• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.73-78
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• 2022

This experiment was conducted to establish the technology for artificial hay preparation in Korea. Using far-infrared heater, a device that can control temperature, airflow, and far-infrared radiation was produced and conducted on the fourth harvested alfalfa. The drying conditions were carried out by selecting a total of four conditions. For each condition, the radiation rate was set to around 40% (33-42%), and the temperature was set at 58~65℃, and the speed of the airflow was fixed at 60m/s. The overall drying time was set to 30 min in the single and 60 min (30-30 min) and 90 min (30-30-30 min) in the complex condition, and the radiation rate and temperature were changed by time period. In the case of drying condition 1, the final dry matter (DM) content was 46.26%, which did not reach a DM suitable for hay. However, all of the alfalfa corresponding to the remaining drying conditions 2 to 7 showed a DM content of 80% or more, resulting in optimal alfalfa hay production. In power consumption according to the drying conditions, the second drying condition showed the lowest at 4.7 KW, and the remaining drying conditions were as high as 6.5 to 7.1 KW. The crude protein content was found to be high at an average of 25.91% and it showed the highest content in the 5th drying condition (26.93%) and the lowest value in the 6th drying condition (25.16%). The digestibility showed a high value with an average of 84.90%, and there was no significant difference among treatments (p>0.05). Considering the above results, it was judged that drying condition 2 was the most advantageous.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.30-36
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• 2022

A sensor is needed to continuously and automatically measure the change in HNS concentration in industrial facilities that directly discharge to the sea after water treatment. The basic function of the sensor is to be able to detect ppb levels even at room temperature. Therefore, a method for increasing the sensitivity of the existing sensor is proposed. First, a method for increasing the conductivity of a film using a conductive carbon-based additive in a nanoparticle thin film and a method for increasing ion adsorption on the surface using a catalyst metal were studied.. To improve conductivity, carbon black was selected as an additive in the film using ITO nanoparticles, and the performance change of the sensor according to the content of the additive was observed. As a result, the change in resistance and response time due to the increase in conductivity at a CB content of 5 wt% could be observed, and notably, the lower limit of detection was lowered to about 250 ppb in an experiment with organic solvents. In addition, to increase the degree of ion adsorption in the liquid, an experiment was conducted using a sample in which a surface catalyst layer was formed by sputtering Au. Notably, the response of the sensor increased by more than 20% and the average lower limit of detection was lowered to 61 ppm. This result confirmed that the chemical resistance sensor using metal oxide nanoparticles could detect HNS of several tens of ppb even at room temperature.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.25-32
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• 2009

Currently, various types of TDM (Transportation Demand Management) policies are being studied and implemented in an attempt to overcome the limitations of supply oriented policies. In this context, this paper addressed issues of effectiveness and possible domestic implementation of the HOT lane. The possible site of implementation selected for this simulation study is part of the Kyung-bu freeway, where a dedicated bus lane is currently being operated. Minimum length of distance required in between interchanges and access points of the HOT lane for vehicles to safely enter and exit the lane, and traffic management policies for effectively managing the weaving traffic trying to enter and exit the HOT lane were presented. A 5.2km section of freeway from Ki-heuing IC to Suwon IC and a 8.3km section from Hak-uei JC to Pan-gyo JC have been selected as possible sites of implementation for the HOT lane, in which congestion occurs regularly due to the high level of travel demand. VISSIM simulation program has been used to analyze the effects of the HOT lane under the assumption that one-lane HOT lane has been put into operation in these sections and that the lane change rate were in between 5% to 30%. The results of each possible scenario have proven that overall travel speed on the general lanes have increased as well by 1.57~2.62km/h after the implementation of the HOT lane. It is meaningful that this study could serve as a basic reference data for possible follow-up studies on the HOT lane as one effective method of TDM policies. Considering that the bus travel rate would continue increase and assuming the improvement in travel speed on general lanes, similar case study can be implemented where gaps between buses on bus lane are available, as a possible alternative of efficient bus lane management policies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.436-445
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• 2023

Methane (CH₄) emissions from rice paddies are mainly observed using the closed chamber method or the eddy covariance method. In this study, a new observation technique combining a portable gas analyzer (Model LI-7810, LI-COR, Inc., USA) and an automatic opening/closing chamber (Model Smart Chamber, LI-COR, Inc., USA) was introduced based on the strengths and weaknesses of the existing measurement methods. A cylindrical collar was manufactured according to the maximum growth height of rice and used as an auxiliary measurement tool. All types of measured data can be monitored in real time, and CH₄ flux is also calculated simultaneously during the measurement. After the measurement is completed, all the related data can be checked using the software called 'SoilFluxPro'. The biggest advantage of the new observation technique is that time-series changes in greenhouse gas concentrations can be immediately confirmed in the field. It can also be applied to small areas with various treatment conditions, and it is simpler to use and requires less effort for installation and maintenance than the eddy covariance system. However, there are also disadvantages in that the observation system is still expensive, requires specialized knowledge to operate, and requires a lot of manpower to install multiple collars in various observation areas and travel around them to take measurements. It is expected that the new observation technique can make a significant contribution to understanding the CH₄ emission pathways from rice paddies and quantifying the emissions from those pathways.

• Journal of Korean Society of Forest Science
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• v.64 no.1
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• pp.11-19
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• 1984

Potted, germinating Pinus rigida ${\times}$ P. taeda seedlings were inoculated with Pisolithus tinctorius (Pt) ectomycorrhizal fungus to test the effectiveness of Pt in relation to organic amendment and changes in soil fertility and soil texture. Pt was cultured as mycelia in vermiculite-peat moss mixture with nutrients and added to sterilized pot soils with or without organic amendment (fully fermented compost) at three soil texture levels (sand, loamy sand, and sandy loam) in a factorial design. Plants were grown in a greenhouse for 4 months and harvested to compare their growth with non-mycorrhizal plants and plants infected by natural fungi. Regardless of sod texture, soil fertility, or organic amendment, seedlings inoculated with Pt were better in dry weight and height than non-mycorrhizal plants or those infected by natural fungi. An exception was observed in the most fertile soil (0.075% N and 1.32% organic matter content in sandy loam with organic amendment), where non-mycorrhizal plants were slightly bigger (8%) and heavier (18%) than Pt-inoculated plants. In over-all average, Pt-inoculated seedlings were 30% taller and 107% heavier than those infected by natural fungi and 31 % taller and 60% heavier than non-mycorrhizal plants. Growth stimulation of seedlings by Pt was more pronounced in less fertile sand soil when organic was not amended. Mycorrhizal frequency of Pt (% of mycorrhizal root tips) was reduced to about half (from 84 to 33% in sandy loam and from 77 to 40% in loamy sand) by organic amendment, while that of natural fungi was not significantly affected. Severe nitrogen deficiency was observed in the needles of non-mycorrhizal plants (1.38% N), while both Pt-inoculated plants (1.68% N) and those infected by natural fungi (1.89% N) did not develop symptom, suggesting an active role of mycorrhizae in absorption of soil nitrogen. Top to root ratio increased with organic amendment to non-mycorrhizal plants, but was not significantly affected by fungal treatment. It was concluded from this study that relative effectiveness of Pt was determined by soil fertility. Organic amendment to less fertile sand soil increased effectiveness of Pt, while the same amendment to more fertile loamy sand and sandy loam decreased effectiveness of Pt. Benefits of Pt mycorrhizae would be expected most either when organic was not added to the soil, or when soil nutrients were not abundant.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.6
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• pp.909-919
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• 2010

The purpose of this study was to investigate the demand and use of fresh-cut produce in school foodservice and restaurant industries. The subjects of this survey study were 200 school nutritionists and 70 cooks or managers in the restaurant industry nationwide. The data were collected by means of self-administered or e-mail questionnaires. Data analysis was completed using the SPSS window (ver. 12.0) program including frequency, $\chi^2$-test and t-test. Survey questions assessed the general characteristic of respondents, and the supply, use, and demand of fresh-cut produce in school foodservice and restaurant industries. Over 74% of the subjects have used fresh-cut produce. Most of the school foodservice (84.0%) kept fresh-cut produce for one day, while restaurant industry (28.3%) kept them up to three days. The nutritionists of school foodservice and managers of restaurant industry considered origin and date of production as the most important factor, respectively, when fresh-cut produce were being used. Fresh-cut root vegetable, such as potato and carrot was used mostly. The main reason not to use the fresh cut produce was due to the distrust of the fresh-cut produce safety in school foodservice and cost in restaurant industry. The main problem in fresh-cut produce use was the need of rewashing (29.9%) in school foodservice and irregular size (39.0%) in restaurant industry. These results indicate that the quality standard and size specification must be prepared with production guideline of safe fresh-cut produce.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.94-102
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• 2001

Bullying has recently become a serious social problem in Korean society. Bullying, which is defined as a phenomenon that one particular student is intensively and continuously harassed or ostracized by a group of students, is apt to produce harmful effects on bullies as well as victims. Bullying has many causes including those originated from the personality of victims and bullies. This study is designed to investigate the difference in personality trait between victims, bullies, victims/bullies, and neither. The subjects of this study were 215(115 male and 100 female) 6th-grade students in the primary school in Seoul. Questionnares were distributed to the students and their carers. The student carers were also asked to answer the questions for a survey called the Korean Personality Invertory for Children(KPI-C). SPSS was used for the statistical analysis of the collected test information；ANOVA, post hoc scheffe test, and T-test were used to analyze the differences between the tested groups. The result of the study is as follows. 1) The victims, bullies, victims/bullies and neither totaled respectively 11(5.1%), 56(26.0%), 11(5.1%) and 137(63.7%). 115 were male and 100 were female. 2) The frequency of victimized is as follows：1 time is 15(7.0%), 2 times is 4(1.9%) and more than 3 times is 3(1.4%). The frequency of bullying is as follows；1 time is 40(18.6%), 2 times is 17 (7.9%) and more than 3 times is 10(4.7%). 3) The differences between froups in KPI-C test is as follows. (1) The ESR(p=.00) scale was significantly lower in the victims group than in the neither group and the HPR(p=.00) scale and PSY(p<.01) scale were significantly higher in the former than in the latter. (2) The ESR(p=.00) scale was significantly lower in the victims/bullies group than in the neither group and the SOM(p=.00) scale and HPR(p=.00) scale were significantly higher in the formaer than in the latter. (3) The SOC(p=.00) scale, PSY(p<.01) scale and AUT(p=.00) scale were significantly higher in the victims group than in the bullies group. (4) There is statistically no difference between the bullies group and the neither group. To conclusion, Victims need to learn how to cope with harsh situations, or they will have to face difficulties in relationships. Even after they experience bullying, they may not realize why they have been bullied, or speak out for themselves.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• 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.