• Title/Summary/Keyword: Location Detection Technology

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A Bacteriological Assessment for Salmonella and Escherichia coli in Some Selected Fresh Water Prawn (Macrobrachium rosenbergii) Farms and Depots

  • Haider, M.N.;Faridullah, M.;Kamal, M.;Islam, M.N.;Khan, M.N.A.
    • Journal of Marine Bioscience and Biotechnology
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    • v.2 no.1
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    • pp.40-47
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    • 2007
  • Golda farms and depots of selected areas of the different districts of Bangladesh viz. Khulna, Bagerhat, Jessore and Norial area were sampled for the detection of Salmonella sp. and Escherichia coli. Incidence of Salmonella positive samples was 39%, 25%, 50% and 42% in the farms and 30%, 20%, 20% and 30% in the depots of Dumuria under Khulna, Bagerhat Sadar under Bagerhat, Avoynagar under Jessore and Kalia under Norail district respectively. On the other hand, E. coli positive samples was 23%, 42%, 25% and 17% in the farms and 70%, 30%, 50% and 30% in the depots of Dumuria (Khulna), Bagerhat Sadar (Bagerhat), Avoynagar (Jessore) and Kalia (Norail) region respectively. The overall results indicate that the trend of Samonella and E. coli contamination in farms and depots of all the regions is more or less similar although some variations were observed among the farms and depots of different location and region.

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An SPC-Based Forward-Backward Algorithm for Arrhythmic Beat Detection and Classification

  • Jiang, Bernard C.;Yang, Wen-Hung;Yang, Chi-Yu
    • Industrial Engineering and Management Systems
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    • v.12 no.4
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    • pp.380-388
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    • 2013
  • Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

Visual Sensing of the Light Spot of a Laser Pointer for Robotic Applications

  • Park, Sung-Ho;Kim, Dong Uk;Do, Yongtae
    • Journal of Sensor Science and Technology
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    • v.27 no.4
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    • pp.216-220
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    • 2018
  • In this paper, we present visual sensing techniques that can be used to teach a robot using a laser pointer. The light spot of an off-the-shelf laser pointer is detected and its movement is tracked on consecutive images of a camera. The three-dimensional position of the spot is calculated using stereo cameras. The light spot on the image is detected based on its color, brightness, and shape. The detection results in a binary image, and morphological processing steps are performed on the image to refine the detection. The movement of the laser spot is measured using two methods. The first is a simple method of specifying the region of interest (ROI) centered at the current location of the light spot and finding the spot within the ROI on the next image. It is assumed that the movement of the spot is not large on two consecutive images. The second method is using a Kalman filter, which has been widely employed in trajectory estimation problems. In our simulation study of various cases, Kalman filtering shows better results mostly. However, there is a problem of fitting the system model of the filter to the pattern of the spot movement.

Real-Time Mapping of Mobile Robot on Stereo Vision (스테레오 비전 기반 이동 로봇의 실시간 지도 작성 기법)

  • Han, Cheol-Hun;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.60-65
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    • 2010
  • This paper describes the results of 2D mapping, feature detection and matching to create the surrounding environment in the mounted stereo camera on Mobile robot. Extract method of image's feature in real-time processing for quick operation uses the edge detection and Sum of Absolute Difference(SAD), stereo matching technique can be obtained through the correlation coefficient. To estimate the location of a mobile robot using ZigBee beacon and encoders mounted on the robot is estimated by Kalman filter. In addition, the merged gyro scope to measure compass is possible to generate map during mobile robot is moving. The Simultaneous Localization and Mapping (SLAM) of mobile robot technology with an intelligent robot can be applied efficiently in human life would be based.

ESTABLISHMENT OF A NEURAL NETWORK MODEL FOR DETECTING A PARTIAL FLOW BLOCKAGE IN AN ASSEMBLY OF A LIQUID METAL REACTOR

  • Seong, Seung-Hwan;Jeong, Hae-Yong;Hur, Seop;Kim, Seong-O
    • Nuclear Engineering and Technology
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    • v.39 no.1
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    • pp.43-50
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    • 2007
  • A partial flow blockage in an assembly of a liquid metal reactor could result in a cooling deficiency of the core. To develop a partial blockage detection system, we have studied the changes of the temperature fluctuation characteristics in the upper plenum according to changes of the t10w blockage conditions in an assembly. We analyzed the temperature fluctuation in the upper plenum with the Large Eddy Simulation (LES) turbulence model in the CFX code and evaluated its statistical parameters. Based on the results of the statistical analyses, we developed a neural network model for detecting a partial flow blockage in an assembly. The neural network model can retrieve the size and the location of a flow blockage in an assembly from a change of the root mean square, the standard deviation, and the skewness in the temperature fluctuation data. The neural network model was found to be a possible alternative by which to identify a flow blockage in an assembly of a liquid metal reactor through learning and validating various flow blockage conditions.

Feasibility study of a resistive-type sodium aerosol detector with ZnO nanowires for sodium-cooled fast reactors

  • Jewhan Lee;Da-Young Gam;Ki Ean Nam;Seong J. Cho;Hyungmo Kim
    • Nuclear Engineering and Technology
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    • v.55 no.7
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    • pp.2373-2379
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    • 2023
  • In sodium systems, leakage is one of the safety concerns; it can cause chemical reactions, which may result in fires. There are contact and non-contact types of leak detectors, and the conventional method of non-contact type detection is by gas sampling. Because of the complexity of this method, there has always been a need for a simple gas sensor, and the resistive-type nanostructure ZnO sensor is a promising option with various advantages. In this study, a ZnO sensor was fabricated, and the concept was tested as a leak detector using a dedicated experiment facility. The experiment results showed distinctive changes in resistance with the presence of sodium aerosol under various conditions. Replacing the conventional gas sampling with the ZnO sensors is expected to enable identification of the leakage location if used as a point-wise instrumentation and to greatly reduce the total cost, making the system simple, light, and effective. For further study, more tests will be performed to evaluate the sensitivity of key parameters under various conditions.

Adaptive Sensor/Heterogeneous Infrastructure Integrated Pedestrian Navigation Technology using Rényi Divergence-based Outlier Detection (Rényi Divergence 기반 이상치 검출을 통한 적응형 센서/이종 인프라 통합 보행자 항법 기술)

  • Jae Uk Kwon;Seong Yun Cho;JaeJun Yoo;SeongHun Seo
    • Journal of Positioning, Navigation, and Timing
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    • v.13 no.3
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    • pp.289-299
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    • 2024
  • In the Pedestrian Dead Reckoning (PDR)/Global Positioning System (GPS)/Wi-Fi-integrated navigation system for indoor/outdoor continuous positioning of pedestrians, the process of detecting outliers in measurements is very important. When accurate location information from measurements is used, reliable correction data can be generated during the fusion filtering process. However, abnormal measurements may occur in certain situations, such as indoor/outdoor transitions, which can degrade filter performance and lead to significant errors in the estimated position. To address this issue, this paper proposes a method for detecting outliers in measurements based on Rényi Divergence (RD). When the deviation of the RD value is large, the measurements are considered outliers, and positioning is performed using only pure PDR. Based on experiments conducted with real data, it was confirmed that outliers were effectively detected for abnormal measurements, leading to an improvement in the performance of pedestrian navigation.

TDoA-Based Practical Localization Using Precision Time-Synchronization (정밀 시각동기를 이용한 TDoA 기반의 위치 탐지)

  • Kim, Jae-Wan;Eom, Doo-Seop
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38C no.2
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    • pp.141-154
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    • 2013
  • The technology of precise time-synchronization between signal receive devices for separation distance operation can be a key point for the technology with TDoA-based system. We propose a new method for the higher accuracy of system's time-synchronization in this paper, which uses OCXO and DPLL with high accuracy to achieve phase synchronization at 1 pps (pulse per second) of signal. And the method receive time value from a GPS satellite. Essentially, the performance of GPS with high accuracy refers to long-term frequency stability for its reliability. As per the characteristic, as the GPS timing signals are synchronized continuously, the accuracy of time-synchronization gets improved proportionally. Therefore, if the time synchronization is accomplished, the accuracy of the synchronization can be up to 0.001 ppb (part per billion). Through the improved accuracy of the time-synchronization, the measurement error of TDOA-based location detection technology is evaluated. Consequently, we verify that TDoA-based location measurement error can be greatly improved via using the improved method for time-synchronization error.

Power spectral density method performance in detecting damages by chloride attack on coastal RC bridge

  • Mehrdad, Hadizadeh-Bazaz;Ignacio J., Navarro;Victor, Yepes
    • Structural Engineering and Mechanics
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    • v.85 no.2
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    • pp.197-206
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    • 2023
  • The deterioration caused by chloride penetration and carbonation plays a significant role in a concrete structure in a marine environment. The chloride corrosion in some marine concrete structures is invisible but can be dangerous in a sudden collapse. Therefore, as a novelty, this research investigates the ability of a non-destructive damage detection method named the Power Spectral Density (PSD) to diagnose damages caused only by chloride ions in concrete structures. Furthermore, the accuracy of this method in estimating the amount of annual damage caused by chloride in various parts and positions exposed to seawater was investigated. For this purpose, the RC Arosa bridge in Spain, which connects the island to the mainland via seawater, was numerically modeled and analyzed. As the first step, each element's bridge position was calculated, along with the chloride corrosion percentage in the reinforcements. The next step predicted the existence, location, and timing of damage to the entire concrete part of the bridge based on the amount of rebar corrosion each year. The PSD method was used to monitor the annual loss of reinforcement cross-section area, changes in dynamic characteristics such as stiffness and mass, and each year of the bridge structure's life using sensitivity equations and the linear least squares algorithm. This study showed that using different approaches to the PSD method based on rebar chloride corrosion and assuming 10% errors in software analysis can help predict the location and almost exact amount of damage zones over time.

Mean Teacher Learning Structure Optimization for Semantic Segmentation of Crack Detection (균열 탐지의 의미론적 분할을 위한 Mean Teacher 학습 구조 최적화 )

  • Seungbo Shim
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.5
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    • pp.113-119
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    • 2023
  • Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.