• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.489-499
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• 2012

In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.

• Convergence Security Journal
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• v.23 no.1
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• pp.27-33
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• 2023

Digitalization due to COVID-19 has rapidly developed artificial intelligence-based voice recognition technology. However, this technology causes unfair social problems, such as race and gender discrimination if datasets are biased against some groups, and degrades the reliability and security of artificial intelligence services. In this work, we compare and analyze accuracy-based unfairness in biased data environments using VGGNet (Visual Geometry Group Network), ResNet (Residual Neural Network), and MobileNet, which are representative CNN (Convolutional Neural Network) models of artificial intelligence. Experimental results show that ResNet34 showed the highest accuracy for women and men at 91% and 89.9%in Top1-accuracy, while ResNet18 showed the slightest accuracy difference between genders at 1.8%. The difference in accuracy between genders by model causes differences in service quality and unfair results between men and women when using the service.

• Korean Journal of Food Science and Technology
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• v.49 no.4
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• pp.349-354
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• 2017

The geographical origins of raw ginseng (RG) were discriminated using an electronic tongue. Taste screening, DFA (discriminant function analysis), and CDA (canonical discriminant analysis) were used to statistically analyze the data. The taste profile patterns of umami, bitterness, and sweetness of the Korean RG was different from those of the Chinese RG. The Korean RG was stronger than the Chinese RG regarding the taste of umami. DFA discriminated the geographical origins of 154 samples, with a few overlapping samples, between the Korean and Chinese RG. CDA showed that the accuracy of origin discrimination for the Korean and Chinese RGs were 87.01 and 94.81%, respectively. The final accuracy of origin discrimination was 90.91%. The distance between the centroids of each group was 2.7463. Thus, the electronic tongue analysis can be used to efficiently differentiate the geographical origins of RG.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.619-630
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• 2011

In order to estimate an appropriate threshold and evaluate its performance for the data mixed with two different distributions, nine kinds of well-known classification accuracy measures such as MVD, Youden's index, the closest-to- (0,1) criterion, the amended closest-to- (0,1) criterion, SSS, symmetry point, accuracy area, TA, TR are clustered into five categories on the basis of their characters. In credit evaluation study, it is assumed that the score random variable follows normal mixture distributions of the default and non-default states. For various normal mixtures, optimal cut-off points for classification measures belong to each category are obtained and type I and II error rates corresponding to these cut-off points are calculated. Then we explore the cases when these error rates are minimized. If normal mixtures might be estimated for these kinds of real data, we could make use of results of this study to select the best classification accuracy measure which has the minimum error rate.

• Journal of Distribution Science
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• v.12 no.11
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• pp.77-83
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• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• The Journal of Korean Physical Therapy
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• v.16 no.4
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• pp.218-225
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• 2004

OBJECTIVES: This study was performed to find out static two-point discrimination (TPD) in fingertips. METHODS: This was a cross-section, measure study of static two-point discrimination involving healthy young adults. Measure was completed by 48 college students in Andong Science College from June 1 to 12, 2004. The minimal distance at which two-points could be discriminated was measured from thumb to little finger. RESULTS: For dermatomal regions of the fingertip, mean values ranged from 3.3mm to 4.9mm (thumb 3.6mm, index finger 3.3mm, middle finger 3.8mm, ring finger 4.2mm, and little finger 4.7mm in the left hand; thumb 3.7mm, index 3.5mm, middle 4.0mm, ring 4.3mm, and little 4.9mm in the right hand). A significant difference in discrimination ability was found between men and women, 3.5mm for women showed a greater sensitivity than 4.1mm for men in the left middle fingertip(p=0.0109), also 3.9mm for women showed a greater accuracy than 4.5mm for men in the left ring fingertip(p=0.0388). In the right index fingertip, women (3.1mm) have a narrow distance than men (3.6mm)(p=0.0329). The minimal distance of TPD was found a significant difference between 20 and 30 years in age. 4mm for 30 years showed a greater distance than 3.5mm for 20 years in the left thumb fingertip(p=0.0354), also, 3.8mm for 30 years showed a greater distance than 3.2mm for 20 years in the left index fingertip(p=0.0174), and 4.3mm for 30 years showed a greater distance than 3.7mm for 20 years in the left middle fingertip(p=0.0444). In the right index fingertip, 20 years (3.2mm) had also a narrow distance than 30 years (4.1mm)(p=0.0020), 20 years (3.9mm) showed a narrow distance than 30 years (4.6mm) in the right middle fingertip(p=0.0124), and 20 years (4.1mm) showed a greater sensitivity than 30 years (5.0mm) in the right ring fingertip(p=0.0070). CONCLUSIONS: Our results suggest that distance of TPD in the both index fingertips for 20 years women was significantly narrowed.

• Korean Journal of Food Science and Technology
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• v.40 no.2
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• pp.135-140
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• 2008

In this study, the macromineral content ratios of four herbal medicine samples(Saposhnikoviae Radix, Bupleuri Radix, Cnidii Rhizoma, and Astragali Radix) were analyzed to discriminate their geographical origins using an energydispersive x-ray fluorescence (EDXRF) technique. EDXRF is a rapid, non-destructive, and multi-elemental analysis technique. Initially, samples of both domestic and imported herbal medicines were pulverized, and then their macromineral contents, including P, S, K, and Ca, were analyzed using EDXRF. For the discrimination of their geographical origins, canonical discriminant analysis was carried out based on the estimated macromineral relative content ratios of the samples. According to the results, the discrimination accuracies were as follows: 93.3% for Saposhnikoviae Radix, 95.7% for Bupleuri Radix, 98.8% for Cnidii Rhizoma, and 87.5% for Astragali Radix. Overall, the results imply that this technique could be used as a standard method, to discriminate their geographical origins between domestic and imported herbal medicines.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.