• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.854-856
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• 2014

Has been released of make it possible to control the using for smart devices of a wide variety home appliances and electronics in smart appliances in accordance with the one person multi devices. In addition, is increasing rapidly for the number of the product on cleaning robot and refrigerator, air conditioning, TV, etc. these devices are using the implement up DLNA system. And at home and abroad for development and has provided with Iot and Alljoyn such systems. But currently using home appliances or electronic devices of there are a lot of the operating system non installed than the installed products. In addition, smart appliances do not use for user than buying existing electronic products a lot more. In addition, more occur for smart appliances of that do not use for the user on smart appliances rather than buying existing electronics. In this paper, Suggested and implemented for system of control such as smart devices to existed home appliance on not have an operating system, Using mobile device for want users to quantify the data to transfer from arduino board.

• Journal of Advanced Navigation Technology
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• v.23 no.3
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• pp.228-236
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• 2019

Recently, multi-sensor integration techniques have been actively studied to obtain reliable and accurate navigation solution in GPS (Global Positioning System)-denied harsh environments such as urban canyons, tunnels, and underground roads. In this paper, we propose a low-cost ultrasonic-speedometer utilizing the characteristics of the ultrasonic propagation. An efficient integrated INS (inertial navigation system)/ultrasonic-speedometer navigation system is also proposed to improve the accuracy of positioning in GPS-denied environments. To evaluate the proposed system, car experiments with field-collected measurements were performed. By the experiment results, it was confirmed that the proposed INS/ultrasonic-speedometer system bounds the positioning error growth effectively even though GPS signal is blocked more than 10 seconds and a low-cost MEMS IMU (micro electro mechanical systems inertial measurement unit) is utilized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1088-1095
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• 2019

The information and communication technology that is being developed recently has been greatly influencing the automobile market. In recent years, devices equipped with IT technology have been installed for the safety and convenience of the driver. However, it has the advantage of increased convenience as well as the disadvantage of increasing traffic accidents due to driver's distraction. In order to prevent such accidents, it is necessary to develop safety systems of various types and ways. In this paper implements a platform that can recognize LDWS and FCWS and PDWS by using a single camera without using radar sensor and camera fusion and stereo camera method using two or more sensors, and proposes to study multi-function driving safety platform using a single camera by analyzing recognition rate evaluation and validity on a vehicle.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.427-432
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• 2019

In this paper, we propose a home monitoring system using sound event detection based on a bidirectional gated recurrent neural network for the hard-of-hearing. First, in the proposed system, packet loss concealment is used to recover a lost signal captured through wireless sensor networks, and reliable channels are selected using multi-channel cross correlation coefficient for effective sound event detection. The detected sound event is converted into the text and haptic signal through a harmonic/percussive sound source separation method to be provided to hearing impaired people. Experimental results show that the performance of the proposed sound event detection method is superior to the conventional methods and the sound can be expressed into detailed haptic signal using the source separation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.637-644
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• 2021

In general, in the fields of computer vision, robotics, and augmented reality, the importance of 3D space and 3D object detection and recognition technology has emerged. In particular, since it is possible to acquire RGB images and depth images in real time through an image sensor using Microsoft Kinect method, many changes have been made to object detection, tracking and recognition studies. In this paper, we propose a method to improve the quality of 3D reconstructed images by processing images acquired through a depth-based (RGB-Depth) camera on a multi-view camera system. In this paper, a method of removing noise outside an object by applying a mask acquired from a color image and a method of applying a combined filtering operation to obtain the difference in depth information between pixels inside the object is proposed. Through each experiment result, it was confirmed that the proposed method can effectively remove noise and improve the quality of 3D reconstructed image.

• Applied Chemistry for Engineering
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• v.32 no.3
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• pp.229-236
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• 2021

During the last two decades, metal-organic frameworks (MOFs) have been drawn attention due to their high specific surface area, porosity, and catalytic activities that allow to use in many applications such as sensor, catalysis, energy storage, etc. To synthesize MOFs hydrothermal or solvothermal method were generally used. However, these methods require high-cost equipment and long time-spend for the synthesis with multi-step process. In contrast, electrochemical synthesis has been considered as a simple and easy process under the ambient conditions. In this review, we described the mechanism of electrochemical MOFs synthesis by the number of configured electrodes system, with the recent reports of various applications.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.73-78
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• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2614-2624
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• 2022

Monte Carlo simulations were used to model a portable Neutron backscattering (NBT) sensor suitable for detecting plastic anti-personnel mines (APMs) buried in dry and moist soils. The model consists of a 100 MBq ²⁵²Cf source encapsulated in a neutron reflector/shield assembly and centered between two ³He detectors. Multi-parameter optimization was performed to investigate the efficiency of Be/Zr(BH₄)₄ and Be/Be(BH₄)₂ assemblies in terms of increasing the signal-to-background (S/B) ratio and reducing the total dose equivalent rate. The MCNP results showed that 2 cm Be/3 cm Zr(BH₄)₄ and 2 cm Be/3 cm Be(BH₄)₂ are the optimal configurations. However, due to portability requirements and abundance of Be, the ²⁵²Cf-2 cm Be/3 cm Be(BH₄)₂ NBT model was selected to scan the center of APM buried 3 cm deep in dry and moist soils. The selected NBT model has positively identified the APM with a S/B ratio of 886 for dry soils of 1 wt% hydrogen content and with S/B ratios of 615, 398, 86, and 12 for the moist soils containing 4, 6, 10, and 14 wt% hydrogen, respectively. The total dose equivalent rate reached 0.0031 mSv/h, suggesting a work load of 8 h/day for 806 days within the permissible annual dose limit of 20 mSv.

• 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.