• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Journal of Forest and Environmental Science
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• v.38 no.3
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• pp.141-151
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• 2022

Soil seed banks as natural storage of plant seeds play an important role in the maintenance and regeneration of watershed. Natural regeneration potential of the soil seed bank of Land use types (LUTs) in Ogun River watershed (ORW) was investigated. ORW was stratified using proportionate sampling technique into Guinea Savannah (GS), Rainforest (RF) and Swamp Forest (SF) Ecological Zones (EZs). Three LUTs: Natural Forest (NF), Disturbed Forest (DF) and Farmland (FL) were purposively selected in GS: GSNF, GSDF, GSFL; RF: RFNF, RFDF, RFFL and SF: SFNF, SFDF, SFFL, respectively. Systematic line transects was used in the laying of the sample plots. Sample plots of 25 m×25 m were established in alternate positions. Ten 1 m×1 m quadrats were randomly laid for soil core sampling from previously randomly selected ten plots. The core samples (10) were pooled per plot in each LUT and placed in individual trays. Ten trays with sterilized soil were used as control. The trays were watered regularly and checked for seedlings emergence fortnightly for 18 months. The experimental design used was 3×3 factorial experiments. ANOVA, Diversity index (H') and Similarity index (SI) were used to analyze the data. There was significant difference in seedling emergence among ecological zones and land use types (p<0.05). A total of 4,400 seedlings emerged from the soil samples. All species were distributed among 32 families. FL in the RF had the highest number of germinated seeds (705±37.33 seedlings) followed by DF in the RF (701±49.6 seedlings). The lowest emergence was in NF of the SF (199±28.41 seedlings). DF in the RF had highest number of species (34) distributed among 22 families. Emergence from soil seed bank of NF in ORW was generally with more of tree species than herbs that were predominant in FL and DF.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.629-638
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• 2021

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands. Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model. Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model. Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• Journal of People, Plants, and Environment
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• v.23 no.5
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• pp.507-519
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• 2020

Background and objective: This study analyzed the growth characteristics, in relation to the soil moisture content, of trees planted in an environment with limited soil depth to provide the baseline data for effective irrigation management. Methods: The experimental treatment was divided into soil moisture contents (SMC) of 20%, 15%, 10%, 5%, and 1%, and the respective watering times were set accordingly. As for plant materials, Nandina domestica, Euonymus alatus, Thuja occidentalis, Hibiscus syriacus, Pinus densiflora, and Pyracantha angustifolia, were chosen for this study, because they are highly likely to be used in urban street environments. Results: The minimum irrigation point suitable for each species was determined by considering various aspects of visual quality and water efficiency in terms of plant growth, including characteristics such as height, root diameter, rootlet development, and weight. Our results indicate that N. domestica should be watered so that the moisture content of the soil is of at least 5%, based on the balance between the stem and roots, as well as on visual quality. E. alatus and P. angustifolia are suitable for watering that results in at least 10% SMC, considering the height, root growth, weight, and visual quality of plants. As for T. occidentalis, it showcases moderate growth with a soil moisture content of at least 5%. Finally, the minimum irrigation time required to obtain 15% SMC is appropriate, in terms of plant growth, fresh weight, and visual quality, for H. syriacus and P. densiflora. Conclusion: This study suggested a basic irrigation guideline for container gardens where trees planted in environments with limited soil depth can be managed so that they are visually appropriate and in good condition of growth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1709-1715
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• 2021

In order to accurately extract the necessary text from the web page, the method of specifying the tag and style attributes where the main contents exist to the web crawler has a problem in that the logic for extracting the main contents. This method needs to be modified whenever the web page configuration is changed. In order to solve this problem, the method of extracting the text by analyzing the frequency of appearance of the text proposed in the previous study had a limitation in that the performance deviation was large depending on the collection channel of the web page. Therefore, in this paper, we proposed a method of extracting texts with high accuracy from various collection channels by analyzing not only the frequency of appearance of text but also parent tag paths of text nodes extracted from the DOM tree of web pages.

• ETRI Journal
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• v.44 no.4
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• pp.573-587
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• 2022

The agricultural sector is completely different from other sectors since it completely relies on various natural and climatic factors. Climate changes have many effects, including lack of annual rainfall and pests, heat waves, changes in sea level, and global ozone/atmospheric CO₂ fluctuation, on land and agriculture in similar ways. Climate change also affects the environment. Based on these factors, farmers chose their crops to increase productivity in their fields. Many existing agricultural ontologies are either domain-specific or have been created with minimal vocabulary and no proper evaluation framework has been implemented. A new agricultural ontology focused on subdomains is designed to assist farmers using Jaccard relative extractor (JRE) and Naïve Bayes algorithm. The JRE is used to find the similarity between two sentences and words in the agricultural documents and the relationship between two terms is identified via the Naïve Bayes algorithm. In the proposed method, the preprocessing of data is carried out through natural language processing techniques and the tags whose dimensions are reduced are subjected to rule-based formal concept analysis and mapping. The subdomain ontologies of weather, pest, and soil are built separately, and the overall agricultural ontology are built around them. The gold standard for the lexical layer is used to evaluate the proposed technique, and its performance is analyzed by comparing it with different state-of-the-art systems. Precision, recall, F-measure, Matthews correlation coefficient, receiver operating characteristic curve area, and precision-recall curve area are the performance metrics used to analyze the performance. The proposed methodology gives a precision score of 94.40% when compared with the decision tree(83.94%) and K-nearest neighbor algorithm(86.89%) for agricultural ontology construction.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.355-360
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• 2022

As the increase cost of domestic red pepper and the increase of imported red pepper, damage cases such as false labeling of the origin of red pepper powder are issued. Accordingly we need to determine quickly and accurately for the origin of red pepper powder. The used method for presently determining the origin has the limitation in that it requires a lot of cost and time by experimentally comparing and analyzing the components of red pepper powder. To resolve the issues, this study proposes machine learning algorithm to classifiy domestic and imported red pepper powder. We have built machine learning model with 53 components contained in red pepper powder and validated. Through the proposed model, it was possible to identify which ingredients are importantly used in determining the origin. In the near future, it is expected that the cost of determining the origin can be further reduced by expanding to various foods as well as red pepper powder.

• Journal of Environmental Science International
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• v.31 no.11
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• pp.941-950
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• 2022

This study was conducted to provide data and stem information to establish a local volume table of Cryptomeria japonica in Jeju Island. Stem analysis was performed on 26 trees by selecting two average trees from each site of the 13 plots of C. japonica stands in 2021 and 2022. During the analysis stage, one outlier tree was rejected, and a total of 260 observations of the specific stem height of 25 trees were used. Of the seven major taper equation models applied for parameter estimation and statistical verification, the Muhairwe 1999 model was found to be the best fit and selected as the optimal model. Stem shape-related estimates were acquired through the selected model, and sectional measurements according to the Smalian formula applied at an interval of 10 cm from the height of the stem were used to develop a volume table. A paired t-test comparison between the C. japonica volume obtained from the present study and those selected from the current yield table by NIFoS(2020), revealed significant differences (p<0.05), highlighting the necessity of a local volume table for C. japonica in Jeju Island.