• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.280-290
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• 2002

Three-dimensional (3-D) electromagnetic (EM) modeling algorithm has been developed using finite element method (FEM) to acquire more efficient interpretation techniques of EM data. When FEM based on nodal elements is applied to EM problem, spurious solutions, so called 'vector parasite', are occurred due to the discontinuity of normal electric fields and may lead the completely erroneous results. Among the methods curing the spurious problem, this study adopts vector element of which basis function has the amplitude and direction. To reduce computational cost and required core memory, complex bi-conjugate gradient (CBCG) method is applied to solving complex symmetric matrix of FEM and point Jacobi method is used to accelerate convergence rate. To verify the developed 3-D EM modeling algorithm, its electric and magnetic field for a layered-earth model are compared with those of layered-earth solution. As we expected, the vector based FEM developed in this study does not cause ny vector parasite problem, while conventional nodal based FEM causes lots of errors due to the discontinuity of field variables. For testing the applicability to high frequencies 100 MHz is used as an operating frequency for the layer structure. Modeled fields calculated from developed code are also well matched with the layered-earth ones for a model with dielectric anomaly as well as conductive anomaly. In a vertical electric dipole source case, however, the discontinuity of field variables causes the conventional nodal based FEM to include a lot of errors due to the vector parasite. Even for the case, the vector based FEM gave almost the same results as the layered-earth solution. The magnetic fields induced by a dielectric anomaly at high frequencies show unique behaviors different from those by a conductive anomaly. Since our 3-D EM modeling code can reflect the effect from a dielectric anomaly as well as a conductive anomaly, it may be a groundwork not only to apply high frequency EM method to the field survey but also to analyze the fold data obtained by high frequency EM method.

• Korean Journal of Ecology and Environment
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• v.48 no.3
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• pp.147-152
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• 2015

Nitrogen (N) loading from domestic, agricultural and industrial sources can lead to excessive growth of macrophytes or phytoplankton in aquatic environment. Many studies have used nitrogen stable isotope ratios to identify anthropogenic nitrogen in aquatic systems as a useful method for studying nitrogen cycle. In this study to evaluate the precision and accuracy of Kjeldahl processes, two reference materials (IAEA-NO-3, N-1) were analyzed repeatedly. Measured the ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ values of IAEA-NO-3 and IAEA-N-1 were $4.7{\pm}0.2$‰ and $0.4{\pm}0.3$‰, respectively, which are within recommended values of analytical uncertainties. Also, we investigated spatial patterns of ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ in effluent plumes from a waste water treatment plant in Han River, Korea. ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ values are enriched at downstream areas of water treatment plant suggesting that dissolved nitrogen in effluent plumes should be one of the main N sources in those areas. The current study clarifies the reliability of Kjeldahl analytical method and the usefulness of stable isotopic techniques to trace the contamination source of dissolved nitrogen such as nitrate and ammonia.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.175-183
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• 2021

Gongjae Yoon DuSeo(1668~1715), from Haenam in the late Joseon Dynasty, is a scholar-born painter who was active during King Sukjong. He is the person who created the foundation as a pioneer of realist paintings in the late Joseon period during the transition from the middle to the latter period. He was born in Namin's prestigious family, but he ended his career as part of a partisan fight and immersed himself in painting and learning. 18C, the beginning of the late Joseon Dynasty, was a period when Silhak emerged and the Jinkyung era opened with awareness of nationalism. At this time, by incorporating the Silhak thought into the art world, the real reformed aesthetic consciousness was demonstrated to pioneer common people's customs, the application of Western painting methods, the pursuit of realist techniques, and the introduction of Namjongmuninhwa. His view of painting, who thoroughly learned the old things and pursued change, must have both the form and spirit that he can achieve 'HwaDo' only when it has the science of 'learning and knowledge' and the technical elements of 'practice and quality' emphasized. He has worked in a variety of reconciliations. In particular, portrait paintings are characterized by ihyeongsasin's realistic expressions of aesthetics. His masterpiece, 「Self-portrait」, excels in extreme-realistic depiction and innovation in composition, and stands out with an unconventional experimentation spirit that expresses his mind and thoughts in a painting with a sense of resentment. His landscape paintings combine to express the form as it is and mental notions, and beautifully embodied Do as a form, thus achieving ihyeongmido, which reached the level of'joyfulness forgotten even the heart of joy'. On the other hand, the generalization of the common people using various common people's lives as the subject of an open-mindedness aimed at gaining the facts of ihyeongsajin, a passive protest against corrupt power and an expression of a spirit of love. Since then, his painting style has been passed down from generation to generation to his eldest son Yoon Deok-hee and his grandson Yoon Yong, leading the change and revival of calligraphy art in the late Joseon Dynasty.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• MISULJARYO - National Museum of Korea Art Journal
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• v.97
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• pp.14-54
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• 2020

The overall study of Samsaebulhoedo (painting of the Assembly of Buddhas of Three Ages) at Yongjusa Temple has focused on dating it, analyzing the painting style, identifying its painter, and scrutinizing the related documents. However, its greater coherence could be achieved through additional support from empirical evidence and logical consistency. Recent studies on Samsaebulhoedo at Yongjusa Temple that postulate that the painting could have been produced by a monk-painter in the late nineteenth century and that an original version produced in 1790 could have been retouched by a painter in the 1920s using a Western painting style lack such empirical proof and logic. Although King Jeongjo's son was not yet installed as crown prince, the Samsaebulhoedo at Yongjusa Temple contained a conventional written prayer wishing for a long life for the king, queen, and crown prince: "May his majesty the King live long / May her majesty the Queen live long / May his highness the Crown Prince live long" (主上殿下壽萬歲, 王妃殿下壽萬歲, 世子邸下壽萬歲). Later, this phrase was erased using cinnabar and revised to include unusual content in an exceptional order: "May his majesty the King live long / May his highness the King's Affectionate Mother (Jagung) live long / May her majesty the Queen live long / May his highness the Crown Prince live long" (主上殿下壽萬歲, 慈宮邸下壽萬歲, 王妃殿下壽萬歲, 世子邸下壽萬歲). A comprehensive comparison of the formats and contents in written prayers found on late Joseon Buddhist paintings and a careful analysis of royal liturgy during the reign of King Jeongjo reveal Samsaebulhoedo at Yongjusa Temple to be an original version produced at the time of the founding of Yongjusa Temple in 1790. According to a comparative analysis of formats, iconography, styles, aesthetic sensibilities, and techniques found in Buddhist paintings and paintings by Joseon court painters from the eighteenth and nineteenth centuries, Samsaebulhoedo at Yongjusa Temple bears features characteristic of paintings produced around 1790, which corresponds to the result of analysis on the written prayer. Buddhist paintings created up to the early eighteenth century show deities with their sizes determined by their religious status and a two-dimensional conceptual composition based on the traditional perspective of depicting close objects in the lower section and distant objects above. This Samsaebulhoedo, however, systematically places the Buddhist deities within a threedimensional space constructed by applying a linear perspective. Through the extensive employment of chiaroscuro as found in Western painting, it expresses white highlights and shadows, evoking a feeling that the magnificent world of the Buddhas of the Three Ages actually unfolds in front of viewers. Since the inner order of a linear perspective and the outer illusion of chiaroscuro shading are intimately related to each other, it is difficult to believe that the white highlights were a later addition. Moreover, the creative convergence of highly-developed Western painting style and techniques that is on display in this Samsaebulhoedo could only have been achieved by late-Joseon court painters working during the reign of King Jeongjo, including Kim Hongdo, Yi Myeong-gi, and Kim Deuksin. Deungun, the head monk of Yongjusa Temple, wrote Yongjusa sajeok (History of Yongjusa Temple) by compiling the historical records on the temple that had been transmitted since its founding. In Yongjusa sajeok, Deungun recorded that Kim Hongdo painted Samsaebulhoedo as if it were a historical fact. The Joseon royal court's official records, Ilseongnok (Daily Records of the Royal Court and Important Officials) and Suwonbu jiryeong deungnok (Suwon Construction Records), indicate that Kim Hongdo, Yi Myeong-gi, and Kim Deuksin all served as a supervisor (gamdong) for the production of Buddhist paintings. Since within Joseon's hierarchical administrative system it was considered improper to allow court painters of government position to create Buddhist paintings which had previously been produced by monk-painters, they were appointed as gamdong in name only to avoid a political liability. In reality, court painters were ordered to create Buddhist paintings. During their reigns, King Yeongjo and King Jeongjo summoned the literati painters Jo Yeongseok and Kang Sehwang to serve as gamdong for the production of royal portraits and requested that they paint these portraits as well. Thus, the boundary between the concept of supervision and that of painting occasionally blurred. Supervision did not completely preclude painting, and a gamdong could also serve as a painter. In this light, the historical records in Yongjusa sajeok are not inconsistent with those in Ilseongnok, Suwonbu jiryeong deungnok, and a prayer written by Hwang Deok-sun, which was found inside the canopy in Daeungjeon Hall at Yongjusa Temple. These records provided the same content in different forms as required for their purposes and according to the context. This approach to the Samsaebulhoedo at Yongjusa Temple will lead to a more coherent explanation of dating the painting, analyzing its style, identifying its painter, and interpreting the relevant documents based on empirical grounds and logical consistency.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.174-189
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• 2015

The objective of this study was to reconstruct spatial information using the results of the investigation and evaluation of the health of the living organisms, habitat, and water quality at the investigation points for the aquatic ecosystem health of the Nakdong River basin, to support the rational decision making of the aquatic ecosystem preservation and restoration policies of the Nakdong River basin using spatial analysis techniques, and to present efficient management methods. To analyze the aquatic ecosystem health of the Nakdong River basin, punctiform data were constructed based on the position information of each point with the aquatic ecosystem health investigation and evaluation results of 250 investigation sections. To apply the spatial analysis technique, the data need to be reconstructed into areal data. For this purpose, spatial influence and trends were analyzed using the Kriging interpolation(ArcGIS 10.1, Geostatistical Analysis), and were reconstructed into areal data. To analyze the spatial distribution characteristics of the Nakdong River basin health based on these analytical results, hotspot(Getis-Ord Gi, $G^*_i$), LISA(Local Indicator of Spatial Association), and standard deviational ellipse analyses were used. The hotspot analysis results showed that the hotspot basins of the biotic indices(TDI, BMI, FAI) were the Andong Dam upstream, Wangpicheon, and the Imha Dam basin, and that the health grades of their biotic indices were good. The coldspot basins were Nakdong River Namhae, the Nakdong River mouth, and the Suyeong River basin. The LISA analysis results showed that the exceptional areas were Gahwacheon, the Hapcheon Dam, and the Yeong River upstream basin. These areas had high bio-health indices, but their surrounding basins were low and required management for aquatic ecosystem health. The hotspot basins of the physicochemical factor(BOD) were the Nakdong River downstream basin, Suyeong River, Hoeya River, and the Nakdong River Namhae basin, whereas the coldspot basins were the upstream basins of the Nakdong River tributaries, including Andong Dam, Imha Dam, and Yeong River. The hotspots of the habitat and riverside environment factor(HRI) were different from the hotspots and coldspots of each factor in the LISA analysis results. In general, the habitat and riverside environment of the Nakdong River mainstream and tributaries, including the Nakdong river upstream, Andong Dam, Imha Dam, and the Hapcheon Dam basin, had good health. The coldspot basins of the habitat and riverside environment also showed low health indices of the biotic indices and physicochemical factors, thus requiring management of the habitat and riverside environment. As a result of the time-series analysis with a standard deviation ellipsoid, the areas with good aquatic ecosystem health of the organisms, habitat, and riverside environment showed a tendency to move northward, and the BOD results showed different directions and concentrations by the year of investigation. These aquatic ecosystem health analysis results can provide not only the health management information for each investigation spot but also information for managing the aquatic ecosystem in the catchment unit for the working research staff as well as for the water environment researchers in the future, based on spatial information.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.