• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.3
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• pp.23-29
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• 2008

It is necessary to develope the active safety system in terms of driver's safety and convenience. In this paper, we were developed the non-contact type of safety window system operated by the initial value of feedback control such as the output signal of photo sensor. It was designed based on the control algorithm with an improved load sensitivity. Therefore, compared with the existing system, it is possible to prevent the occurrence of a mull-function. Also, it has a convenient functions of the window such as an auto up/down and closing, and has a response times better. It can be installed the various types of common vehicles that have the different movement distance and speed of window. In conclusion, the developed system may be adapted the vehicle commercially.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.127-134
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• 2009

In this study, possibility of the laser eavesdropping is investigated on the window glasses with various thicknesses, Glass windows are excited by maximum length sequency (MLS) signal and the vibration sound is detected by a laser doppler vibrometer. From the detected sound, speech intelligibility is objectively estimated. Speech transmission index (STI), which is based on the modulation transfer function (MTF). is calculated for the estimation. Finally, disturbing wave effect on the speech intelligibility is analysed by using an outside speaker and a window shaker attached on the glass window. The purpose of the study is to estimate the possibility of remote eavesdropping by the laser sensor and to evaluate the performance of the homemade window shaker to protect from the remote eavesdropping.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.19 no.2
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• pp.61-65
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• 2013

Modified atmosphere (MA) of reduced $O_2$ and elevated $CO_2$ concentrations has been used for keeping the quality of fresh produce and extending the shelf life. As a way to attain the beneficial MA package around the produce, a gas diffusion tube or perforation can be attached onto the container and controlled on real time in its opening/closing responding to $O_2$ and $CO_2$ concentrations measured by gas sensors. The timely-controlled opening of the gas diffusion tube can work in harmony with the produce respiration and help to create the desired MA. By use of the mathematical modeling, the effect of tube dimension on the controlled container atmosphere was figured out in this study. Spinach and king oyster mushroom were used as typical commodities for designing the model container system (0.35 and 0.9 kg in 13 L, respectively) because of their respiration characteristics and the optimal MA condition ($O_2$ 7~10%/$CO_2$ 5~10% for spinach; $O_2$ 2~5%/$CO_2$ 10~15% for mushroom). With a control logic for the gas composition to stay as close as possible to optimum MA window without invading injurious low $O_2$ and/or high $CO_2$ concentrations, the atmosphere of the sensor-controlled container could stay at its lower $O_2$ boundary or upper $CO_2$ limit under certain tube dimensional conditions. There were found to be the ranges of the tube diameter and length allowing the beneficial MA. The desired range of the tube dimension for spinach consisted of combinations of larger diameter and shorter length in the window of 0.3~2 cm diameter and 0.2~10 cm length. Similarly, that for king oyster mushroom was combinations of larger diameter and shorter length in the window of 0.9~2 cm diameter and 0.2~3 cm in length. Clear picture on generally affordable tube dimension range may be formulated by further study on a wide variety of commodity and pack conditions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.400-403
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• 2008

In this work, an individual trespassing detector using a PIR sensor to detect infrared rays only between the range of $9.4{\sim}10.4{\mu}m$ radiated from the body is proposed. This detector using FIR sensor detects not insect or object but human body, It doesn't restrict the inhabitant's behavior because the filter of pm sensor is designed to have face angle and the detector only detects the window area. The existing wide angle filter, RIR sensor, detects $30^{\circ}$ angle while the face angle filter sensor on this paper detects $11^{\circ}$ angle with 3cm of face angle filter from 2m of detecting distance. In case of interruption of electric power, 250mAh of lithium-ion battery has worked for 10 hours consuming 22mA in normal state. Meanwhile, in case of interruption of electric power, 250mAh of battery has worked for 4 hours consuming 60mA in trespassing detecting state. Projector, receptor, controller and alarm are put on one PCB in order to make it convenient to install without any special installation skill.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.277-283
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• 2019

In this paper, we propose a distributed fusion estimation for sensor networks using a receding horizon strategy. Communication channels were modelled as Markov jump systems, and a posterior probability distribution for communication channel characteristics was calculated and incorporated into the filter to allow distributed fusion estimation to handle path loss observation situations automatically. To implement distributed fusion estimation, a Kalman-Consensus filter was then used to obtain the average consensus, based on the estimates of sensors randomly distributed across sensor networks. The advantages of the proposed algorithms were then verified using a large-scale sensor network example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.48-55
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• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.163-174
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• 2006

In distributed wireless sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. We propose a classification framework for continuous multivariate stream data. The proposed approach works in two steps. In the preprocessing step, it takes input as a sliding window of multivariate stream data and discretizes the data in the window into a string of symbols that characterize the signal changes. In the classification step, it uses a standard text classification algorithm to classify the discretized data in the window. We evaluated both supervised and unsupervised classification algorithms. For supervised, we tested Bayesian classifier and SVM, and for unsupervised, we tested Jaccard, TFIDF Jaro and Jaro Winkler. In our experiments, SVM and TFIDF outperformed other classification methods. In particular, we observed that classification accuracy is improved when the correlation of attributes is also considered along with the n-gram tokens of symbols.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1103-1109
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• 2014

The motion recognition system has been broadly studied in digital image and video processing fields. Recently, method using th depth image is used very useful. However, recognition accuracy of depth image based method will be loss caused by size and shape of object distorted for angle of the depth sensor. Therefore, distortion correction of depth sensor is positively necessary for distinguished performance of the recognition system. In this paper, we propose a pre-processing algorithm to improve the motion recognition system. Depth data from depth sensor converted to real world, performed the corrected angle, and then inverse converted to projective world. The proposed system make progress using the OpenCV and the window program, and we test a system using the Kinect in real time. In addition, designed using Verilog-HDL and verified through the Zynq-7000 FPGA Board of Xilinx.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.235-243
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• 2014

In this paper, we propose an efficient control system for intelligent LED indoor lighting. The proposed an efficient control system for intelligent LED indoor lighting were included to elements such as daylight intensity measured through the PIR sensor and illuminance sensor at lighting style by the schedule defined and the occupancy detection. And it was controlled lighting through to the wireless sensor network, and was designed for the energy savings. Also, the lighting control of indoor lighting based on occupancy detection detect fine movements using a PIR sensor. And an unnecessary lighting intensity control of the window-side and the inside were controlled according to daylight level measurement result using the light sensor. In daylight inflow many case, the window-side lighting was to automatically darker, and in daylight inflow less case, was designed to be automatically bright. The efficiency validate results of an efficient control system for intelligent LED indoor lighting, the brightness of the indoor light were to maximize the energy saving by controlling in real time when entering as indoor a little that external lighting or daylight.