• Pediatric Infection and Vaccine
• /
• v.28 no.2
• /
• pp.92-100
• /
• 2021

Purpose: Rapid detection of etiologic organisms is crucial for initiating appropriate therapy in patients with central nervous system (CNS) infection. This study aimed to evaluate the diagnostic value of the BioFire^® Meningitis/Encephalitis (ME) panel in detecting etiologic organisms in cerebrospinal fluid (CSF) samples from febrile infants. Methods: CSF samples from infants aged <90 days who were evaluated for fever were collected between January 2016 and July 2019 at the Seoul National University Children's Hospital. We performed BioFire^® ME panel testing of CSF samples that had been used for CSF analysis and conventional tests (bacterial culture, Xpert^® enterovirus assay, and herpes simplex virus-1 and -2 polymerase chain reaction) and stored at -70℃ until further use. Results: In total, 72 (24 pathogen-identified and 48 pathogen-unidentified) CSF samples were included. Using BioFire^® ME panel testing, 41 (85.4%) of the 48 pathogen-unidentified CSF samples yielded negative results and 22 (91.7%) of the 24 pathogen-identified CSF samples yielded the same results (enterovirus in 19, Streptococcus agalactiae in 2, and Streptococcus pneumoniae in 1) as those obtained using the conventional tests, thereby resulting in an overall agreement of 87.5% (63/72). Six of the 7 pathogen-unidentified samples were positive for human parechovirus (HPeV) via BioFire^® ME panel testing. Conclusions: Compared with the currently available etiologic tests for CNS infection, BioFire^® ME panel testing demonstrated a high agreement score for pathogen-identified samples and enabled HPeV detection in young infants. The clinical utility and cost-effectiveness of BioFire^® ME panel testing in children must be evaluated for its wider application.

• Journal of Intelligence and Information Systems
• /
• v.27 no.4
• /
• pp.49-71
• /
• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.191-206
• /
• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of the Society of Disaster Information
• /
• v.18 no.1
• /
• pp.202-217
• /
• 2022

Purpose: The recent increased use of Personal Mobility (PM) has been accompanied by a rise in the annual number of accidents. Accordingly, the safety requirements for PM use are being strengthened, but the laws/systems, infrastructure, and management systems remain insufficient for fostering a safe environment. Therefore, this study comprehensively searches the main problems and improvement methods through a review of previous studies that are related to PM. Then the priorities according to the importance of the improvement methods are presented through the Delphi survey. Method: The research method is mainly composed of a literature study and an expert survey (Delphi survey). Prior research and improvement cases (local governments, government departments, companies, etc.) are reviewed to derive problems and improvements, and a problem/improvement classification table is created based on keywords. Based on the classification contents, an expert survey is conducted to derive a priority improvement plan. Result: The PM-related problems were in 'non-compliance with traffic laws, lack of knowledge, inexperienced operation, and lack of safety awareness' in relation to human factors, and 'device characteristics, road-drivable space, road facilities, parking facilities' in relation to physical factors. 'Management/supervision, product management, user management, education/training' as administrative factors and legal factors are divided into 'absence/sufficiency of law, confusion/duplication, reduced effectiveness'. Improvement tasks related to this include 'PM education/public relations, parking/return, road improvement, PM registration/management, insurance, safety standards, traffic standards, PM device safety, PM supplementary facilities, enforcement/management, dedicated organization, service providers, management system, and related laws/institutional improvement', and 42 detailed tasks are derived for these 14 core tasks. The results for the importance evaluation of detailed tasks show that the tasks with a high overall average for the evaluation items of cost, time, effect, urgency, and feasibility were 'strengthening crackdown/instruction activities, education publicity/campaign, truancy PM management, and clarification of traffic rules'. Conclusion: The PM market is experiencing gradual growth based on shared services and a safe environment for PM use must be ensured along with industrial revitalization. In this respect, this study seeks out the major problems and improvement plans related to PM from a comprehensive point of view and prioritizes the necessary improvement measures. Therefore, it can serve as a basis of data for future policy establishment. In the future, in-depth data supplementation will be required for each key improvement area for practical policy application.

• Journal of Bio-Environment Control
• /
• v.31 no.1
• /
• pp.14-21
• /
• 2022

Uniform scions and rootstocks should be produced to ensure grafting success. Light quality is an important environmental factor that regulates seedling growth. The effects of warm- and cool-white light emitting diode (LED) ratios on seedling growth were investigated. Scions and rootstocks of cucumber, tomato, and watermelon were grown in a closed transplant production system using LED as the sole lighting source. The LED treatments were W1C0 (only warm-white), W1C1 (warm-white: cool-white = 1:1), W3C1 (warm-white: cool-white = 3:1), and W5C2 (warm-white: cool-white = 5:2). The seedlings grown in W1C1 had the shortest hypocotyls, and the seedlings grown in W1C0 had the longest hypocotyls among the three tested vegetables. The hypocotyls of watermelon scions, watermelon rootstocks, and tomato rootstocks were shortest in W1C1, followed by those in W3C1, W5C2, and W1C0, but there was no significant difference between W3C1 and W5C2, which remained the same as the ratio of cool-white LEDs increased. In addition, tomato scions had the first and second longest hypocotyls in W1C0 and W3C1, respectively, and the shortest hypocotyls in W5C2 and W1C1, along with W5C2 and W1C1, although the difference was not significant. The stem diameter was highest in W1C0 except for tomato seedlings and rootstocks of watermelon. The shoot fresh weight of scions and rootstocks of cucumber and watermelon and the root fresh weight of cucumber scions were lowest in W1C1. These results indicated that different ratios of LED lighting sources had a strong effect on the hypocotyl elongation of seedlings.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.48 no.1
• /
• pp.1-10
• /
• 2022

Customized cosmetics are continuously mentioned as a trend in the cosmetics industry to respond to the recent rapid changes in the social environment and pursue individuality and diversity. Accordingly, four types of liposome essence corresponding to skin types were prepared by varying the ratio of liposome formulation and essence formulation as a customized cosmetic base that can be easily mixed and applied at the workplace. The volatilization residues of four types of liposome essence were measured and the nanoparticle size, polydispersity index, zeta potential and viscosity according to time for 90 d were measured, and Turbiscan was measured as a method for evaluating the stability of a colloidal dispersion system. In addition, a simple usability evaluation was performed for four types of liposome essence corresponding to the skin type. As a result, the amount of volatile residue in the four types of liposome essence was increased in dry products rather than oily ones, and the particle size showed a tendency to increase with time in the range of 165 to 175 nm, increasing up to 31.5%, and the polydispersity index was 0.23 to 0.26. There was little change with time, and the zeta potential was -74 to -72 mV, showing a slight decrease with time, but there was little change to the extent of a maximum decrease of 14.0%. Viscosity showed a decreasing trend with time in the range of 2,580 ~ 3,290 cps, showing a maximum decrease of 17.5%. In the turbiscan measurement, all of the turbiscan stability index, a measure of stability, were less than 1.0, indicating dispersion stability. In the overall simple usability satisfaction evaluation for skin types (6 points), products for oily skin (5.33 ± 0.75 points) > products for medium dry skin (5.13 ± 0.95 points) > products for dry skin (5.03 ± 0.96 points) > products for oily skin (4.80 ± 1.04 points) points) were evaluated in order. The four types of liposome essence corresponding to skin types with different ratios of liposome formulation and essence formulation were physically stable, and the possibility of application as a customized cosmetic base according to skin type was confirmed.

• The Korean Society of Law and Medicine
• /
• v.21 no.2
• /
• pp.209-244
• /
• 2020

TMS and tDCS are non-invasive devices that treat the diseases of patients or individual users, and manage or improve their health by applying stimulation to a brain through magnetism and electricity. The effect and safety of these devices have proved to be valid in several diseases, but research in this area is still much going on. Despite increasing cases of their application, legislations directly regulating TMS and tDCS are hard to find. Legal regulation regarding TMS and tDCS in the United States, Germany and Japan reveals that while TMS has been approved as a medical device with a moderate risk, tDCS has not yet earned approval as a medical device. However, the recent FDA guidance, European MDR changes, recalls in the US, and relevant legal provisions of Germany and Japan, as well as recommendations from expert groups all show signs of tDCS growing closer to getting approved as a medical device. Of course, safety and efficacy of tDCS can still be regulated as a general product instead of as a medical device. Considering multiple potential impacts on a human brain, however, the need for independent regulation is urgent. South Korea also lacks legal provisions explicitly regulating TMS and tDCS, but they fall into the category of the grade 3 medical devices according to the notifications of the Korean Ministry of Food and Drug Safety. And safety and efficacy of TMS are to be evaluated in compliance with the US FDA guidance. But no specific guidelines exist for tDCS yet. Given that tDCS devices are used in some hospitals in reality, and also at home by individual buyers, such a regulatory gap must quickly be addressed. In a longer term, legal system needs to be in place capable of independently regulating non-invasive brain stimulating devices.

• Korean Chemical Engineering Research
• /
• v.60 no.3
• /
• pp.334-346
• /
• 2022

There is a lack of knowledge and information on the understanding and application of the Process Safety Management (PSM) system, recognized as a major cause of industrial accidents in small-and medium-sized workplaces. Hence, it is necessary to prepare a protocol to secure the practical and continuous levels of implementation for PSM and eliminate human errors through tracking management. However, insufficient research has been conducted on this. Therefore, this study investigated and analyzed the various violations in the administrative measures, based on the regulations announced by the Ministry of Employment and Labor, in approximately 200 small-and medium-sized PSM workplaces with fewer than 300 employees across in korea. This study intended to contribute to the prevention of major industrial accidents by developing a facility maintenance web program that removed human errors in small-and medium-sized workplaces. The major results are summarized as follows. First, It accessed the web via a QR code on a smart device to check the equipment's specification search function, cause of failure, and photos for the convenience of accessing the program, which made it possible to make requests for the it inspection and maintenance in real time. Second, it linked the identification of the targets to be changed, risk assessment, worker training, and pre-operation inspection with the program, which allowed the administrator to track all the procedures from start to finish. Third, it made it possible to predict the life of the equipment and verify its reliability based on the data accumulated through the registration of the pictures for improvements, repairs, time required, cost, etc. after the work was completed. It is suggested that these research results will be helpful in the practical and systematic operation of small-and medium-sized PSM workplaces. In addition, it can be utilized in a useful manner for the development and dissemination of a facility maintenance web program when establishing future smart factories in small-and medium-sized PSM workplaces under the direction of the government.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.235-243
• /
• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.