• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.9
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• pp.504-512
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• 2016

In this study, we investigated the environmental impact of organic and conventional paddy by monitoring soil properties and water quality. We sampled and analyzed topsoil (0~15 cm), subsoil (15~30 cm), and water of organic and conventional paddy fields in Yongin and Anseong, South Korea. The statistical significance between groups was determined by Duncan's multiple range test. The results show that T-P concentrations in both topsoil and subsoil of Anseong paddy were higher than those of Yongin paddy. The significant difference of T-P between organic and conventional paddy was observed in Anseong but not in Yongin. T-N of organic paddy soil was lower than that of conventional paddy in both Anseong and Yongin region. Water content for subsoil of organic paddy in Anseong was significantly different from others, which is consistent with the results of silt-clay content. pH and EC of water in conventional paddy were higher than those in organic paddy. In Anseong, COD, T-P, and $PO_4-P$ concentration of conventional paddy were higher than those of organic paddy. The regression analysis presented that there were no significant relationship between soil properties and water quality data except T-N.

• Journal of the korean academy of Pediatric Dentistry
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• v.30 no.2
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• pp.291-297
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• 2003

While making diagnosis and the treatment plan for the growing children who visited at Chonnam National University Hospital for orthodontic treatment, authors obtained 8 lateral cephalometric measurements in antero-posterior and vertical relationship such as APDI, WITS, ANB, SN-MP, ODI, PFH/AFH, Y-axis, SUM for children aged 7 to 9 with class III malocclusion and compared them with these of 73 children of elementary school aged 7 to 9 with proper profile and normal occlusion in Gwangju. The results were as follows: 1. Between normal occlusion and class III malocclusion, ANB, SN-MP, ODI, SUM, except PFH/AFH and Y-axis showed statistically significant differences(p<0.05). 2. Between mesurements to describe skeletal disorder of antero-posterior relationship such as APDI, WITS, ANB and skeletal disorder of vertical relationship such as SN-MP, ODI, PFH/AFH, Y-axis, SUM, all of them in both normal occlusion and Class III malocclusion showed significant correlation, except Y-axis, SUM correlation(p<0.01). 3. Wald' statistics of WITS, ANB and APDI expressing skeletal disorder of antero-posterior relationship showed 7.118, 5.148, 0.741, respectively and Wald' statistics of ODI, Y-axis, PFH/AFH, SN-MP, SUM were presented 28.348, 2.238, 1.376, 0.090, 0.089, respectively. Therefore, WITS and ODI could be considered as useful diagnotic measurements for class III malocclusion.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.509-520
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• 2015

College Scholastic Ability Test(CSAT) is a primary test to evaluate the study achievement of high-school students and used by most universities for admission decision in South Korea. Because its level of difficulty is a significant issue to both students and universities, the government makes a huge effort to have a consistent difficulty level every year. However, the actual levels of difficulty have significantly fluctuated, which causes many problems with university admission. In this paper, we build two types of data-driven prediction models to predict correct answer rate and to identify significant factors for CSAT English test through accumulated test data of CSAT, unlike traditional methods depending on experts' judgments. Initially, we derive candidate question-specific factors that can influence the correct answer rate, such as the position, EBS-relation, readability, from the annual CSAT practices and CSAT for 10 years. In addition, we drive context-specific factors by employing topic modeling which identify the underlying topics over the text. Then, the correct answer rate is predicted by multiple linear regression and level of difficulty is predicted by classification tree. The experimental results show that 90% of accuracy can be achieved by the level of difficulty (difficult/easy) classification model, whereas the error rate for correct answer rate is below 16%. Points and problem category are found to be critical to predict the correct answer rate. In addition, the correct answer rate is also influenced by some of the topics discovered by topic modeling. Based on our study, it will be possible to predict the range of expected correct answer rate for both question-level and entire test-level, which will help CSAT examiners to control the level of difficulties.

• The korean journal of orthodontics
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• v.36 no.2 s.115
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• pp.136-144
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• 2006

The purpose of this study was to determine whether association exists between temporomandibular joint (TMJ) characteristics in transcranial radiographs and TMJ internal derangement and to evaluate the validity of transcranial radiographs in diagnosis of internal derangement. Transcranial radiographs and magnetic resonance imaging (MRI) of 113 TMJs from 76 subjects were used for this study and all TMJs were classified into 3 groups according to the results of MRI: normal disk position, disk displacement with reduction, and disk displacement without reduction. Transcranial analysis included linear measurement of joint spaces and condylar head angle measurement. To determine any relationship between transcranial measurements according to disk displacement, one-way ANOVA was used. The results showed that condyle-fossa relationship in standard transcranial radiographs had no relationships with disk displacement. And, as disk displacement progressed, condylar angle between head and neck increased significantly. This result can be interpreted that condylar head angle reflects structural hard tissue change according to internal derangement progress. But this is insufficient in the determination of internal derangement. Therefore, although still clinically helpful, the validity of standard transcranial radiographs to diagnose TMJ internal derangement was questioned.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.83-98
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• 2021

This study identified the influence of employees' hybrid start-up intention (intention to start a business while maintaining a job) on the employees' self-determination motivation (intrinsic motivation, extrinsic motivation) and prior knowledge through the Model of Goal-directed Behavior (MGB). We used a PLS-SEM called SmartPLS 3.0 for 126 valid samples collected by judgement extraction for office workers throughout June 13, 2020 to July 3, 2020, and empirically evaluated the measurement model (internal consistency reliability, convergent and discriminant validity) and the structural model (multicollinearity, determination coefficient, effect size, predictive relevance, etc.). Only the intrinsic motivation for realizing the hybrid start-up goal of office workers had a significant impact on the hybrid start-up attitude and subjective norms, and the prior knowledge of hybrid start-up had a significant impact on the hybrid start-up desire and the hybrid start-up intention. In order to induce hybrid start-ups for workers with unstable employment, we need systems and programs that can inspire employees with intrinsic motivation and knowledge about hybrid start-up, so follow-up researches are necessary to analyze about government systems and consulting support that can promote hybrid start-up.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.135-142
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• 2013

Contour detection is important for many computer vision applications, such as shape discrimination and object recognition. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. The novelty of this operator is that dilation is limited to Deluanary triangular. An efficient implementation is presented. The grouping algorithm is then embedded in a multi-threshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contour.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.111-120
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• 2017

In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.559-562
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• 2017

In city, tracing of power transmission lines is difficult due to compound installation of overhead and underground lines, transposition, bad view caused by trees or big buildings. It is hard problem for electrical technician on site to trace power transformers or power lines to serve customers in 3 phase -4 wires power distribution systems. It is necessary that the correct and fast tracing method is required for load balancing among distribution lines. Old technology use to trace off-lines with high power impulse injection. Our proposed method use to trace live lines with very small power high frequency signal injection. Typical power transformers in the distribution system prevent propagating the higher frequency carrier signal. The proposed method uses the limited propagation ability to identify the power transformer to serve customers. Two end communication terminals are required to be synchronized between them for determination on electrically same phases. Challenging issue is to achieve synchronization without GPS providing synchronizing time. A novel power transformer and wire identification system is designed and implemented. The system consists of a transmitter and a receiver with power-line communication module. Some experiments are conducted to verify the theoretical concepts in a big commercial building. Also some simulations are done to help and understand the concepts by using MATLAB Simulink simulator.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.103-111
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• 2023

Consensus, that aims to converge the states of agents to the same states through information exchanges between agents, has been widely studied to control the multi-agent systems. In real systems, the measurement variables of each agent may be different, the loss of information across communication may occur, and the different networks for each state may need to be constructed for safety. Moreover, the input saturation and the disturbances in the system may cause instability. Therefore, this paper studies the PID(Proportional-Integral-Derivative)-based consensus control to achieve the swarm behavior of the multi-agent systems considering the heterogeneous state information, the input saturations, and the disturbances. Specifically, we consider the multiple follower agents and the single leader agent modeled by the second-order systems, and investigate the conditions to achieve the consensus based on the stability of the error system. It is confirmed that the proposed algorithm can achieve the consensus if only the connectivity of the position graph is guaranteed. Moreover, by extending the consensus algorithm, we study the formation control problem for the multi-agent systems. Finally, the validity of the proposed algorithm was verified through the simulations.