• Current Optics and Photonics
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• v.7 no.3
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.1-7
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• 2023

This study has utilized self-assembled monolayers technology to improve electrical property of graphene-oxide, which has been seperated graphine powder through a chemical exfoliation. Aluminum sulfate (Al₂(SO₄)₃) was applied on graphene-oxide as a reactant, and the fundamental research was carried out to apply on the self-sensing of cement-based construction structures. Electric resistance measurement result has shown that cement-composites with GO and Al-GO can be used as a conductor, electric resistance of GO and Al-GO contained composites improved by 10.2% and 15.9% respectively when compared to the standard cement-composite. Microstructure analyzation shown the formation of Al(OH)₃ gel when Al-GO was added, which is speculated to result the smooth flow of current by improving the density of cement-composite. This implies that graphene-oxide has a possibility to be utilized as smart building materials and construction structure itself rather than just a structure.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.194-198
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• 2023

With the recent development of industrial technology, the problem of odor due to leakage of toxic gas discharged from industrial complexes is gradually increasing. Among them, hydrogen sulfide is a colorless representative odorous substance that can cause pain through irritation of the mucous membranes of the eyes and respiratory tract, and is a gas that can cause central nervous system paralysis and suffocation when exposed to high concentrations. Therefore, in order to improve the odor problem, research on a gas sensor capable of quickly and reliably detecting a leak of hydrogen sulfide is being actively conducted. A lot of research has been done on the existing metal oxide-based hydrogen sulfide gas sensor, but it has the disadvantage of requiring low selectivity and high temperature operating conditions. Therefore, in this study, a Pt/CNT-based electrochemical hydrogen sulfide gas sensor capable of detecting at low temperatures with high selectivity for hydrogen sulfide was developed. A working electrode capable of selectively detecting only hydrogen sulfide was fabricated by synthesizing Pt nanoparticles as a catalyst on functionalized CNT and applied to an electrochemical hydrogen sulfide gas sensor. It was confirmed that the manufactured Pt/CNT-based electrochemical hydrogen sulfide gas sensor has a current change of up to 100uA for hydrogen sulfide, and the both response time and recovery time were within 15 seconds.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.81-87
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• 2023

SAE J2601 and SAE J2799, the communication protocols between a hydrogen refueling station and a fuel cell electric vehicle, only cover hydrogen charging. In this paper, we measure the hydrogen detection, current, and voltage of a fuel cell electric vehicle and transmit the sensor data to the hydrogen refueling station by changing the WiFi protocol. A small-scale laboratory model was built using Raspberry Pi for sensing, controlling, and transmitting sensor data of a fuel cell electric vehicle. The sensor data was stored in the database of the hydrogen refueling station, and a dashboard was configured using Grafana to analyze the stored data. When hydrogen is detected, the dispenser valve of the hydrogen refueling station is locked. Then, we measured the average transmission delay according to the WiFi protocol. The results showed that IEEE 802.11a is the most suitable WiFi protocol for transmitting sensor data between the hydrogen refueling station and the fuel cell electric vehicle.

• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.63-77
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• 2024

Yield monitoring systems have become integral to precision agriculture, providing insights into the spatial variability of crop yield and playing an important role in modern harvesting technology. This paper aims to review current research trends in yield monitoring systems, specifically designed for non-grain crops, including cabbages, radishes, potatoes, and tomatoes. A systematic literature survey was conducted to evaluate the performance of various monitoring methods for non-grain crop yields. This study also assesses both mass- and volume-based yield monitoring systems to provide precise evaluations of agricultural productivity. Integrating load cell technology enables precise mass flow rate measurements and cumulative weighing, offering an accurate representation of crop yields, and the incorporation of image-based analysis enhances the overall system accuracy by facilitating volumetric flow rate calculations and refined volume estimations. Mass flow methods, including weighing, force impact, and radiometric approaches, have demonstrated impressive results, with some measurement error levels below 5%. Volume flow methods, including paddle wheel and optical methodologies, yielded error levels below 3%. Signal processing and correction measures also play a crucial role in achieving accurate yield estimations. Moreover, the selection of sensing approach, sensor layout, and mounting significantly influence the performance of monitoring systems for specific crops.

• Journal of Astronomy and Space Sciences
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• v.41 no.1
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• pp.1-15
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• 2024

The Korean heliospheric community, led by the Korea Astronomy and Space Science Institute (KASI), is currently assessing the viability of deploying a spacecraft at the Sun-Earth Lagrange Point L4 in collaboration with National Aeronautics and Space Administration (NASA). The aim of this mission is to utilize a combination of remote sensing and in situ instruments for comprehensive observations, complementing the capabilities of the L1 and L5 observatories. The paper outlines longterm scientific objectives, underscoring the significance of multi-point in-situ observations to better understand critical heliospheric phenomena. These include coronal mass ejections, magnetic flux ropes, heliospheric current sheets, kinetic waves and instabilities, suprathermal electrons and solar energetic particle events, as well as remote detection of solar radiation phenomena. Furthermore, the mission's significance in advancing space weather prediction and space radiation exposure assessment models through the integration of L4 observations is discussed. This article is concluded with an emphasis on the potential of L4 observations to propel advancements in heliospheric science.

• Journal of Animal Science and Technology
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• v.66 no.1
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.385-394
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• 2015

Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.

• Korean Chemical Engineering Research
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• v.48 no.1
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• pp.33-38
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• 2010

A microfluidic system has been developed using biomaterial for the measurement of cupric ion concentration. The cell-membrane-mimicking bilayer lipid membrane(BLM)-coated silver electrode was used for the sensing of cupric ion concentration. The silver-supported BLM could increase its stability. A silver-supported bilayer lipid membrane(s-BLM) was easily obtained using its self-assembling characteristics by immersing silver wire into lipid(phosphatidylcholine; PC) solution and then dipping into aqueous KCl solution. These s-BLMs were used to determine the relationship between $Cu^{2+}$ concentration and current crossing s-BLM. Their relationship showed high linearity and reproducibility. The calibration curve was constructed to express the relationship between $Cu^{2+}$ concentration and current in the $Cu^{2+}$ concentration range of 10 and $130{\mu}M$. This calibration curve was used to measure $Cu^{2+}$ concentration in an unknown sample. Microfluidic system with s-BLM was made of PDMS(polydimethyl siloxane) using typical soft photolithography and molding technique. This integrated system has various functions such as activation of the silver surface without cutting silver wire, coating of BLM on silver surface, injection of KCl buffer solution, injection of $Cu^{2+}$ sample and measurement of $Cu^{2+}$ concentration in the sample.