• Asia-Pacific Journal of Business
• /
• v.11 no.2
• /
• pp.207-233
• /
• 2020

Purpose - The purpose of this paper analyzes the relationship between the Corporate Social Responsibility(CSR) and Corporate Value to estimate whether the characteristics of Firm can change this relationship. Design/methodology/approach - This paper utilizes the total 776 firms' data over the period 2014-2018, and develops a new ESG index which was estimated by PCA. Findings - First, the estimated ESG index implies that Large company has the highest value of CSR, while Medium sized and Small company have the relatively low one. And comparing to the case of 2014, the trend of ESG index in Large company does not decrease in 2018. Second, there is a clear and significant non linear relationship between CSR and corporate value, it implies that the U-shaped exists in the Korean Firms. Such a tendency is mush stronger in the Large company. Third, the new ESG index indicates that it takes more time to increase Firm value in the Medium sized and Small company while there is a high possibility of increasing value in Large company from the little gab between the threshold points and mean value of ESG. Research implications or Originality - The non linear tendency between the Corporate Social Responsibility and Corporate Value is strongly affected by Firm size and the relative high quintile of ESG, but it is less affected by Firm history.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.255-263
• /
• 2022

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

• The Korean Journal of Food And Nutrition
• /
• v.36 no.4
• /
• pp.274-285
• /
• 2023

This study was conducted to select representative agricultural products (4 types of fruits and 4 types of wild vegetables) in Chungju, define their sensual characteristics, derive suitable flavour-pairing and recipes for each ingredient, and use them as a cornerstone in the development of menus. For the experiment, 10 experts were selected to choose 8 representative agricultural products in Chungju, and 18 menus were selected through a flavour-pairing survey. A consumer panel (a total of 413 people, 105 in their 20s, 103 in their 30s, 103 in their 40s, and 102 in their 50s) for evaluating the characteristics of consumer preferences was selected. After the flavour-pairing survey 'sweet taste', 'light flavour', 'soft flavour', 'savoury flavour', 'familiar flavour', 'harmonious flavour', 'softness', and 'harmoniousness with food ingredients' were determined as drivers of liking, on the other hand, 'disturbance with food ingredients' and 'soybean fishy smell' were determined as drivers of disliking. The degree of consumer preference and overall acceptance were found to be related to the consumers' familiarity, suggesting that if a menu should be developed using unfamiliar local agricultural products, it should be configured with familiar recipes and seasoning methods.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.41.1-41.1
• /
• 2020

Different molecular lines trace different physical environments (with various densities and temperatures) within molecular clouds (MCs). Therefore, multimolecular line observations are crucial to study the physical and chemical structures of MCs. We observed the Orion A and Ophiuchus clouds in six different molecular lines as a Taeduk Radio Astronomy Observatory Key Science Program (TRAO-KSP), "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale" (TIMES; PI: Jeong-Eun Lee). Here, we investigate the characteristic relations between the observed lines by performing the Principal Component Analysis (PCA). We also investigate the correlation between the line intensity distributions and the physical parameters, such as the gas column density and dust temperature. Finally, we will discuss how the correlations among different chemical tracers vary with the star formation environments.

• International Journal of Computer Science & Network Security
• /
• v.24 no.4
• /
• pp.179-191
• /
• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Asian-Australasian Journal of Animal Sciences
• /
• v.24 no.4
• /
• pp.449-456
• /
• 2011

Eighteen different biometric traits in 407 Kankrej cows from their breeding zone, i.e. Palanpur district of Gujarat, India, were recorded and analyzed by factor analysis to explain body conformation. The averages of body length, height at withers, height at shoulder, height at knee, heart girth, paunch girth, face length, face width, horn length, horn diameter, distance between horns, ear length, ear width, neck length, neck diameter, tail length with switch, tail length without switch and distance between hip bones were $123.44{\pm}0.37$, $124.49{\pm}0.28$, $94.68{\pm}0.30$, $38.2{\pm}0.14$, $162.56{\pm}0.56$, $178.95{\pm}0.70$, $44.09{\pm}0.10$, $15.91{\pm}0.05$, $42.47{\pm}0.53$, $26.07{\pm}0.19$, $13.34{\pm}0.08$, $31.24{\pm}0.12$, $16.10{\pm}0.05$, $50.63{\pm}0.18$, $73.21{\pm}0.32$, $111.62{\pm}0.53$, $89.34{\pm}0.34$ and $17.28{\pm}0.10\;cm$, respectively. The correlation coefficients between different traits ranged from -0.806 (horn diameter and distance between horns) to 0.815 (heart girth and paunch girth). Most of the correlations were positive and significant. Factor analysis with promax rotation with power 3 revealed three factors which explained about 66.02% of the total variation. Factor 1 described the cow body and explained 38.89% of total variation. The second factor described the front view/face of the cow and explained 19.68% of total variation. The third factor described the back of the cow and explained 7.44% of total variation. It was necessary to include some more variables for factor 3 to obtain a reliable estimate of the back view of the cow. The lower communities shown for distance between horns, horn diameter, ear width and neck diameter indicated that these traits did not contribute effectively to explaining body conformation and can be dropped from recording, whereas all other traits are important and needed to explain body conformation in Kankrej cows. The result suggests that principal component analysis (PCA) could be used in breeding programs with a drastic reduction in the number of biometric traits to be recorded to explain body conformation.

• Journal of Biomedical and Translational Research
• /
• v.19 no.4
• /
• pp.116-124
• /
• 2018

Korean Native Pig (KNP) has a uniform black coat color, excellent meat quality, white colored fat, solid fat structure and good marbling. However, its growth performance is low, while the western origin Yorkshire pig has high growth performance. To take advantage of the unique performance of the two pig breeds, we raised crossbreeds (KNP ${\times}$ Yorkshire to make use of the heterotic effect. We then analyzed the liver transcriptome as it plays an important role in fat metabolism. We sampled at two stages: 10 weeks and at 26 weeks. The stages were chosen to correspond to the change in feeding system. A total of 16 pigs (8 from each stage) were sampled and RNA sequencing was performed. The reads were mapped to the reference genome and differential expression analysis was performed with edgeR package. A total of 324 genes were found to be significantly differentially expressed (${\left|log2FC\right|}$ > 1 & q < 0.01), out of which 180 genes were up-regulated and 144 genes were down-regulated. Principal Component Analysis (PCA) showed that the samples clustered according to stages. Functional annotation of significant DEGs (differentially expressed genes) showed that GO terms such as DNA replication, cell division, protein phosphorylation, regulation of signal transduction by p53 class mediator, ribosome, focal adhesion, DNA helicase activity, protein kinase activity etc. were enriched. KEGG pathway analysis showed that the DEGs functioned in cell cycle, Ras signaling pathway, p53 signaling pathway, MAPK signaling pathway etc. Twenty-nine transcripts were also part of the DEGs, these were predominantly Cys2His2-like fold group (C2H2) family of zinc fingers. A protein-protein interaction (PPI) network analysis showed that there were three highly interconnected clusters, suggesting an enrichment of genes with similar biological function. This study presents the first report of liver tissue specific gene regulation in a cross-bred Korean pig.

• Journal of Animal Science and Technology
• /
• v.58 no.9
• /
• pp.35.1-35.10
• /
• 2016

Background: The aim of this work was the comparison between the carcass and the meat ewes of the regional Traditional market and the Islamic religious (Halal) market. Methods: Thirty and 20 at the end of career traditional market and Halal market ewes were slaughtered following the EC (European Council, 2009) animal welfare guidelines. Live weight of ewes was taken and dressing percentage of carcasses was calculated. On every carcass zoometric measurement and the evaluation trough the EU grid rules were performed. On the Musculus longissimus thoracis of 12 Traditional market carcasses and 11 Halal market carcasses the physical-chemical and nutritional analysis were performed. Consumer tests for liking meat ewe were performed in order to find consumer's preference level for Traditional and Halal markets ewe meat. Considering as fixed factor the ewe meat market (Traditional and Halal), results were submitted to oneway Analysis of Variance (ANOVA) and to Principal Component Analysis (PCA). Results: The Halal market ewes have shown lower dressing percentages ($42.91{\pm}0.82$ vs $46.42{\pm}0.69$) and lower conformation score ($4.5{\pm}0.5$ vs $7.8{\pm}0.4$). The Halal market meat showed higher cooking loss in oven ($37.83{\pm}1.20$ vs $32.03{\pm}1.15%$), lesser Chroma value ($18.63{\pm}0.70$ vs $21.84{\pm}0.67$), and lesser Hue angle value ($0.26{\pm}0.02$ vs $0.34{\pm}0.02$). This product had also lower fat percentage ($4.2{\pm}0.4$ vs $7.09{\pm}0.4$). The traditional market meat had higher percentage in monounsatured fatty acids (MUFA) ($43.84{\pm}1.05$ vs $38.22{\pm}1.10$), while the Halal market meat had higher percentage in ${\omega}3$ poliunsatured fatty acids (PUFA) ($5.04{\pm}0.42$ vs $3.60{\pm}0.40$). The consumer test showed as the ewe meat was appreciate by the consumers. Conclusions: Both meat typologies have shown good nutritional characteristics. The traditional market meat had higher MUFA composition, and a better MUFA/satured fatty acids (SFA) ratio, while the Halal market meat had higher PUFA composition. These results were also supported by the PCA. The consumers preferred the traditional market meat.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.27-36
• /
• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.