• Journal of Veterinary Science
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• v.21 no.2
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• pp.14.1-14.10
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• 2020

African swine fever (ASF) is a highly contagious disease of domestic pigs and wild boars (WBs). Without a vaccine, early antibody and antigen detection and rapid diagnosis are crucial for the effective prevention of the disease and the employment of control measures. In Sardinia, where 3 different suid populations coexisted closely for a long time, the disease persists since 1978. The recent ASF eradication plan involves more stringent measures to combat free-ranging pigs and any kind of illegality in the pig industry. However, critical issues such as the low level of hunter cooperation with veterinary services and the time required for ASF detection in the WBs killed during the hunting season still remain. Considering the need to deliver true ASF negative carcasses as early as possible, this study focuses on the evaluation and validation of a duplex pen-side test that simultaneously detects antibodies and antigens specific to ASF virus, to improve molecular diagnosis under field conditions. The main goal was to establish the specificity of the two pen-side tests performed simultaneously and to determine their ability to detect the true ASF negative carcasses among the hunted WBs. Blood and organ samples of the WBs hunted during the 2018/2019 hunting seasons were obtained. A total of 160 animals were tested using the pen-side kit test; samples were collected for virological and serological analyses. A specificity of 98% was observed considering the official laboratory tests as gold standards. The new diagnostic techniques could facilitate faster and cost-effective control of the disease.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1130-1133
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• 2003

The goal of this research is developing advanced lane detecting algorithm for unmanned vehicle. Previous lane detecting method to bring on error become of the lane loss and noise. Therefore, new algorithm developed to get exact information of lane. This algorithm can be used to AGV(Autonomous Guide Vehicle) and LSWS(Lane Departure Warning System), ACC(Adapted Cruise Control). We used 1/10 scale RC car to embody developed algorithm. A CCD camera is installed on top of vehicle. Images are transmitted to a main computer though wireless video transmitter. A main computer finds information of lane in road image. And it calculates control value of vehicle and transmit these to vehicle. This algorithm can detect in input image marked by 256 gray levels to get exact information of lane. To find the driving direction of vehicle, it search line equation by curve fitting of detected pixel. Finally, author used median filtering method to removal of noise and used characteristic part of road image for advanced of processing time.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.347-357
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• 2013

Cyber-Physical system(CPS) is characterized by collaborating computational elements controlling physical entities. In CPS, human desire to acquire useful information and control devices anytime and anywhere automatically has increased the necessity of a high reliable system. However, the physical world where CPS is deployed has management complexity and maintenance cost of 'CPS', so that it is impossible to make reliable systems. Thus, this paper presents an 'Autonomic Control System towards High-reliable Cyber-Physical Systems' that comprise 8-steps including 'fault analysis', 'fault event analysis', 'fault modeling', 'fault state interpretation', 'fault strategy decision', 'fault detection', 'diagnosis&reasoning' and 'maneuver execution'. Through these activities, we fascinate to design and implement 'Autonomic control system' than before. As a proof of the approach, we used a ISR(Intelligent Service Robot) for case study. The experimental results show that it achieves to detect a fault event for autonomic control of 'CPS'.

• ETRI Journal
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• v.37 no.1
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• pp.11-20
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• 2015

This paper deals with optimal power allocation for channel estimation of orthogonal frequency-division multiplexing uplinks in time-varying channels. In the existing literature, the estimation of time-varying channel response in an uplink environment can be accomplished by estimating the corresponding channel parameters. Accordingly, the optimal power allocation studied in the literature has been in terms of minimizing the mean square error of the channel estimation. However, the final goal for channel estimation is to enable the application of coherent detection, which usually means high spectral efficiency. Therefore, it is more meaningful to optimize the power allocation in terms of capacity. In this paper, we investigate capacity with imperfect channel estimation. By exploiting the derived capacity expression, an optimal power allocation strategy is developed. With this developed power allocation strategy, improved performance can be observed, as demonstrated by the numerical results.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.501-507
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• 2012

Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. In this paper, we explain a study on natural user interface (NUI) in human hand motion recognition using RGB color information and depth information by Kinect camera from Microsoft Corporation. To achieve the goal, hand tracking and gesture recognition have no major dependencies of the work environment, lighting or users' skin color, libraries of particular use for natural interaction and Kinect device, which serves to provide RGB images of the environment and the depth map of the scene were used. An improved Camshift tracking algorithm is used to tracking hand motion, the experimental results show out it has better performance than Camshift algorithm, and it has higher stability and accuracy as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.1976-1996
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• 2011

Cognitive radio (CR) MAC protocol provides access control of unused spectrum resources without causing interference to primary users. To achieve this goal, in this paper a TDMA based cooperative multi-channel cognitive radio MAC (MCR-MAC) protocol is proposed for wireless ad hoc networks to provide reliable protection for primary users by achieving cooperative detection of incumbent system signals around the communication pair. Each CR node maintains transmission opportunity schedules and a list of available channels that is employed in the neighbor discovery period. To avoid possible signal collision between incumbent systems and cognitive radio ad hoc users, we propose a simple but efficient emergency notification message exchanging mechanism between neighbor CR nodes with little overhead. Our simulation results show that the proposed MCR-MAC can greatly reduce interference with primary users and remarkably improve the network throughput.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.331-336
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• 2008

In this paper, we propose a real-time people tracking system with multiple CCD cameras for security inside the building. To achieve this goal, we present a method for 3D walking human tracking based on the IMPRESARIO framework incorporating cascaded classifiers into hypothesis evaluation. The efficiency of adaptive selection of cascaded classifiers has been also presented. The camera is mounted from the ceiling of the laboratory so that the image data of the passing people are fully overlapped. The implemented system recognizes people movement along various directions. To track people even when their images are partially overlapped, the proposed system estimates and tracks a bounding box enclosing each person in the tracking region. The approximated convex hull of each individual in the tracking area is obtained to provide more accurate tracking information. We have shown the improvement of reliability for likelihood calculation by using cascaded classifiers. Experimental results show that the proposed method can smoothly and effectively detect and track walking humans through environments such as dense forests.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.828-831
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• 1996

Radiation monitoring system is needed at nuclear power plant and nuclear facility. Manual survey techniques are commonly used, but they are time consuming and somewhat inaccurate. Automatic radiation surveys are very important because it provides significant savings in men-rem and wages. Unmanned, remote automatic radiation measurement system should be small and light-weighted in order to mount on robotic system. The system we have developed consists of detection parts, signal processing part, interface, and software part. Position information is provided by using of a collimator. The measurement process is achieved by the scanning of detector and image processing techniques are used to display radiation levels. We designed collimators, detectors, signal processing circuit, and constructed prototype system. The goal of this system is the mapping of camera image and radiation level distribution.