• Nutrition Research and Practice
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• v.16 no.2
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• pp.173-193
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• 2022

BACKGROUND/OBJECTIVES: Alzheimer's disease (AD) is one of the most representative neurodegenerative disease mainly caused by the excessive production of amyloid beta (Aβ). Several studies on the antioxidant activity and protective effects of Populus tomentiglandulosa (PT) against cerebral ischemia-induced neuronal damage have been reported. Based on this background, the present study investigated the protective effects of PT against cognitive impairment in AD. MATERIALS/METHODS: We orally administered PT (50 and 100 mg/kg/day) for 14 days in an Aβ_25-35-induced mouse model and conducted behavioral experiments to test cognitive ability. In addition, we evaluated the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum and measured the production of lipid peroxide, nitric oxide (NO), and reactive oxygen species (ROS) in tissues. RESULTS: PT treatment improved the space perceptive ability in the T-maze test, object cognitive ability in the novel object recognition test, and spatial learning/long-term memory in the Morris water-maze test. Moreover, the levels of AST and ALT were not significantly different among the groups, indicating that PT did not show liver toxicity. Furthermore, administration of PT significantly inhibited the production of lipid peroxide, NO, and ROS in the brain, liver, and kidney, suggesting that PT protected against oxidative stress. CONCLUSIONS: Our study demonstrated that administration of PT improved Aβ_25-35-induced cognitive impairment by regulating oxidative stress. Therefore, we propose that PT could be used as a natural agent for AD improvement.

• Journal of Platform Technology
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• v.9 no.4
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• pp.3-9
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• 2021

In this paper, a graph data model is introduced to effectively recognize the situation between each object on the road detected by vehicles or road infrastructure sensors. The proposed method builds a graph database by modeling each object on the road as a node of the graph and the relationship between objects as an edge of the graph, and updates object properties and edge properties in real time. In this case, the relationship between objects represented as edges is set when there is a possibility of approach between objects in consideration of the position, direction, and speed of each object. Finally, we propose a spatial indexing technique for graph nodes and edges to update the road object graph database represented through the proposed graph modeling method continuously in real time. To show the superiority of the proposed indexing technique, we compare the proposed indexing based database update method to the non-indexing update method through simulation. The results of the simulation show the proposed method outperforms more than 10 times to the non-indexing method.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.529-536
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• 2022

In the past two years, Severe Acute Respiratory Syndrome Coronavirus-2(SARS-CoV-2) has been hitting more and more to people. This paper proposes a novel U-Net Convolutional Neural Network to classify and segment COVID-19 lung CT images, which contains Sub Coding Block (SCB), Atrous Spatial Pyramid Pooling(ASPP) and Attention Gate(AG). Three different models such as FCN, U-Net and U-Net-SCB are designed to compare the proposed model and the best optimizer and atrous rate are chosen for the proposed model. The simulation results show that the proposed U-Net-MMFE has the best Dice segmentation coefficient of 94.79% for the COVID-19 CT scan digital image dataset compared with other segmentation models when atrous rate is 12 and the optimizer is Adam.

• Industry Promotion Research
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• v.7 no.4
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• pp.85-95
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• 2022

This study intends to analyze the meaning of symbolism in ancestral memorial rite arrangemen from the view that ancestral worship connecting traditional society with modernity are the transmission of ritual. It appears as a change the theory of Yin-Yang and Five Elements that became the basis and ideology about the Confucian view of life and death, an understanding of the universe structure, and a change in the four seasons. Ancestral memorial rite arrangements acknowledge the existence of ancestors. And it is understood as a ceremonial instrument which the living and the dead communicate spatially with time through the symbolic system. In addition, the four seasons, spaces of the skyground and underground were symbolized and embodied through the selection and arrangement of ancestral memorial rites. In the modern ancestral memorial rite arrangement, the factors that determine the location require Time-space analysis of the target. This is because the offering is understood not only as a functional role but also as a temporal and spatial symbolism to be expressed through the offering. In this study, it is meaningful to consider it from the perspective of inheritance of ancestral worship culture through discussions about the ideological background and symbolic system that appeared in ancestral memorial rite arrangement

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.217-227
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• 2022

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.428-430
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• 2022

Image processing is playing an important part in automation and artificial intelligence systems, such as object tracking, object recognition and classification, and the importance of IoT technology and automation is emphasizing as interest in automation increases. However, in a system that requires detailed data such as an image boundary, a precise noise removal algorithm is required. Therefore, in this paper, we propose a filtering algorithm based on the pixel value distribution pattern to minimize the information loss in the filtering process. The proposed algorithm finds the distribution pattern of neighboring pixel values with respect to the pixel values of the input image. Then, a weight mask is calculated based on the distribution pattern, and the final output is calculated by applying it to the filtering mask. The proposed algorithm has superior noise removal characteristics compared to the existing method and restored the image while minimizing blurring.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.137-152
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• 2022

The cadastral boundary points which cadastral surveyor presented on the ground by conducting cadastral surveying represents the limits of land ownership and give binding force and determination power. However the land disputes have increased these days mainly due to cadastral registration errors, surveying errors and land owners recognition error. In these cases, the cadastral survey interests try to find solution by either a administrative procedures by appealing civil complaint or border determination litigation through court. The neighboring residents and related organization have difficulties in resolving the civil complaints. In this study cadastral surveying and cadastral boundary determination process has been reviewed and the results of cadastral surveying screening by Central Cadastre Committee from 2016 to 2021 have been classified. The outcomes of this research will be used for cadastral surveying and contribute for reducing land disputes and improve reliablity of cadastral surveying.

• Smart Media Journal
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• v.12 no.9
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• pp.134-141
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• 2023

In this paper, we propose a ToF-Stereo fusion sensor system for 3D space scanning that increases the recognition rate of 3D objects, guarantees object detection quality, and is robust to the environment. The ToF-Stereo sensor fusion system uses a method of fusing the sensing values of the ToF sensor and the Stereo RGB sensor, and even if one sensor does not operate, the other sensor can be used to continuously detect an object. Since the quality of the ToF sensor and the Stereo RGB sensor varies depending on the sensing distance, sensing resolution, light reflectivity, and illuminance, a module that can adjust the function of the sensor based on reliability estimation is placed. The ToF-Stereo sensor fusion system combines the sensing values of the ToF sensor and the Stereo RGB sensor, estimates the reliability, and adjusts the function of the sensor according to the reliability to fuse the two sensing values, thereby improving the quality of the 3D space scan.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.1-8
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• 2024

We present a novel algorithm for creating 2D maps tailored for autonomous navigation within orchards. Recognizing that fruit trees in orchards are typically aligned in rows, our primary goal is to accurately detect these tree rows and project this information onto the map. Initially, we propose a simple algorithm that recognizes trees from point cloud data by analyzing the spatial distribution of points. We then introduce a method for detecting fruit tree rows based on the positions of recognized fruit trees, which are integrated into the 2D orchard map. Validation of the proposed approach was conducted using real-world orchard point cloud data acquired via LiDAR. The results demonstrate high tree detection accuracy of 90% and precise tree row mapping, confirming the method's efficacy. Additionally, the generated maps facilitate the development of natural navigation paths that align with the orchard's layout.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.