• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.49-54
• /
• 2016

The IoT has attracted attention as one of the technologies that are applied to various industries and create new services. The IoT can utilize existing network technologies to create services by providing Internet connection between objects. Objects Personalized services can be created by collecting various data using the IoT. In the field of agriculture, we are promoting sustainable agriculture and enhancing competitiveness through the use of the IoT, and the convergence of IoT in agriculture is pushing for smart agriculture. In Korea, the Ministry of Agriculture, Food and Rural Affairs is preparing measures to spread smart farms to improve agricultural competitiveness using IoT technology. Therefore, we propose the development model of smart agriculture in the future through the case study on the IoT based on agriculture.

• Journal of IKEEE
• /
• v.18 no.1
• /
• pp.57-63
• /
• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. The self-Localization of indoor mobile robot is very important for the sophisticated trajectory control. In this paper, the robot's self-localization algorithm is designed by RF sensor network and fuzzy inference. The robot realizes its self-localization, using RFID sensors, through the collaboration algorithm which uses fuzzy inference for combining the strengths of triangulation and triangulation. For the triangulation self-Localization, RSSI is implemented. TOA method is used for realizing the triangulation self-localization. The final improved position is, through fuzzy inference, made by the fusion algorithm of the resultant coordinates from trilateration and triangulation in real time. In this paper, good performance of the proposed self-localization algorithm is confirmed through the results of a variety of experiments in the base of RFID sensor network and reader system.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1273-1279
• /
• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Journal of Korean Society for Geospatial Information Science
• /
• v.13 no.3 s.33
• /
• pp.59-67
• /
• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Journal of Sensor Science and Technology
• /
• v.22 no.2
• /
• pp.144-149
• /
• 2013

In this research, we developed a biosensor to detect lung cancer-specific biomarker using Anodic Aluminum Oxide (AAO) chip based on interference and nano surface plasmon resonance (nanoSPR). The nano-porous AAO chip was fabricated $2{\mu}m$ of pore-depth by two-step anodizing method for surface uniformity. NanoSPR has sensitivity to the refractive index (RI) of the surrounding medium and also provides simple and label-free detection when specific antibodies are immobilized to the Au-deposited surface of nano-porous AAO chip. To detect the lung cancer-specific biomarker, antibodies were immobilized on the surface of the chip by Self Assembled Monolayer (SAM) method. Since then lung cancer-specific biomarker was applied atop the antibodies immobilized layer. The specific reaction of the antigen-antibody contributed to the change in the refractive index that cause shift of resonance spectrum in the interference pattern. The Limit of Detection (LOD) was 1 fg/ml by using our nano-porous AAO biosensor chip.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.24 no.2
• /
• pp.166-171
• /
• 2011

We use the electrical energy and it is essential energy in modern life, but we lay cable underground due to the issue for environment and safety. Safety for worker is still insufficient for the development of safety equipment and related research has been focused on the cable lifetime diagnosis at underground cable work. I have to develop live line detector around the magnetic field were investigated at underground cable. In this paper, we were investigated by variation of coil turns and load due to detection of magnetic field at lines around. And detected value of developing products compared with measured value of milli-gauss meter. As a result, the value of the number of coil turns was found to be proportional to the measured value. But turn-numbers increase showed that the weak noise. I could be confirmed that sensor showed the optimum value from 4,000 to 5,0000.

• Journal of Sensor Science and Technology
• /
• v.29 no.3
• /
• pp.172-179
• /
• 2020

We studied an integrated-optic biosensor configuration that operates at a wavelength of 0.63 ㎛ based on the evanescent-wave and two horizontal mode power coupling of Si₃N₄ rib-optical waveguides formed on a Si/SiO₂/Si₃N₄/SiO₂ multilayer thin films. The sensor consists of a single-mode input waveguide, followed by a two-mode section which acts as the sensing region, and a Y-branch output for separating the two output waveguides. The coupling between the two propagating modes in the sensing region produces a periodically repeated optical power exchanges along the propagation. A light power was steered from one output channel to the other due to the change in the cladding layer (bio-material) refractive index, which affected the effective refractive index (phase-shift) of two modes through evanescent-wave. Waveguide analyses based on the rib optical waveguide dimensions were performed using various numerical computational software. Sensitivity values of 12~23 and 65~165 au/RIU, respectively for the width and length of 4 ㎛, and 3841.46 and 26250 ㎛ of the two-mode region corresponding to the refractive index range 1.36~1.43 and 1.398~1.41, respectively, were obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.7
• /
• pp.2304-2320
• /
• 2021

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.344-352
• /
• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.9
• /
• pp.751-756
• /
• 2012

In this paper, we propose a foreground extraction and depth map generation method using a time-of-flight (TOF) depth camera. Although, the TOF depth camera captures the scene's depth information in real-time, it has a built-in noise and distortion. Therefore, we perform several preprocessing steps such as image enhancement, segmentation, and 3D warping, and then use the TOF depth data to generate the depth-discontinuity regions. Then, we extract the foreground object and generate the depth map as of the color image. The experimental results show that the proposed method efficiently generates the depth map even for the object boundary and textureless regions.