• Title/Summary/Keyword: Real-time processing

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Raw Inonotus obliquus polysaccharide counteracts Alzheimer's disease in a transgenic mouse model by activating the ubiquitin-proteosome system

  • Shumin Wang;Kaiye Dong;Ji Zhang;Chaochao Chen;Hongyan Shuai;Xin Yu
    • Nutrition Research and Practice
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    • v.17 no.6
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    • pp.1128-1142
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    • 2023
  • BACKGROUND/OBJECTIVES: Inonotus obliquus has been used as antidiabetic herb around the world, especially in the Russian and Scandinavian countries. Diabetes is widely believed to be a key factor in Alzheimer's disease (AD), which is widely considered to be type III diabetes. To investigate whether I. obliquus can also ameliorate AD, it would be interesting to identify new clues for AD treatment. We tested the anti-AD effects of raw Inonotus obliquus polysaccharide (IOP) in a mouse model of AD (3×Tg-AD transgenic mice). MATERIALS/METHODS: SPF-grade 3×Tg-AD mice were randomly divided into three groups (Control, Metformin, and raw IOP groups, n = 5 per group). β-Amyloid deposition in the brain was analyzed using immunohistochemistry for AD characterization. Gene and protein expression of pertinent factors of the ubiquitin-proteasome system (UPS) was determined using real-time quantitative polymerase chain reaction and Western blotting. RESULTS: Raw IOP significantly reduced the accumulation of amyloid aggregates and facilitated UPS activity, resulting in a significant reduction in AD-related symptoms in an AD mouse model. The presence of raw IOP significantly enhanced the expression of ubiquitin, E1, and Parkin (E3) at both the mRNA and protein levels in the mouse hippocampus. The mRNA level of ubiquitin carboxyl-terminal hydrolase isozyme L1, a key factor involved in UPS activation, also increased by approximately 50%. CONCLUSIONS: Raw IOP could contribute to AD amelioration via the UPS pathway, which could be considered as a new potential strategy for AD treatment, although we could not exclude other mechanisms involved in counteracting AD processing.

Autonomous Driving Platform using Hybrid Camera System (복합형 카메라 시스템을 이용한 자율주행 차량 플랫폼)

  • Eun-Kyung Lee
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.6
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    • pp.1307-1312
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    • 2023
  • In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

A Study on the Efficient Load Balancing Method Considering Real-time Data Entry form in SDN Environment (SDN 환경에서 실시간 데이터 유입형태를 고려한 효율적인 부하분산 기법 연구)

  • Ju-Seong Kim;Tae-Wook Kwon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.6
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    • pp.1081-1086
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    • 2023
  • The rapid growth and increasing complexity of modern networks have highlighted the limitations of traditional network architectures. The emergence of SDN (Software-Defined Network) in response to these challenges has changed the existing network environment. The SDN separates the control unit and the data unit, and adjusts the network operation using a centralized controller. However, this structure has also recently caused a huge amount of traffic due to the rapid spread of numerous Internet of Things (IoT) devices, which has not only slowed the transmission speed of the network but also made it difficult to ensure quality of service (QoS). Therefore, this paper proposes a method of load distribution by switching the IP and any server (processor) from the existing data processing scheduling technique, RR (Round-Robin), to mapping when a large amount of data flows in from a specific IP, that is, server overload and data loss.

Design and Implementation of Early Warning Monitoring System for Cross-border Mining in Open-pit Mines (노천광산의 월경 채굴 조기경보 모니터링시스템의 설계 및 구현)

  • Li Ke;Byung-Won Min
    • Journal of Internet of Things and Convergence
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    • v.10 no.2
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    • pp.25-41
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    • 2024
  • For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

An Automatic Data Collection System for Human Pose using Edge Devices and Camera-Based Sensor Fusion (엣지 디바이스와 카메라 센서 퓨전을 활용한 사람 자세 데이터 자동 수집 시스템)

  • Young-Geun Kim;Seung-Hyeon Kim;Jung-Kon Kim;Won-Jung Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.1
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    • pp.189-196
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    • 2024
  • Frequent false positives alarm from the Intelligent Selective Control System have raised significant concerns. These persistent issues have led to declines in operational efficiency and market credibility among agents. Developing a new model or replacing the existing one to mitigate false positives alarm entails substantial opportunity costs; hence, improving the quality of the training dataset is pragmatic. However, smaller organizations face challenges with inadequate capabilities in dataset collection and refinement. This paper proposes an automatic human pose data collection system centered around a human pose estimation model, utilizing camera-based sensor fusion techniques and edge devices. The system facilitates the direct collection and real-time processing of field data at the network periphery, distributing the computational load that typically centralizes. Additionally, by directly labeling field data, it aids in constructing new training datasets.

A Study on the Data Analysis of Fire Simulation in Underground Utility Tunnel for Digital Twin Application (디지털트윈 적용을 위한 지하공동구 화재 시뮬레이션의 데이터 분석 연구)

  • Jae-Ho Lee;Se-Hong Min
    • Journal of the Society of Disaster Information
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    • v.20 no.1
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    • pp.82-92
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    • 2024
  • Purpose: The purpose of this study is to find a solution to the massive data construction that occurs when fire simulation data is linked to augmented reality and the resulting data overload problem. Method: An experiment was conducted to set the interval between appropriate input data to improve the reliability and computational complexity of Linear Interpolation, a data estimation technology. In addition, a validity verification was conducted to confirm whether Linear Interpolation well reflected the dynamic changes of fire. Result: As a result of application to the underground common area, which is the study target building, it showed high satisfaction in improving the reliability of Interpolation and the operation processing speed of simulation when data was input at intervals of 10 m. In addition, it was verified through evaluation using MAE and R-Squared that the estimation method of fire simulation data using the Interpolation technique had high explanatory power and reliability. Conclusion: This study solved the data overload problem caused by applying digital twin technology to fire simulation through Interpolation techniques, and confirmed that fire information prediction and visualization were of great help in real-time fire prevention.

Density map estimation based on deep-learning for pest control drone optimization (드론 방제의 최적화를 위한 딥러닝 기반의 밀도맵 추정)

  • Baek-gyeom Seong;Xiongzhe Han;Seung-hwa Yu;Chun-gu Lee;Yeongho Kang;Hyun Ho Woo;Hunsuk Lee;Dae-Hyun Lee
    • Journal of Drive and Control
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    • v.21 no.2
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    • pp.53-64
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    • 2024
  • Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R2) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

Research on Ocular Data Analysis and Eye Tracking in Divers

  • Ye Jun Lee;Yong Kuk Kim;Da Young Kim;Jeongtack Min;Min-Kyu Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.8
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    • pp.43-51
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    • 2024
  • This paper proposes a method for acquiring and analyzing ocular data using a special-purpose diver mask targeted at divers who primarily engage in underwater activities. This involves tracking the user's gaze with the help of a custom-built ocular dataset and a YOLOv8-nano model developed for this purpose. The model achieved an average processing time of 45.52ms per frame and successfully recognized states of eyes being open or closed with 99% accuracy. Based on the analysis of the ocular data, a gaze tracking algorithm was developed that can map to real-world coordinates. The validation of this algorithm showed an average error rate of about 1% on the x-axis and about 6% on the y-axis.

Improving and Managing Air Quality in Noksan National Industrial Complex: Focus on Volatile Organic Compounds (녹산국가산단의 대기질 개선 및 관리방안: 휘발성유기화합물질 위주로)

  • Jong-min Kang
    • Journal of Environmental Science International
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    • v.33 no.9
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    • pp.645-665
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    • 2024
  • For volatile organic compounds (VOCs) emanating from workplaces within the Noksan national industrial complex, the emission characteristics of pollutants were identified through zone-based measurements using vehicles equipped with selective ion flow tube mass spectrometry(SIFT-MS). The average concentration of total VOCs was higher in zones 2, 4, and 5 than in zones 1, 3, and 6, and was 2.1 to 4.2 times higher than background concentrations. The average concentrations of pollutants investigated were (from highest to lowest) methyl ethyl ketone, formaldehyde, methanol, and n-hexane. However, the pollutants that should be prioritized for reduction to decrease ozone generation were (from highest to lowest) methyl ethyl ketone, n-hexane, for maldehyde, and ethylbenzene+xylene. Benzene, a substance governed by atmospheric environmental standards, exhibited a frequency distribution exceeding the stipulated limits, and concentrations exceeding 100 ppb were identified for methyl ethyl ketone, methanol, toluene, and n-hexane. In certain class 4 and 5 workplace facilities, VOC emissions and emission prevention installations were inadequately managed, necessitating the formulation of management measures for small enterprises. Also, workplaces that emit large amounts of VOCs need to upgrade to VOC-prevention installations with higher processing efficiencies. To efficiently monitor VOCs in a wide range of areas, such as the Noksan national industrial complex, it is considered appropriate to monitor workplaces that emit high concentrations of VOCs using mobile SIFT-MS in real time rather than relying on fixed monitoring methods. A specialized method targeting approximately 10 VOCs is necessary to quickly track emission sources.Furthermore, it is essential to phase in a system for the intensive management of suspected workplaces based on accumulated data from SIFT-MS in areas where high VOC concentrations are measured and to establish a cooperative system for sharing data between relevant institutions.

A Design and Implementation of Worker Motion 3D Visualization Module Based on Human Sensor

  • Sejong Lee
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.9
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    • pp.109-114
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    • 2024
  • In this paper, we design and implement a worker motion 3D visualization module based on human sensors. The three key modules that make up this system are Human Sensor Implementation, Data Set Creation, and Visualization. Human Sensor Implementation provides the functions of setting and installing the human sensor locations and collecting worker motion data through the human sensors. Data Set Creation offers functions for converting and storing motion data, creating near real-time worker motion data sets, and processing and managing sensor and motion data sets. Visualization provides functions for visualizing the worker's 3D model, evaluating motions, calculating loads, and managing large-scale data. In worker 3D model visualization, motion data sets (Skeleton & Position) are synchronized and mapped to the worker's 3D model, and the worker's 3D model motion animation is visualized by combining the worker's 3D model with analysis results. The human sensor-based worker motion 3D visualization module designed and implemented in this paper can be widely utilized as a foundational technology in the smart factory field in the future.