• Journal of Sensor Science and Technology
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• 제8권1호
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• pp.63-68
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• 1999

We proposed a method of eye pattern detection in the 2-D image which was obtained by CCD video camera. To detect face region and eye pattern, we proposed pattern search network and batch SVD algorithm which had the statistical equivalence of PCA. We also used HMM to improve the accuracy of detection. As a result, we acknowledged that the proposed algorithm was superior to PCA pattern detection algorithm in computational cost and accuracy of defection. Furthermore, we evaluated that the proposed algorithm was possible in real-time face pattern detection with 2 frame images per second.

• Journal of the Korea Institute of Military Science and Technology
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• 제18권5호
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• pp.538-547
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• 2015

The goal of this study is to develop an algorithm to propose optimal deployment of detection sensor nodes in the target area, based on a performance surface, which represents detection performance of active and passive acoustic sonar systems. The performance surface of the active detection system is calculated from the azimuthal average of maximum detection ranges, which is estimated with a transmission loss and a reverberation level predicted using ray-based theories. The performance surface of the passive system is calculated using the transmission loss model based on a parabolic equation. The optimization of deployment configurations is then performed by a hybrid method of a virtual force algorithm and a particle swarm optimization. Finally, the effectiveness of deployment configurations is analyzed and discussed with the simulation results obtained using the algorithm proposed in this paper.

• Nuclear Engineering and Technology
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• 제36권1호
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• pp.73-82
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• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 한국추진공학회 2011년도 제37회 추계학술대회논문집
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• pp.599-602
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• 2011

A propulsion controller for one-time flight vehicles should be designed robustly so that it can complete its missions even in case sensor failures. These vehicles improve their fault tolerance by back-up sensors prepared for the failure of major sensors, which raises the total cost. This paper presents the NARX model which substitutes vehicles' velocity sensors, and detects failure of sensor signals by using model based fault detection. The designed NARX model and fault detection algorithm were optimized and installed in TI's TMS320F2812 so that they were linked to HILS instruments in real-time. The designed propulsion controller made the vehicle to have better fault tolerance with fewer sensors and to complete its missions under a lot of complicated failure situations. The controller's applicability was finally confirmed by tests under the HILS environment.

• Journal of Magnetics
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• 제15권4호
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• pp.221-224
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• 2010

A laboratory sensor model was designed, constructed, and tested based on a newly proposed working principle of magnetic field detection. The principle of sensing employed a time-coded method in correlation with exploiting the advantageous features of the GMI effect. The sensor demonstrated a sensitivity of $10\;{\mu}s/{\mu}T$ in the field range of ${\pm}100\;{\mu}T$. The sensing element in the form of an amorphous thin wire, $100\;{\mu}m$ in diameter ${\times}50\;mm$ long, was fit into a small field modulation coil of 60 mm length. At a magnetic field modulation in the range of hundreds of Hz, the change in time interval of two adjacent GMI voltage peaks was linearly related to the external magnetic field to be measured. This mechanism improved the sensor linearity of the GMI sensor to better than 0.2% in the measuring range of ${\pm}100\;{\mu}T$.

• Journal of the Institute of Electronics and Information Engineers
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• 제53권2호
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• pp.133-140
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• 2016

In active sonar field, a target detection and localization based on a distributed sensor network has been much studied for the underwater surveillance of the coast. Zhou et al. proposed a target localization method utilizing the positions of target-detected sensors in distributed sensor network which consists of detection-only sensors. In contrast with a conventional method, Zhou's method dose not require to estimate the propagation model parameters of detection signal. Also it needs the lower computational complexity, and to transmit less data between network nodes. However, it has large target localization error. So it has been modified for reducing localization error by Ryu. Modified Zhou's method has better estimation performance than Zhou's method, but still relatively large estimation error. In this paper, a target localization method based on modified Zhou's method is proposed for reducing the localization error. The proposed method utilizes the geometry of the positions of target-detected sensors and a line that represents the bearing of target, a line can be found by modified Zhou's method. This paper shows that the proposed method has better target position estimation performance than Zhou's and modified Zhou's method by computer simulations.

• Journal of IKEEE
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• 제20권4호
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• pp.449-453
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• 2016

Failures would occur because of the hostile nature environment in Wireless Sensor Networks (WSNs) which is deployed randomly. Therefore, considering faults in WSNs is essential when we design WSN. This paper classified fault model in the sensor node. Especially, this paper proposed new error correcting code scheme and fault recovery algorithm in the CH(Cluster Head) node. For the range of the small size information (<16), the parity size of the proposed code scheme has the same parity length compared with the Hamming code, and it has a benefit to generate code word very simple way. This is very essential to maintain reliability in WSN with increase power efficiency.

• Journal of Korea Multimedia Society
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• 제19권12호
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• pp.1927-1935
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• 2016

This paper propose a framework for human body parts in RGB-D image. We conduct tasks of obtaining person area, finding candidate areas and local detection in order to detect hand, foot and head which have features of long accumulative geodesic distance. A person area is obtained with background subtraction and noise removal by using depth image which is robust to illumination change. Finding candidate areas performs construction of graph model which allows us to measure accumulative geodesic distance for the candidates. Instead of raw depth map, our approach constructs graph model with segmented regions by quadtree structure to improve searching time for the candidates. Local detection uses HOG based SVM for each parts, and head is detected for the first time. To minimize false detections for hand and foot parts, the candidates are classified with upper or lower body using the head position and properties of geodesic distance. Then, detect hand and foot with the local detectors. We evaluate our algorithm with datasets collected Kinect v2 sensor, and our approach shows good performance for head, hand and foot detection.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.410-413
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• 2008

Existing Outlier detections have been widely studied in geosensor networks. Recently, machine learning and data mining have been applied the outlier detection method to build a model that distinguishes outliers based on anchored criterion. However, it is difficult for the existing methods to detect outliers against incoming time-variant data, because outlier detection needs to monitor incoming data and classify irregular attacks. Therefore, in order to solve the problem, we propose a time-variant outlier detection using 2-dimensional grid method based on unanchored criterion. In the paper, outliers using geosensor data was performed to classify efficiently. The proposed method can be utilized applications such as network intrusion detection, stock market analysis, and error data detection in bank account.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.