon qualities, should be taken. (3) 22％ of all factories in our. country have only low-temperature cocoon drying machine. Therefore the installment of hot-air cocoon drying machine is required urgently. (4) In view of cocoon qualities in our country, the grouping method of cocoon for reeling. taken by about 50％ of the factories at percent, which classify cocoons for reeling as high group (1,2,3,4 grades) and low group(5,6 grades), will have to be replaced by the method tat classify them high group (1,2 grades) middle group (3,4 grades), low group (5,6 grades). (5) The .ratio of cocoon assorting stood about 10％ in multi-ends reeling, about 15％ in automatic reeling, conclusively, the ratio of cocoon assorting for automatic reeling was higher tan that for multi-ends reeling. One person's ability for a day in cocoon assorting reaches to about 80-100kg. (6) Cocoon cooking condition requires the increase of the cooking time, the pressure and temperature used to be prolonged as much as the qualities of cocoons are material cocoon ior automatic and double cocoon machines are treated uncompletely. (7) Automatic silk reeling is being performed at 1-2$^{\circ}C$ lower in reeling water temperature and operated at about twice velocity. (8) The temperature and humidity of rereeling room stood at 25$^{\circ}C$, 67.2％ R.H and 32.3$^{\circ}C$, 51.9％ R.H of rereeling machine are showed, Average rereeling velocity is 233m/min and large reefs charged for one person are 7.5 reels and form of skein used in all factories is double skein. (9) About 73％ of water sources for filature used under-earth water. About 48％ of all filature factories in our country have not yet water purifying equipments. Installation of the equipment for these factories seems to be urgent, (10) Denier .balance, sizing reel, seriplane, are being used in most factories as self-inspection apparatus. (11) More than 90％ of the factories use the vacum tank in rereeling process and about 20％ of them use it in cocoon cooing process (12) Only 21％ of the factories use chemicals in filature process. About all them use "Seracol 100" in cocoon cooking process and "Seracol 500" in rereeling process, (13) Above survey results explain each all factories show large difference in the processing management. Therefore, it is believed that intercommunication through seminar or technical exchange will contribute to the production evaluation of cocoon in our filature industry.

• 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.

• Sleep Medicine and Psychophysiology
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• v.26 no.1
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• pp.23-32
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• 2019

Objectives: Insomnia is not only the most common sleep-related disorder, but also is one of the most important. Knowledge of the comorbidities of insomnia is essential for proper treatment including pharmacological and non-pharmacological methods to prevent disease chronification. This study aimed to determine sleep clinic patients' knowledge of insomnia. Methods: This study recruited 44 patients (24 males and 20 females; mean age $54.11{\pm}16.30years$) from the sleep clinic at National Center for Mental Health. All subjects were asked to complete a self-report questionnaire about their reasons for visiting a sleep clinic and about their knowledge of treatment and comorbidities of insomnia. Results: The reasons for visiting the sleep clinic were insomnia symptoms of daytime sleepiness, irregular sleeping time, nightmares, snoring, and sleep apnea, in that order. Of the responders, 72.7% had a comorbidity of insomnia, and 22.7% showed high-risk alcohol use. In addition, 70.5% of responders chose pharmacological treatment of insomnia as the first option and reported collection of information about treatment of insomnia mainly from the internet and medical staff. More than half (52.3%) of the respondents reported that they had never heard about non-pharmacological treatments of insomnia such as cognitive behavioral treatment (CBT-I) or light therapy. The response rate about comorbidities of varied, with 75% of responders reporting knowledge of the relation between insomnia and depression, but only 38.6% stating awareness of the relation between insomnia and alcohol use disorder. Of the total responders, 68.2% were worried about hypnotics for insomnia treatment, and 70% were concerned about drug dependence. Conclusion: This study showed that patients at a sleep clinic had limited knowledge about insomnia. It is necessary to develop standardized insomnia treatment guidelines and educational handbooks for those suffering from insomnia. In addition, evaluation of alcohol use disorders is essential in the initial assessment of sleep disorders.

• Journal of Food Hygiene and Safety
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• v.16 no.3
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• pp.168-177
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• 2001

Sanitary management practices were assessed to insure the safety of school foodservice, to prevent the outbreak of foodborne illness, and to improve the quality of school foodservice. To accomplish these objectives, a survey was conducted and analyzed on elementary and high school foodservice operations located in Seoul area. A Questionnaire from based on HACCP standards was developed and used for self-reported evaluation of the school foodservice managers on their sanitary management practices. The results were analysed by examining their activities and identifying weaknesses in those activities. The questionnaire was composed of three sectors with all 53 questions; 33 questions for time-temperature management, 5 for personal hygiene and 15 for equipment/facility sanitation. Five-point-scale was used on the questionnaire answers. Among the schools responded,253 (98.4% of the total) were elementary schools and 19 (1.6%) were high schools. Among the three sectors, personal hygiene performance was mostly well conducted by marking average 4.06$\pm$0.57. Equipment/facility sanitation came next by marking average 3.84$\pm$0.53. Time-temperature marked average 3.45$\pm$0.46. “Storage after cooking (2.03$\pm$0.94)”was identified as the least managed activities because the school foodservice operations were not equipped well with hot holding and/or cold holding. “Separate use of sink per usage (3.03$\pm$1.10)” and “proper location of hand washing facility (3.07$\pm$1.13))” were identified as the least practiced activities in equipment/facility sanitation sector. To enhance these practices, proper number of sinks and hand washing facilities should be equipped first within the kitchen area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.

• 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.