• Animal cells and systems
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• v.14 no.3
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• pp.207-212
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• 2010

The characteristics of insect communities in specific localities reflect climatic and environmental status and change. We investigated the description pattern of new insect species by reviewing announcements of new or newly recorded insects in Korea published in five systematic journals between 1999 and 2009: Entomological Research (1999-2009), Insect Koreana (1999-2003), Journal of Asia-Pacific Entomology (1999-2009), Korean Journal of Applied Entomology (1999-2009), and Korean Journal of Systematic Zoology (1999-2009). A total of 757 new species were reported in 299 research papers. More than 85% of the newly described or reported species belonged to four orders: Coleoptera (225 species), Lepidoptera (202 species), Hymenoptera (141 species), and Diptera (82 species). The number of new species fluctuated from year to year, and a few major authors described most species. The graph of the cumulative number of species described in Korea was not asymptotic. We also examined the decadal trends in the proportion of species according to their biogeographical origin. The numbers of northern (Palearctic and Nearctic) and southern (Oriental) species recorded in Korea between 1999 and 2009 were increasing mainly due to the recent taxonomic research environment. It is also possible that recent climatic change induces new migrants toKorea from more southern parts of Oriental region, but more information is needed to confirm this hypothesis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.1
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• pp.40-50
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• 2023

Objectives: This study examined the validity of the Childhood Autism Rating Scale, Second Edition (CARS-2) compared with the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) in identifying autism spectrum disorder (ASD). Methods: A total of 237 children were tested using both the CARS-2 and ADOS-2. We examined the correlation using Pearson's correlation analysis. In addition, we used a receiver operating characteristic graph to determine the optimal standard version of the CARS-2 (CARS2-ST) cutoff score for ASD diagnosis using the ADOS-2. Results: The concurrent validity of the CARS2-ST was demonstrated by a significant correlation with the ADOS-2 (r=0.864, p<0.001). The optimal CARS2-ST cutoff scores were 30 and 28.5 for identifying autism and autism spectrum, respectively, based on the ADOS-2. Conclusion: We suggest a newly derived CARS2-ST cutoff score of 28.5 for screening ASD and providing early intervention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1708-1734
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• 2022

In the development of logistics system, the breakthrough of important technologies such as technology platform for logistics information management and control is the key content of the study. Based on Javascript and JQuery, the logistics system realizes real-time monitoring, collection of historical status data, statistical analysis and display, intelligent recommendation and other functions. In order to strengthen the cooperation of warehouse storage, enhance the utilization rate of resources, and achieve the purpose of real-time and visual supervision of transportation equipment and cargo tracking, this paper studies the VNF dynamic deployment and SFC routing problem in the network load change scenario based on the logistics system. The BIP model is used to model the VNF dynamic deployment and routing problem. The optimization objective is to minimize the total cost overhead generated by each SFCR. Furthermore, the application of the SFC mapping algorithm in the routing topology solving problem is proposed. Based on the concept of relative cost and the idea of topology transformation, the SFC-map algorithm can efficiently complete the dynamic deployment of VNF and the routing calculation of SFC by using multi-layer graph. In the simulation platform based on the logistics system, the proposed algorithm is compared with VNF-DRA algorithm and Provision Traffic algorithm in the network receiving rate, throughput, path end-to-end delay, deployment number, running time and utilization rate. According to the test results, it is verified that the test results of the optimization algorithm in this paper are obviously improved compared with the comparison method, and it has higher practical application and promotion value.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.15-20
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• 2023

Machine learning is comprised of supervised learning, unsupervised learning and reinforcement learning as the type of data and processing mechanism. In this paper, as input and output are unclear and it is difficult to apply the concrete modeling mathematically, reinforcement learning method are applied for crawling robot in this paper. Especially, Q-Learning is the most effective learning technique in model free reinforcement learning. This paper presents a method to implement a crawling robot that is operated by finding the most optimal crawling method through trial and error in a dynamic environment using a Q-learning algorithm. The goal is to perform reinforcement learning to find the optimal two motor angle for the best performance, and finally to maintain the most mature and stable motion about EV3 Crawling robot. In this paper, for the production of the crawling robot, it was produced using Lego Mindstorms with two motors, an ultrasonic sensor, a brick and switches, and EV3 Classroom SW are used for this implementation. By repeating 3 times learning, total 60 data are acquired, and two motor angles vs. crawling distance graph are plotted for the more understanding. Applying the Q-learning reinforcement learning algorithm, it was confirmed that the crawling robot found the optimal motor angle and operated with trained learning, and learn to know the direction for the future research.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• Korean Journal of Environmental Biology
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• v.19 no.2
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• pp.153-157
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• 2001

The relationship between SPM (Suspended Particulate Matter) and fungal spore in the air was investigated in Seosan, a rural county of Korea, in spring of 2000. SPM concentrations in the air were $199.8\mu{g}\;m^{-3}$, in the 1st Yellow Sand Period (March, 23-24), $249.4\mu{g}\;m^{-3}$ in the 2nd Yellow Sand Period (April, 7-9) and $98.9\mu{g}\;m^{-3}$ in the Non Yellow Sand Period (May, 12-16), respectively. Although there was somewhat difference in total SPM concentration between the two Yellow Sand Periods, majority of the total SPM were composed of $5\mu{m}$ sized coarse particles over the two periods. However, fine particles sized about $1\mu{m}$and coarse particles sized about $5-6\mu{m}$ ultimately showed peaks, which was within typical bimodal pattern at the graph of SPM size distribution in the Non Yellow Sand Period. Four mold genera grown from airborne fungal spores were finally identified in full-grown colonies at the SPM samples during the Yellow Sand Periods. These genera were Fusarium, Aspergillus, Penicillium and Basipetospora.

• Journal of the Korean Society of Tobacco Science
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• v.20 no.2
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• pp.191-197
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• 1998

Discriminant analysis using near infrared spectra derived from Korean Flue-cured(KF) and American Flue-cured(AF), and also Korean Burley(KB) and American Burley(AB) tobacco was done to classify flue-cured and burley tobacco as either grown in Korea or grown in the USA. Samples were scanned in the wavelength of 400 ～ 2500 nm by near infrared analyzer(NIRSystem Co., model 6500). The discrimination equations for flue-cured and burley tobacco were developed using partial least square 2 method in Infrasoft International NIRS 3 software package. KF samples used for the development of the discrimination equations were higher contents of total sugar, crude ash and chlorine, and higher value of leaf density and brightness, but lower contents of nicotine, total nitrogen and ether extracts, and higher value of redness than those of AF samples. KB samples were higher contents of nicotine, crude ash and chlorine, but lower contents of ether extracts and value of brightness than those of AB samples. On 3 dimensional graph drawn with 3 principal component scores calculated with 3 principal component from KF and KB sample spectra, KF sample spectra were significantly different from AF, and also KB sample spectra were significantly different from AB. The discrimination equations of flue-cured and burley were developed with 3 principal component, respectively. The discrimination equations for flue-cured and burley had a standard error of 0.03 and 0.04, and a R2 of 0.88 and 0.84, respectively. The tobacco samples used for the development of discrimination equation were perfectly classified as KF and AF by flue-cured discrimination equation, and also perfectly classified KB and AB by burley discrimination equation, respectively. The correct classification rates of KF and AF samples not used for the development of discrimination equations were 9S % (828 out of 869 samples) and 98 % (98 out of 100 samples) by flue-cured discrimination equations, and KB and AB samples were 94%(345 out of 368 samples) and 100%(42 out of 42 samples) by burley discrimination equations, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.3
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• pp.297-302
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• 2008

Application of genotype by environment ($G\;{\times}\;E$) interaction would be used for identifying optimum test condition of the varietal adaptation in the establishment of breeding purpose. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant difference for $G\;{\times}\;E$ interaction were observed for all variable examined. For yield, 0.18 of total sum of squares corresponded to $G\;{\times}\;E$ interaction. Correlation analysis was carried out between genotypic scores of the first interaction principal component axis (IPCA 1) for agronomic characters. Significant correlations were observed between IPCA 1 for yield and capsule bearing stem length (CBSL), number of capsule per plant (NOC). The biplot of grain yield means for IPCA1 which accounted for 34% of the variation in total treatment sums of squares showed different reaction according to $G\;{\times}\;E$ interaction, genotypes and environments. Taegu showed relatively lower positive IPCA1 scores, and it also showed smaller coefficient variation of yield mean where it is recommendable as a optimal site for the sesame cultivar adaptation and evaluation trial. In case of variables, Yangbaek and M1 showed relatively lower IPCA1 scores, but the score direction showed opposite each other on the graph. Ansan, Miryang1, Miryang4, and Miryang6 seemed to be similar group in view of yield response against IPCA1 scores. These results will be helpful to select experimental site for sesame in Korea to minimize $G\;{\times}\;E$ interaction for the selection of promising genotype with higher stability.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.7
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• pp.1098-1102
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• 2005

Properties of rice flours prepared from milled and broken rice produced by pre-washing and subsequent drying process were investigated. Compared to untreated ordinary milled rice, pre-washing process slightly increased lightness of rice flour, while decreased yellowness. Both WAI and WSI were higher in the flour from pre-washed rice, and gel consistency was the highest in the flour from pre-washed broken rice. Pre-washed rice showed increased amylograph peak viscosity and reduced setback values. On the other hand, pre-washed broken rice showed decreased peak viscosity and increased setback values. Differential scanning calorimetry (DSC) results showed that the pre-washing process reduced gelatinization onset- and peak temperatures, with increased gelatinization enthalpy. Total bacteria and yeast count were lower in the pre­washed rice, suggesting the pre-washing process could partially eliminate microbial contamination of ordinary milled rice.