• BioChip Journal
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• v.16
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• pp.82-98
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• 2020

We report a quantitative and systematic method for determining 3D-printing and surface-treatment conditions that can help improve the optical quality of direct-printed microfluidic devices. Digital light processing (DLP)-stereolithography (SLA) printing was extensively studied in microfluidics owing to the rapid, one-step, cleanroom-free, maskless, and high-definition microfabrication of 3D-microfluidic devices. However, optical imaging or detection for bioassays in DLP-SLA-printed microfluidic devices are limited by the translucence of photopolymerized resins. Various approaches, including mechanical abrasions, chemical etching, polymer coatings, and printing on transparent glass/plastic slides, were proposed to address this limitation. However, the effects of these methods have not been analyzed quantitatively or systematically. For the first time, we propose quantitative and methodological determination of 3D-printing and surface-treatment conditions, based on optical-resolution analysis using USAF 1951 resolution test targets and a fluorescence microbead slide through 3D-printed coverslip chips. The key printing parameters (resin type, build orientation, layer thickness, and layer offset) and surface-treatment parameters (grit number for sanding, polishing time with alumina slurry, and type of refractive-index-matching coatings) were determined in a step-wise manner. As a result, we achieved marked improvements in resolution (from 80.6 to 645.1 lp/mm) and contrast (from 3.30 to 27.63% for 645.1 lp/mm resolution). Furthermore, images of the fluorescence microbeads were qualitatively analyzed to evaluate the proposed 3D-printing and surface-treatment approach for fluorescence imaging applications. Finally, the proposed method was validated by fabricating an acoustic micromixer chip and fluorescently visualizing cavitation microstreaming that emanated from an oscillating bubble captured inside the chip. We expect that our approach for enhancing optical quality will be widely used in the rapid manufacturing of 3D-microfluidic chips for optical assays.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.513-520
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• 2024

This study investigates the changes in teachers' roles as the impact of AI on school education expands. Traditionally, teachers have been responsible for core aspects of classroom instruction, curriculum development, assessment, and feedback. AI can automate these processes, particularly enhancing efficiency through personalized learning. AI also supports complex classroom management tasks such as student tracking, behavior detection, and group activity analysis using integrated camera and microphone systems. However, AI struggles to automate aspects of counseling and interpersonal communication, which are crucial in student life guidance. While direct conversational replacement by AI is challenging, AI can assist teachers by providing data-driven insights and pre-conversation resources. Key competencies required for teachers in the AI era include expertise in advanced instructional methods, dataset analysis, personalized learning facilitation, student and parent counseling, and AI digital literacy. Teachers should collaborate with AI to emphasize creativity, adjust personalized learning paths based on AI-generated datasets, and focus on areas less amenable to AI automation, such as individualized learning and counseling. Essential skills include AI digital literacy and proficiency in understanding and managing student data.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1027-1034
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• 2024

This study aims to address the issues of outliers and missing values in AI-based smart farming to improve data quality and enhance the accuracy of agricultural predictive activities. By utilizing real data provided by the Rural Development Administration (RDA) and the Korea Agency of Education, Promotion, and Information Service in Food, Agriculture, Forestry, and Fisheries (EPIS), outlier detection and missing value imputation techniques were applied to collect and manage high-quality data. For successful smart farm operations, an IoT-based AI automatic growth measurement model is essential, and achieving a high data quality index through stable data preprocessing is crucial. In this study, various methods for correcting outliers and imputing missing values in growth data were applied, and the proposed preprocessing strategies were validated using machine learning performance evaluation indices. The results showed significant improvements in model performance, with high predictive accuracy observed in key evaluation metrics such as ROC and AUC.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.305-309
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• 2024

Trimethylamine (TMA) is an organic amine gas that serves as a key indicator for evaluating the freshness of seafood. We synthesized a highly sensitive trimethylamine (TMA) sensor based on porous indium oxide (In₂O₃) nanoparticles (NPs) loaded with CuO in the range of 6.7 to 28.4 at.%. CuO was loaded by hydrazine reduction onto as prepared In₂O₃ NPs using the microwave irradiation method. Crystal structures, morphologies, and chemical composition of CuO/In₂O₃ nanostructures (NSs) were characterized by X-ray diffraction, field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, and inductively coupled plasma mass spectrometry. The response of the 23.8 at.% CuO/In₂O₃ to 2.5 ppm TMA at 325℃ was 5.7, which was 2.8 times higher than that of porous In₂O₃ NPs. The high sensitivity and selective detection of TMA were attributed to electronic interactions between CuO and In₂O₃ and the high catalytic activity of CuO to TMA. Altogether, this CuO/In₂O₃ sensor could be used in the future to detect low concentrations of TMA, thereby aiding in the storage and distribution of marine food resources.

• Journal of Digestive Cancer Research
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• v.12 no.2
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• pp.90-101
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• 2024

The incidence of colon cancer in South Korea has recently been the highest among gastrointestinal cancers. Early diagnosis is critical, and image-enhanced endoscopy (IEE) is a key diagnostic method. Colon tumors primarily include serrated polyps, adenomatous polyps, and colon cancer. Early endoscopic techniques relied on simple visual inspection for diagnosis, with tumor size and shape being the primary considerations. Low-resolution images made these methods ineffective for detecting small or early-stage lesions. IEE now enables detailed examination using high-resolution images and various color and structure analyses. Techniques like narrow band imaging (NBI) allow precise observation of vascular patterns and surface structures. Hyperplastic polyps often appear similar in color to the surrounding mucosa, with no visible vascular pattern. Sessile serrated lesions have a cloudy surface with distinct boundaries and irregular patterns, often with black spots in the crypts. Adenomatous polyps are darker brown, with a visible white epithelial network and various pit patterns. Magnified images help differentiate between low- and high-grade dysplasia, with low-grade showing regular patterns and high-grade showing increased irregularities. The NBI International Colorectal Endoscopic classification identifies malignant colon tumors as brown or dark brown with disorganized vascular patterns. The Japan NBI Expert Team classification includes loose vascular areas and disrupted thick vessels. The Workgroup serrAted polypS and Polyposis classification aids in differentiating between hyperplastic polyps and sessile serrated lesions/adenomas when deciding whether to resect polyps larger than 5 mm. Suspected high-grade dysplasia warrants endoscopic submucosal dissection and follow-up. Future advancements in IEE are expected to further enhance early detection and diagnostic accuracy.

• Korean Security Journal
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• no.26
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• pp.29-58
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• 2011

Since 11 September 2001, warnings of risk in the nexus of terrorism and nuclear weapons and materials which poses one of the gravest threats to the international community have continued. The purpose of this study is to analyze the aim, principles, characteristics, activities, impediments to progress and developmental recommendation of the Global Initiative to Combat Nuclear Terrorism(GICNT). In addition, it suggests implications of the GICNT for the ROK policy. International community will need a comprehensive strategy with four key elements to accomplish the GICNT: (1) securing and reducing nuclear stockpiles around the world, (2) countering terrorist nuclear plots, (3) preventing and deterring state transfers of nuclear weapons or materials to terrorists, (4) interdicting nuclear smuggling. Moreover, other steps should be taken to build the needed sense of urgency, including: (1) analysis and assessment through joint threat briefing for real nuclear threat possibility, (2) nuclear terrorism exercises, (3) fast-paced nuclear security reviews, (4) realistic testing of nuclear security performance to defeat insider or outsider threats, (5) preparing shared database of threats and incidents. As for the ROK, main concerns are transfer of North Korea's nuclear weapons, materials and technology to international terror groups and attacks on nuclear facilities and uses of nuclear devices. As the 5th nuclear country, the ROK has strengthened systems of physical protection and nuclear counterterrorism based on the international conventions. In order to comprehensive and effective prevention of nuclear terrorism, the ROK has to strengthen nuclear detection instruments and mobile radiation monitoring system in airports, ports, road networks, and national critical infrastructures. Furthermore, it has to draw up effective crisis management manual and prepare nuclear counterterrorism exercises and operational postures. The fundamental key to the prevention, detection and response to nuclear terrorism which leads to catastrophic impacts is to establish not only domestic law, institution and systems, but also strengthen international cooperation.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.121-138
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• 2011

More affordable and available cutting-edge technologies (e.g., wireless vehicle communication) are regarded as a possible alternative to the fixed infrastructure-based traffic information system requiring the expensive infrastructure investments and mostly implemented in the uninterrupted freeway network with limited spatial system expansion. This paper develops an advanced decentralized traveler information System (ATIS) using vehicle-to-vehicle (V2V) communication system whose performance (drivers' travel time savings) are enhanced by three complementary functions (autonomous automatic incident detection algorithm, reliable sample size function, and driver behavior model) and evaluates it in the typical $6{\times}6$ urban grid network with non-recurrent traffic state (traffic incident) with the varying key parameters (traffic flow, communication radio range, and penetration ratio), employing the off-the-shelf microscopic simulation model (VISSIM) under the ideal vehicle communication environment. Simulation outputs indicate that as the three key parameters are increased more participating vehicles are involved for traffic data propagation in the less communication groups at the faster data dissemination speed. Also, participating vehicles saved their travel time by dynamically updating the up-to-date traffic states and searching for the new route. Focusing on the travel time difference of (instant) re-routing vehicles, lower traffic flow cases saved more time than higher traffic flow ones. This is because a relatively small number of vehicles in 300vph case re-route during the most system-efficient time period (the early time of the traffic incident) but more vehicles in 514vph case re-route during less system-efficient time period, even after the incident is resolved. Also, normally re-routings on the network-entering links saved more travel time than any other places inside the network except the case where the direct effect of traffic incident triggers vehicle re-routings during the effective incident time period and the location and direction of the incident link determines the spatial distribution of re-routing vehicles.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.379-399
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• 2020

The detection of illegal ship is one of the key factors in building a marine surveillance system. Effective marine surveillance requires the means for continuous monitoring over a wide area. In this study, the possibility of ship detection monitoring based on satellite SAR, HF radar, UAV and AIS integration was investigated. Considering the characteristics of time and spatial resolution for each platform, the ship monitoring scenario consisted of a regular surveillance system using HFR data and AIS data, and an event monitoring system using satellites and UAVs. The regular surveillance system still has limitations in detecting a small ship and accuracy due to the low spatial resolution of HF radar data. However, the event monitoring system using satellite SAR data effectively detects illegal ships using AIS data, and the ship speed and heading direction estimated from SAR images or ship tracking information using HF radar data can be used as the main information for the transition to UAV monitoring. For the validation of monitoring scenario, a comprehensive field experiment was conducted from June 25 to June 26, 2019, at the west side of Hongwon Port in Seocheon. KOMPSAT-5 SAR images, UAV data, HF radar data and AIS data were successfully collected and analyzed by applying each developed algorithm. The developed system will be the basis for the regular and event ship monitoring scenarios as well as the visualization of data and analysis results collected from multiple platforms.