• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.835-840
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• 2023

RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.1
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• pp.35-45
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• 2020

This study focused on the park as a space to support the behavior of urban citizens in modern society. Modern city parks are not spaces that play a specific role but are used by many people, so their function and meaning may change depending on the user's behavior. In addition, current online data may determine the selection of parks to visit or the usage of parks. Therefore, this study analyzed the change of behavior in Yeouido Park, Yeouido Hangang Park, and Yangjae Citizen's Forest from 2000 to 2018 by utilizing a time series analysis. The analysis method used Big Data techniques such as text mining and social network analysis. The summary of the study is as follows. The usage behavior of Yeouido Park has changed over time to "Ride" (Dynamic Behavior) for the first period (I), "Take" (Information Communication Service Behavior) for the second period (II), "See" (Communicative Behavior) for the third period (III), and "Eat" (Energy Source Behavior) for the fourth period (IV). In the case of Yangjae Citizens' Forest, the usage behavior has changed over time to "Walk" (Dynamic Behavior) for the first, second, and third periods (I), (II), (III) and "Play" (Dynamic Behavior) for the fourth period (IV). Looking at the factors affecting behavior, Yeouido Park was had various factors related to sports, leisure, culture, art, and spare time compared to Yangjae Citizens' Forest. The differences in Yangjae Citizens' Forest that affected its main usage behavior were various elements of natural resources. Second, the behavior of the target areas was found to be focused on certain main behaviors over time and played a role in selecting or limiting future behaviors. These results indicate that the space and facilities of the target areas had not been utilized evenly, as various behaviors have not occurred, however, a certain main behavior has appeared in the target areas. This study has great significance in that it analyzes the usage of urban parks using Big Data techniques, and determined that urban parks are transformed into play spaces where consumption progressed beyond the role of rest and walking. The behavior occurring in modern urban parks is changing in quantity and content. Therefore, through various types of discussions based on the results of the behavior collected through Big Data, we can better understand how citizens are using city parks. This study found that the behavior associated with static behavior in both parks had a great impact on other behaviors.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.237-251
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• 2020

This study examined structural analysis of supports in tunnel and displacement and underground stress of tunnel by measurement, in order to evaluate the performance of high-strength lattice girders developed as a substitute for H-profiles. According to the three-dimensional nonlinear structural analysis results of the tunnel support, the load and displacement relationship between the H-profiles and the high-strength lattice girders showed almost the same behavior, and the maximum load of the high-strength lattice girders were 1.0 to 1.2 times greater than the H-profiles. By the results of the three-dimensional tunnel cross-section analysis of the supports, the axial force was occurred largely in the lower left and right sides of the tunnel, and showed a similar trend to the field test values. In the results of the measurement of the roof settlement and rod extension, the final displacement of the steel arch rib (H-profile) and high-strength lattice girder section in tunnel was converged to a constant value without significant difference within the first management standard of 23.5 mm. According to the results of underground displacement measurement, the final change amount of the two support sections showed a slight displacement change, but converged to a constant value within the first management standard of 10 mm. By the results of measurement of shotcrete stress and steel arch rib stress, the final change amount of the two support sections showed a slight stress change, but converged to a constant value within the first management standard of 81.1 kg/㎠ and 54.2 tonf.

• Journal of Hospice and Palliative Care
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• v.16 no.4
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• pp.205-215
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• 2013

Most terminally ill cancer patients experience various physical and psychological symptoms during their illness. In addition to pain, they commonly suffer from fatigue, anorexia-cachexia syndrome, nausea, vomiting and dyspnea. In this paper, I reviewed some of the common non-pain symptoms in terminally ill cancer patients, based on the National Comprehensive Cancer Network (NCCN) guidelines to better understand and treat cancer patients. Cancer-related fatigue (CRF) is a common symptom in terminally ill cancer patients. There are reversible causes of fatigue, which include anemia, sleep disturbance, malnutrition, pain, depression and anxiety, medical comorbidities, hyperthyroidism and hypogonadism. Energy conservation and education are recommended as central management for CRF. Corticosteroid and psychostimulants can be used as well. The anorexia and cachexia syndrome has reversible causes and should be managed. It includes stomatitis, constipation and uncontrolled severe symptoms such as pain or dyspnea, delirium, nausea/vomiting, depression and gastroparesis. To manage the syndrome, it is important to provide emotional support and inform the patient and family of the natural history of the disease. Megesteol acetate, dronabinol and corticosteroid can be helpful. Nausea and vomiting will occur by potentially reversible causes including drug consumption, uremia, infection, anxiety, constipation, gastric irritation and proximal gastrointestinal obstruction. Metoclopramide, haloperidol, olanzapine and ondansetron can be used to manage nausea and vomiting. Dyspnea is common even in terminally ill cancer patients without lung disease. Opioids are effective for symptomatic management of dyspnea. To improve the quality of life for terminally ill cancer patients, we should try to ameliorate these symptoms by paying more attention to patients and understanding of management principles.

• Korea Trade Review
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• v.48 no.3
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• pp.217-242
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• 2023

This study aims to analyze the factors influencing the logistics demand of inland ports along the Yangtze River and predict future port logistics demand based on these factors. The logistics demand prediction using system dynamics techniques was conducted for a total of six ports, including Chongqing and Yibin ports in the upper reaches, Jingzhou and Wuhan ports in the middle reaches, and Nanjing and Suzhou ports in the lower reaches of the Yangtze River. The logistics demand for all ports showed an increasing trend in the mid-term prediction until 2026. The logistics demand of Chongqing port was mainly influenced by the scale of the hinterland economy, while Yibin port appeared to heavily rely on the level of port automation. In the case of the upper and middle reach ports, logistics demand increased as the energy consumption of the hinterland increased and the air pollution situation worsened. The logistics demand of the middle reach ports was greatly influenced by the hinterland infrastructure, while the lower reach ports were sensitive to changes in the urban construction area. According to the sensitivity analysis, the logistics demand of ports relying on large cities was relatively stable against the increase and decrease of influential factors, while ports with smaller hinterland city scales reacted sensitively to changes in influential factors. Therefore, a strategy should be established to strengthen policy support for Chongqing port as the core port of the upper Yangtze River and have surrounding ports play a supporting role for Chongqing port. The upper reach ports need to play a supporting role for Chongqing port and consider measures to enhance connections with middle and lower reach ports and promote the port industry. The development strategy for inland ports along the Yangtze River suggests the establishment of direct routes and expansion of the transportation network for South Korean ports and stakeholders. It can suggest expanding the hinterland network and building an efficient transportation system linked with the logistics hub. Through cooperation, logistics efficiency can be enhanced in both regions, which will contribute to strengthening the international position and competitiveness of each port.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.173-189
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• 2023

As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.11
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• pp.1102-1110
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• 2008

This research was performed to analyze the characteristics of wood wastes from origin and to suggest grade-classification for them. Korean proximate analysis was conducted, and heating value, heavy metals and Cl concentrations were analyzed for gradeclassification. Wood wastes were sampled from forest, living, construction and demolition, and industrial areas with origin. Moisture content of most wood wastes was ranged in 5$\sim$10%. VS (volatile solids) and ash contents of them showed > 95% and < 5%, respectively. Most wood wastes except wood for growing mushroom permitted the standard (low heating value $\geq$ 3,500 kcal/kg) for refusederived fuel. CCA (Cr, Cu, As) concentration of wood wastes used in bench, wasted fishing boat, and railroad crosstie was higher than that of the other ones. Cl content showed approximately 1.3% in wood box for fish and $\leq$ 0.2% in the other wood wastes. Cl content of all wood wasted used in this research permitted the standard (Cl $\leq$ 0.2%, dry weight basis) for refuse-derived fuel. If the wood wastes were classified in 3-grade, plywoods would be in 2nd grade, and MDF (medium density fiber), wooden bench, painted electric wire drum, wasted fishing boat, and railroad crosstie be in 3rd grade.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.235-250
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• 2021

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and information flows in and out of a rice cultivation system and (2) appropriate metrics to assess production, efficiency, GHG fluxes, and resilience. The study site was one of the Korean Network of Flux measurement (KoFlux) sites (i.e., GRK) located at Gimje, Korea, managed by National Academy of Agricultural Science, Rural Development Administration. Fluxes of energy, water, carbon dioxide (CO₂) and methane (CH₄) were directly measured using eddy-covariance technique during the growing seasons of 2011, 2012 and 2014. The production indicators include gross primary productivity (GPP), grain yield, light use efficiency (LUE), water use efficiency (WUE), and carbon uptake efficiency (CUE). The GHG mitigation was assessed with indicators such as fluxes of carbon dioxide (F_CO2), methane (F_CH4), and nitrous oxide (F_N2O). Resilience was assessed in terms of self-organization (S), using information-theoretic approach. Overall, the results demonstrated that the rice cultivation system at GRK was climate-smart in 2011 in a relative sense but failed to maintain in the following years. Resilience was high and changed little for three year. However, the apparent competing goals or trade-offs between productivity and GHG mitigation were found within individual years as well as between the years, causing difficulties in achieving the triple-win scenario. The pursuit of CSA requires for stakeholders to prioritize their goals (i.e., governance) and to practice opportune interventions (i.e., management) based on the feedback from real-time assessment of the CSA indicators (i.e., monitoring) - i.e., a purpose-driven visioneering.