• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.89-98
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• 2023

Background: Organophosphorus flame retardants (OPFRs) are a group of chemical substances used in building materials and plastic products to suppress or mitigate the combustion of materials. Although OPFRs are generally used in mixed form, information on their mixture toxicity is quite scarce. Objectives: This study aims to elucidate the toxicity and determine the types of interaction (e.g., synergistic, additive, and antagonistic effect) of OPFRs mixtures. Methods: Nine organophosphorus flame retardants, including TEHP (tris(2-ethylhexyl) phosphate) and TDCPP (tris(1,3-dichloro-2-propyl) phosphate), were selected based on indoor dust measurement data in South Korea. Nine OPFRs were exposed to the luminescent bacteria Aliivibrio fischeri for 30 minutes and the human hepatocyte cell line HepG2 for 48 hours. Chemicals with significant toxicity were only used for mixture toxicity tests in HepG2. In addition, the observed EC_x values were compared with the predicted toxicity values in the CA (concentration addition) prediction model, and the MDR (model deviation ratio) was calculated to determine the type of interaction. Results: Only four chemicals showed significant toxicity in the luminescent bacteria assays. However, EC₅₀ values were derived for seven out of nine OPFRs in the HepG2 assays. In the HepG2 assays, the highest to lowest EC₅₀ were in the order of the molecular weight of the target chemicals. In the further mixture tests, most binary mixtures show additive interactions except for the two combinations that have TPhP (triphenyl phosphate), i.e., TPhP and TDCPP, and TPhP and TBOEP (tris(2-butoxyethyl) phosphate). Conclusions: Our data shows OPFR mixtures usually have additivity; however, more research is needed to find out the reason for the synergistic effect of TPhP. Also, the mixture experimental dataset can be used as a training and validation set for developing the mixture toxicity prediction model as a further step.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.67-85
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• 2023

Purpose - This study seeks to investigate the determinants of global sourcing intention in clothing sewing industry, in particular with its focus on women's knit wear production. Design/methodology/approach - This study collected a unique set of qualitative data through 31 in-depth interviews with fashion brands, promotion agencies, and sewing factories between July 2023 and October 2023. In addition, it analyzed the dataset using the MAXQDA to complement the research findings. Findings - We have two findings. First, the interviewees commonly mentioned the following factors as reasons for considering global sourcing: the human factors(aging of skilled technicians and labor shortages), the financial factors(gap in production unit prices at home and abroad), the relational factors(lack of novelty), and the physical factors(loss of production infrastructure and network), while the human factors(skilled workforce), the production factors(delivery date and product quality), and the relational factors(timely communication and mutual trust) as reasons for continuing domestic sourcing. Additional code analysis of interview also supports this finding. On the other hand, there was also a subtle difference between buyers(brands) and suppliers(promotion agencies and processing plants), and buyers consider the exact delivery date critical so that they could see trend-sensitive women's knit wear on time, and suppliers took production costs, labor costs, and labor shortages, which are financial factors, more seriously. Research implications or Originality - This study provides a richer and more balanced view of existing literature, which has generally tended to introduce global sourcing across the clothing industry despite the existence of various diversity within the industry. In addition, through qualitative research, we introduce that the sewing industry is carried out according to complex factors, and by revealing and categorizing the determinants of global sourcing, we supplement the existing research on the clothing sewing industry centered on survey. On a practical note, this study introduces that there is a difference in view of domestic sourcing and global sourcing between buyers(brands) and suppliers(promotion agencies and sewing factories), suggesting practical implications for revitalizing networks and deriving win-win cooperation network models among members in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.518-528
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• 2014

In this paper, we propose a method to recognize the action direction of human by developing 4D space-time (4D-ST, [x,y,z,t]) features. For this, we propose 4D space-time interest points (4D-STIPs, [x,y,z,t]) which are extracted using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the proposed features are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding image planes in training step. We can recognize the directions of actors in the test video since our training sets, which are projections of 3D-S volumes and 4D-STIPs to various image planes, contain the direction information. The process for recognizing action direction is divided into two steps, firstly we recognize the class of actions and then recognize the action direction using direction information. For the action and direction of action recognition, with the projected 3D-S volumes and 4D-STIPs we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. For the action recognition, features are trained by support vector data description (SVDD) according to the action class and recognized by support vector domain density description (SVDDD). For the action direction recognition after recognizing actions, each actions are trained using SVDD according to the direction class and then recognized by SVDDD. In experiments, we train the models using 3D-S volumes from INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset and recognize action direction by constructing a new SNU dataset made for evaluating the action direction recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• Korean Journal of Social Welfare Studies
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• no.36
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• pp.175-202
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• 2008

This paper analyzes the relationship between social assistance and poverty in Korea. Social assistance in Korea which had a Poor Law tradition for about forty years, was finally reformed and a new general assistance scheme designed to protect fundamental human rights, was established in 1999. Are the means-tested social assistance benefits effective in protecting the poor from hardship? How effectively do the means-tested benefits reduce poverty? To assess the effects of the means-tested social assistance benefits in Korea on poverty reduction, the study uses household data from the National Basic Livelihood Security Programme Review Board (NRB). Moreover, to obtain a comparative perspective, a British dataset the Family Resources Survey (FRS) is assessed. The incidence and the intensity of poverty for a range of household types are analyzed before and after social assistance benefits for both countries. The results of the analysis show the Korean social assistance benefits do not radically alleviate poverty, although recipients' income positions are improved after social assistance transfers. Compared to Korea, Britain achieved marked success of means-tested social assistance benefits with a high degree of effectiveness, especially among the extreme and the severe poverty brackets, while there are variations between different household types regarding the incidence and the intensity of poverty before and after transfer.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1195-1210
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• 2023

Coastal debris has emerged as a salient issue due to its adverse effects on coastal aesthetics, ecological systems, and human health. In pursuit of effective countermeasures, the present study delineated the construction of a specialized image dataset for coastal debris detection and embarked on a comparative analysis between two paramount real-time object detection algorithms, YOLOv8 and RT-DETR. Rigorous assessments of robustness under multifarious conditions were instituted, subjecting the models to assorted distortion paradigms. YOLOv8 manifested a detection accuracy with a mean Average Precision (mAP) value ranging from 0.927 to 0.945 and an operational speed between 65 and 135 Frames Per Second (FPS). Conversely, RT-DETR yielded an mAP value bracket of 0.917 to 0.918 with a detection velocity spanning 40 to 53 FPS. While RT-DETR exhibited enhanced robustness against color distortions, YOLOv8 surpassed resilience under other evaluative criteria. The implications derived from this investigation are poised to furnish pivotal directives for algorithmic selection in the practical deployment of marine debris monitoring systems.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.5
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• pp.29-41
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• 2014

Ecosystem services provide various benefits to human beings, but are considered to be free of cost. To protect ecosystems in an economically sustainable way, several developing countries have adopted a policy known as the Payment for Ecosystem Services (PES) that compensates upstream services with monetary incentives collected from service users. Vietnam is one of the countries that have enacted a nationwide PES policy. However, the policy in Vietnam requires further development in order to evaluate the spatial priority zones based on the quantification of ecosystem services. To obtain a recent and high-quality land cover map, we first classified the land cover in the Da River basin, in northern Vietnam, using Landsat dataset. We then applied a water balance theory and an USLE equation to assess hydrological ecosystem services concerning water supply and sediment retention. Following the assessment, we identified the priority areas for hydrological ecosystem services exclusively for forest environments. We found that the quantity and distribution of services from forests varied, due to the topography, climate, and land cover. According to a quantile distribution, Mt. Phu Luong, Mt. Fansipan, and Hoang Lien National Park were evaluated as high service areas in terms of both water yield and sediment retention. As a result, this assessment method can help construct spatial priority zones concerning ecosystem service distribution, and can also contribute to benefit sharing by indicating which forest and landowners require compensation.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.21-32
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• 2015

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D xy spatial planes of traditional frames, but from the 2D time axis called spatio-temporal frame as well. Such spatial-temporal features are able to capture dynamic information from the action videos and are well-suited to recognize human actions without need of 3D extensions for the feature descriptors. The spatio-temporal BoF approach using SIFT and SURF feature descriptors obtains good recognition rates on a well-known actions recognition dataset. Compared with more sophisticated scheme of 3D based HoG/HoF descriptors, proposed method is easier to compute and simpler to understand.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.207-215
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• 2003

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated stuructures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. We have developed EGSP, a eukaryotic gene structure program, using duration hidden markov model. The program consists of two major processes, one of which is a training process to produce parameter values from training data sets and the other of which is to predict protein coding regions based on the parameter values. The program predicts multiple genes rather than a single gene from a DNA sequence. A few computational models were implemented to detect signal pattern and their scanning efficiency was tested. Prediction performance was calculated and was compared with those of a few commonly used programs, GenScan, GeneID and Morgan based on a few criteria. The results show that the program can be practically used as a stand-alone program and a module in a system. For gene prediction of eukaryotic microbial genomes, training and prediction analysis was done with Saccharomyces chromosomes and the result shows the program is currently practically applicable to real eukaryotic microbial genomes.