• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.477-481
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• 2010

In this paper, we try to classify behavior patterns of a person around a bed based on a wireless sensor network system. We define five behavioral patterns and three states of a person around a bed which is described by a state machine. We collected data sensed by motion detection and vibration sensors installed around a bed from which a feature vector was extracted. Based on feature vector corresponding to behavioral patterns and the state machine, we established a model for behavioral patterns. To validate the model, experiments on subjects were performed and the model was fixed. These experimental results revealed that behavior patterns of a person around a bed can be classified well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.705-709
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• 2005

In this paper, we introduce fuzzy algorithm similar to human's way of thinking and designed collision detection system of vehicles. First, before the model vehicles design, we did simulation collision detection using PID and Fuzzy Controller. As a result, P.O that is Percent Overshoot when make use of PID controller happened from smallest 32% to 45%. But, In case of using fuzzy controller they produced about 10% in 7% in case use 25 rule. We designed model vehicles that introduce Auto Guided Vehicle(AGV) with confirmed result in simulation. We set Polaroid 6500 sensor on the front of model automobile because distinguish existence automobile to the head. And we composed motor drive part to run vehicles and 80C196KC processor for control movement of vehicles influenced on distance data of the front vehicles that receive from supersonic waves sensor. In case of using Fuzzy controller, last value percent error happened about maximum 15% in smallest 5%, and we confirmed that distance with front vehicles kept when state hold time is about maximum 16 seconds in smallest 10 seconds.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.3
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• pp.8-13
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• 2012

In this paper, we investigate cooperative spectrum sensing scheme for sensor network-aided cognitive radio systems with energy harvesting capability. In the proposed model, each sensor node harvests ambient energy from environment such as solar, wind, mechanical vibration, or thermoelectric effect. We propose adaptive cooperative spectrum sensing scheme in which each sensor node adaptively carries out energy detection depending on the residual energy in its energy storage and then conveys the sensing result to the fusion center. From simulation results, we show that the proposed scheme minimizes the false alarm probability for given target detection probability by adjusting the number of samples for energy detector.

• Smart Media Journal
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• v.13 no.4
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• pp.23-32
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• 2024

Large-scale fires and their consequential damages are becoming increasingly common, but confidence in fire detection systems is waning. Recently, widely-used chemical fire detectors frequently generate lots of false alarms, while video-based deep learning fire detection is hampered by its time-consuming and expensive nature. To tackle these issues, this study proposes a fire detection model utilizing an autoencoder approach. The objective is to minimize false alarms while achieving swift and precise fire detection. The proposed model, employing an autoencoder methodology, can exclusively learn from normal data without the need for fire-related data, thus enhancing its adaptability to diverse environments. By amalgamating data from five distinct sensors, it facilitates rapid and accurate fire detection. Through experiments with various hyperparameter combinations, the proposed model demonstrated that out of 14 scenarios, only one encountered false alarm issues. Experimental results underscore its potential to curtail fire-related losses and bolster the reliability of fire detection systems.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.588-590
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• 2006

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto-associative kernel regression by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.493-496
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• 2010

At present, Metal Oxide gas Sensors (MOXs) are widely used in gas detection because of its advantages, including high sensitivity and low cost. However, MOX presents well-known problems, including lack of selectivity and environment effect, which has motivated studies on different measurement strategies and signal-processing algorithms. In this paper, we present an artificial neural network (ANN) that models an MOX sensor (TGS822) used in a dynamic environment. This model takes into account dependence in relative humidity and in gas nature. Using MATLAB interface in the design phase and optimization, the proposed model is implemented as a component in an electronic simulator library and accurately expressed the nonlinear character of the response and that its dependence on temperature and relative humidity were higher than gas nature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.625-626
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• 2013

This paper introduces a new method that detects human motions using a Kinect sensor. Also this paper describes a method to mimic the detected human motions. We first build a human stick model by processing the output of Kinect sensor. We detect a specific motion by using the position of each joint of the human stick model and by using the angles between joints.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.2
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• pp.125-134
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• 2022

Recently, there are an increased the number of reports on the misuse or malicious use of an UAS. Thus, many researchers are studying on defense schemes for UAS by developing or improving C-UAS sensor technology. However, the wrong placement of sensors may lead to a defense failure since the proper placement of sensors is critical for UAS defense. In this study, a multi-object optimization model for C-UAS sensor placement in an air base is proposed. To address the issue, we define two objective functions: the intersection ratio of interested area and the minimum detection range and try to find the optimized placement of sensors that maximizes the two functions. C-UAS placement model is designed using a NSGA-II algorithm, and through experiments and analyses the possibility of its optimization is verified.

• Journal of Sensor Science and Technology
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• v.5 no.4
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• pp.47-56
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• 1996

The basic principle and characteristics of a DTG(dynamically tuned gyroscope) are presented in this paper, which is used for the detection of disturbance and for the stabilization of gimbal. An accurate model of the rate mode DTG is proposed. This model has a resonance characteristics which is more similar to the characteristics of practical systems than the conventional 2nd order system model. Therefore, this model is applicable to the general rate mode gyroscope. Some problems at using DTG for a real electro optical tracking system are discussed and a solution is described.