• 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.

• Research in Plant Disease
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• v.28 no.2
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• pp.98-107
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• 2022

The incidence of Tomato brown rugose fruit virus (ToBRFV) and biological and molecular characterization of the Palestinian isolates of ToBRFV are described in this study. Symptomatic leaf samples obtained from Solanum lycopersicum L. (tomatoes) and Nicotiana tabacum L. (cultivated tobacco) plants were tested for tobamoviruses infection by reverse transcription polymerase chain reaction. Tomato leaf samples collected from Tulkarm and Qalqilia are infected with ToBRFV-PAL with an infection rate of 76% and 72.5%, respectively. Leaf samples collected from Jenin and Nablus were found to be mixed infected with ToBRFV-PAL and Tobacco mosaic virus (TMV) (100%). Sequence analysis of the ToBRFV-PAL genome showed that the net average nucleotide divergence between ToBRFV/F48-PAL strain and the Israeli and Turkish strains was 0.0026398±0.0006638 (±standard error of mean), while it was 0.0033066±0.0007433 between ToBRFV/F42-PAL and these two isolates. In the phylogenetic tree constructed with the complete genomic sequence, all the ToBRFV isolates were clustered together and formed a sister branch with the TMV. The sequenced Palestinian isolates of ToBRFV-PAL shared the highest nucleotide identity with the Israeli ToBRFV isolate suggesting that the virus was introduced to Palestine from Israel. The findings of this study enhance our understanding of the biological and molecular characteristics of ToBRFV which would help in the management of the disease.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.1
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• pp.25-32
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• 2022

Investment casting is a production method commonly used to manufacture precision equipment, medical fields, and accessories, and has continued to develop through the modernization of equipment and high quality of materials, and its scope of use has been expanded. The purpose of this study is to minimize the defect rate by deriving structural improvement and standardization of mold temperature, which are key elements of the investment casting process, to minimize the defect rate. The scope of the study is limited to jewelry manufacturing casting processes suitable for understanding the structure and principles of small gate, and an experimental research is to be conducted by using soft Wax, gypsum powder, and 14 K gold as research materials. According to the results, the most appropriate casting standard temperature for the casting pattern of Alloy 14 k was the lowest turbulence at 980℃ flask temperature of 550℃, so good products could be produced. As a future task of this study, detailed studies are needed to data the structure and system temperature of small gate, reduce production defects in the field, and provide data for excellent investment casting competitiveness.

• Journal of Software Assessment and Valuation
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• v.7 no.1
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• pp.29-39
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• 2011

To evaluate the quality of clone detection tools, we should know how many clones the tool misses. Hence we need to have the standard code-clone reference corpus for a carefully chosen set of sample source codes. The reference corpus available so far has been built by manually collecting clones from the results of various existing tools. This paper presents a tree-pattern-based clone detection tool that can be used for automatic generation of reference corpus. Our tool is compared with CloneDR for precision and Bellon's reference corpus for recall. Our tool finds no false positives and 2 to 3 times more clones than CloneDR. Compared to Bellon's reference corpus, our tools shows the 93%-to-100% recall rate and detects far more clones.

• Journal of the Korean Wood Science and Technology
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• v.52 no.2
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• pp.118-133
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• 2024

The sapwood portion of fast-growing teak is mostly ignored due to its inferior quality. One of the possibilities for utilizing sapwood waste is to convert it into activated carbon that has good adsorption capabilities. The raw materials used in this research were sapwood of 14-year-old fast-growing teak sapwood (FTS) waste, which was taken from three trees from community forests in Wonosari, Gunungkidul, Yogyakarta Special Region. FTS waste was taken from the bottom of the tree up to a height of 1.3 m. The activation process is conducted with an activation temperature of 750℃, 850℃, and 950℃. The heating duration consists of three variations: 30 min, 60 min, and 90 min. The quality evaluation parameters of activated carbon include yield, moisture content, volatile matter content, ash content, fixed carbon content, adsorption capacity of benzene, adsorption capacity of methylene blue, and adsorption capacity of iodine. The results showed that the activated carbon produced had the following quality parameters: yield of 75.61%; moisture content of 1.27%; volatile matter content of 9.98%; ash content of 5.43%; fixed carbon content of 84.58%; benzene absorption capacity of 8.58%; methylene blue absorption capacity of 87.73 mg/g; and iodine adsorption capacity of 948.19 mg/g. It can be concluded that activated carbon from FTS waste has good iodine adsorption, which fulfilled the SNI 06-3730-1995 quality standard. Due to the iodine adsorption ability of FTS waste activated carbon, the conversion of FTS waste to activated carbon is categorized as a potential method to increase the value of this material.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.1
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• pp.23-28
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• 2015

This study was performed to investigate the adequate standard pot and number of plants per tree of raising seeding pot on the foxtail millet transplanting culture in the southern province. Due to the various application of wellbeing-health food recently, for upbringing of the foxtail millet, millet and sorghum in minor cereals, R & D and policy support is being promoted actively. The foxtail millet growing season is so short from 90 to 130 days, and it is large variations for a growth temperature. The main results are as follows. When it comes to foxtail millet transplantation, seedling quality of 406 holes, 200 holes and 162 holes of raising seeding pot type were not all significant, and field rooting percentage is accounted for all 94 to 95%. Yield of a foxtail millet was exposed in 406holes 305 kg/10a>162holes 303 kg> 200holes 302 kg order, and it was no significance between test processing. When it's the raising seeding transplanting culture, in case of pot culture, 406holes pot culture were reduced the bed soil cost 63%, pot 50%, working hours 18% for 200holes pot. Transplanting seedling quality per a foxtail millet transplanting culture method, dry weight was high inclination as transplanting number of plant is less, and field rooting percentage displayed more than all 95%. Yield appeared to 2 plants seedling transplanting 315kg/10a> 3 plants seedling transplanting 304kg>1 plant seedling transplanting 256kg order. The projected cost per the pot-sort on the raising seeding transplanting culture of foxtail millet, the seedling transplanting culture of 406holes was reduced 40% percentages compared to 200holes as 76,230won/10a. As a result, 406holes pot and 2plants seedling transplanting culture, labor-saving culture was possible.

• Horticultural Science & Technology
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• v.33 no.4
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• pp.591-597
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• 2015

We determined the total C and N stocks in trees and soils after 1 year of fertilization in an experimental orchard with 16-year-old 'Niitaka' pear (Pyrus pyrifolia Nakai cv. Niitaka) trees planted at $5.0m{\times}3.0m$ spacing on a Tatura trellis system. Pear trees were fertilized at the rate of 200 kg N, 130 kg P and $180kg\;K\;ha^{-1}$. At the sampling time (August 2013), trees were uprooted, separated into six fractions [trunk, main branches, lateral branches (including shoots), leaves, fruit, and roots] and analyzed for their total C and N concentrations and dry masses. Soil samples were collected from 0 to 0.6 m in 0.1 m intervals at 0.5 m from the trunk, air-dried, passed through a 2-mm sieve, and analyzed for total C and N concentrations. Undisturbed soil core samples were also taken to determine the bulk density. Dry mass per tree was 5.6 kg for trunk, 12.0 kg f or m ain branches, 15.7 kg for lateral branches, 5.7 kg for leaves, 9.8 kg for fruits, and 10.5 kg for roots. Total amounts of C and N per tree were respectively 2.6 and 0.02 kg for trunk, 5.5 and 0.04 kg for main branches, 7.2 and 0.07 kg for lateral branches, 2.6 and 0.11 kg for leaves, 4.0 and 0.03 kg for fruit, and 4.8 and 0.05 kg for roots. Carbon and N stocks stored in the soil per hectare were 155.7 and 14.0 Mg, respectively, while those contained in pear trees were 17.8 and $0.2Mg{\cdot}ha^{-1}$ based on a tree density of 667 trees/ha. Overall, C and N stocks per hectare stored in the pear orchard were 173.6 and 14.2 Mg, respectively. Compared with results obtained in 2012, the amounts of C stocks have increased by $17.7Mg{\cdot}ha^{-1}$, while those of N stocks remained virtually unchanged ($0.66Mg{\cdot}ha^{-1}$).