• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.141-155
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• 2021

Purpose: To develop a 3D magnetic resonance fingerprinting (MRF) method for application in high resolution knee cartilage PD, T₁, T₂ mapping. Materials and Methods: A novel 3D acquisition trajectory with golden-angle rotating radial in k_xy direction and interleaved echo planar imaging (EPI) acquisition in the k_z direction was implemented in the MRF framework. A centric order was applied to the interleaved EPI acquisition to reduce Nyquist ghosting artifact due to field inhomogeneity. For the reconstruction, singular value decomposition (SVD) compression method was used to accelerate reconstruction time and conjugate gradient sensitivity-encoding (CG-SENSE) was performed to overcome low SNR of the high resolution data. Phantom experiments were performed to verify the proposed method. In vivo experiments were performed on 6 healthy volunteers and 2 early osteoarthritis (OA) patients. Results: In the phantom experiments, the T₁ and T₂ values of the proposed method were in good agreement with the spin-echo references. The results from the in vivo scans showed high quality proton density (PD), T₁, T₂ map with EPI echo train length (N_ETL = 4), acceleration factor in through plane (R_z = 5), and number of radial spokes (N_spk = 4). In patients, high T₂ values (50-60 ms) were seen in all transverse, sagittal, and coronal views and the damaged cartilage regions were in agreement with the hyper-intensity regions shown on conventional turbo spin-echo (TSE) images. Conclusion: The proposed 3D MRF method can acquire high resolution (0.5 mm³) quantitative maps in practical scan time (~ 7 min and 10 sec) with full coverage of the knee (FOV: 160 × 160 × 120 mm³).

• Journal of Korea Port Economic Association
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• v.38 no.1
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• pp.15-29
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• 2022

The Korean logistics industry is in a double challenges to train logistics professional in terms of quantity and quality. The study aims to derive implications for the logistics professional training policy and present the capabilities and skills of logistics manpower necessary in the Industry 4.0. To this end, the supply and demand status of logistics personnel was examined through a full survey of logistics curriculum of domestic universities, case analysis of successful overseas logistics education and training programs was attempted, and the skills and capabilities for logistics professional were comprehensively presented through in-depth literature research. As a policy recommendations for future training and education of logistics professional following policies are suggested; necessity of national standard logistics competency map and integrated platform for national logistics capabilities/skills development, creation of industry-university co-education/training ecosystem and education programs for new logistics professional in response to digital transformation and COVID-19 pandemic.

• Journal of Preventive Medicine and Public Health
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• v.56 no.2
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• pp.111-127
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• 2023

Objectives: Pre-pregnancy diet has an important role in preparing for healthy generation. However, evidence on this issue has been scarce. A scoping review synthesising current evidence will support the demand to map 'what has been researched' on pre-pregnancy diet and maternal and child health. Methods: Systematic search was performed using PICOS (Population, Intervention, Comparison, Outcomes, and Study design) framework in electronic databases. Articles were screened for eligibility, summarized, and the quality was assessed using the National Institute of Health assessment tool. The review structure complies with Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guide. Results: Forty-two articles were included after full-text screening. Twenty-five studies were in high-income countries (HICs), six in each upper-middle income, five in lower-middle income countries (LMICs), and one in low-income countries (LIC). Based on the regions: North America (n=16), Europe (n=5), South America (n=4), Australia (n=4), Asia (n=5), Middle East (n=2), and sub-Saharan Africa (n=1). The two-most observed diet-related exposures were dietary pattern (n=17) and dietary quality (n=12). The most assessed outcome was gestational diabetes mellitus (n=28) and fetal and newborn anthropometry (n=7). The average quality score±standard deviation was 70±18%. Conclusions: Research related to pre-pregnancy diet is still concentrated in HICs. The context of diet may vary; therefore, future research is encouraged in LMICs and LICs context, and Mediterranean, South-East Asia, Pacific, and African regions. Some maternal and child nutrition-related morbidity, such as anemia and micronutrient deficiencies, have not been discussed. Research on these aspects will benefit to fill in the gaps related to pre-pregnancy diet and maternal and child health.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.56-63
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• 2024

As the safety of unmanned vehicles continues to improve, their usage in urban environments, which are full of obstacles such as buildings, is expected to increase. When numerous unmanned vehicles are operated in such environments, an algorithm that takes into account mutual collision avoidance, as well as static and dynamic obstacle avoidance, is necessary. In this paper, we propose an algorithm that handles task assignment and path planning. To efficiently plan paths, we construct a grid-based map and derive the paths from it. To enable quick re-planning in dynamic environments, we focus on reducing computational time. Through simulation, we explain obstacle avoidance and mutual collision avoidance in small-scale problems and confirm their performance by observing the entire mission completion time (Makespan) in large-scale problems.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.3
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• pp.13-20
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• 2001

The purpose of this study is development of the management and guide system for convenient use of new address guide system for systematic management and use of spatial information gained by mapping of numerical map of building and road to large basic new address, survey and input of main gate, decision of the road session, naming and input of road name, grant of numbering of all houses and buildings, new address guide, real life geographical information and added information service on web site in Jecheon-si city area. In this study, the development and establishment of new address management and guide system in local cities are designed by making full use of GIS function of client server based system. A guide system is designed the same database as management system, and it is transformed into Web GIS environment and developed in order to add the various functions of life GIS as well as new address guide. In the result, the effectiveness of new address system and guide of Jecheon-si city could be highly increased owing to not only citizens' easy access but also easy availability of various informations necessary in life by developed its system.

• Publications of The Korean Astronomical Society
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• v.27 no.4
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• pp.111-116
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• 2012

Far-infrared observations provide crucial data for the investigation and characterisation of the properties of dusty material in the Interstellar Medium (ISM), since most of its energy is emitted between ~ 100 and $200{\mu}m$. We present the first all-sky image from a sensitive all-sky survey using the Japanese AKARI satellite, in the wavelength range $50-180{\mu}m$. Covering > 99% of the sky in four photometric bands with four filters centred at $65{\mu}m$, $90{\mu}m$, $140{\mu}m$, and $160{\mu}m$ wavelengths, this achieved spatial resolutions from 1 to 2 arcmin and a detection limit of < 10 MJy $sr^{-1}$, with absolute and relative photometric accuracies of < 20%. All-sky images of the Galactic dust continuum emission enable astronomers to map the large-scale distribution of the diffuse ISM cirrus, to study its thermal dust temperature, emissivity and column density, and to measure the interaction of the Galactic radiation field and embedded objects with the surrounding ISM. In addition to the point source population of stars, protostars, star-forming regions, and galaxies, the high Galactic latitude sky is shown to be covered with a diffuse filamentary-web of dusty emission that traces the potential sites of high latitude star formation. We show that the temperature of dust particles in thermal equilibrium with the ambient interstellar radiation field can be estimated by using $90{\mu}m$, $140{\mu}m$, and $160{\mu}m$ data. The FIR AKARI full-sky maps provide a rich new data set within which astronomers can investigate the distribution of interstellar matter throughout our Galaxy, and beyond.

• Journal of Korean Neurosurgical Society
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• v.61 no.3
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• pp.363-375
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• 2018

Intraoperative monitoring (IOM) utilizes electrophysiological techniques as a surrogate test and evaluation of nervous function while a patient is under general anesthesia. They are increasingly used for procedures, both surgical and endovascular, to avoid injury during an operation, examine neurological tissue to guide the surgery, or to test electrophysiological function to allow for more complete resection or corrections. The application of IOM during pediatric brain tumor resections encompasses a unique set of technical issues. First, obtaining stable and reliable responses in children of different ages requires detailed understanding of normal age-adjusted brain-spine development. Neurophysiology, anatomy, and anthropometry of children are different from those of adults. Second, monitoring of the brain may include risk to eloquent functions and cranial nerve functions that are difficult with the usual neurophysiological techniques. Third, interpretation of signal change requires unique sets of normative values specific for children of that age. Fourth, tumor resection involves multiple considerations including defining tumor type, size, location, pathophysiology that might require maximal removal of lesion or minimal intervention. IOM techniques can be divided into monitoring and mapping. Mapping involves identification of specific neural structures to avoid or minimize injury. Monitoring is continuous acquisition of neural signals to determine the integrity of the full longitudinal path of the neural system of interest. Motor evoked potentials and somatosensory evoked potentials are representative methodologies for monitoring. Free-running electromyography is also used to monitor irritation or damage to the motor nerves in the lower motor neuron level : cranial nerves, roots, and peripheral nerves. For the surgery of infratentorial tumors, in addition to free-running electromyography of the bulbar muscles, brainstem auditory evoked potentials or corticobulbar motor evoked potentials could be combined to prevent injury of the cranial nerves or nucleus. IOM for cerebral tumors can adopt direct cortical stimulation or direct subcortical stimulation to map the corticospinal pathways in the vicinity of lesion. IOM is a diagnostic as well as interventional tool for neurosurgery. To prove clinical evidence of it is not simple. Randomized controlled prospective studies may not be possible due to ethical reasons. However, prospective longitudinal studies confirming prognostic value of IOM are available. Furthermore, oncological outcome has also been shown to be superior in some brain tumors, with IOM. New methodologies of IOM are being developed and clinically applied. This review establishes a composite view of techniques used today, noting differences between adult and pediatric monitoring.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2004.11a
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• pp.100-103
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• 2004

Experiments and simulation were done to study the impact of contrast agent when CT scan was used to attenuation correction for PET images in PET/CT system. Whole body phantom was imaged with various concentration of iodine-based contrast agent using CT. Mathematical emission and transmission density map with liver were made to simulate for whole body FDG imaging. Various transmission density maps was generated with non-uniform enhancement of contrast agent, hypo-attenuating of contrast agent for tumor, different concentration of contrast agent, and so on. Attenuation correction was done with all transmission maps. In the experiments, we confirmed that attenuation coefficient was changed by concentration of contrast agent. From the simulation data, image quality of attenuation corrected images was affected by contrast agent and artifact was produced by contrast agent. These results indicated that the contrast agent should be used with a full understanding of its potential problem in PET/CT system.