• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.117-126
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• 2019

The data generated in the IoT environment is very diverse. Especially, the development of the fourth industrial revolution has made it possible to increase the number of fixed and unstructured data generated in manufacturing facilities such as Smart Factory. With Big Data related solutions, it is possible to collect, store, process, analyze and visualize various large volumes of data quickly and accurately. Therefore, in this paper, we will directly generate data using Raspberry Pi used in IoT environment, and analyze using various Big Data solutions. Collected by using an Sqoop solution collected and stored in the database to the HDFS, and the process is to process the data by using the solutions available Hive parallel processing is associated with Hadoop. Finally, the analysis and visualization of the processed data via the R programming will be used universally to end verification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.279-287
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• 2019

In this paper, we attempted to analyze the balance ability of the body by measuring changes in body motion and plantar pressure distribution. So we developed a program that can measure and analyze range of motion and center of body pressure using inertial measurement unit(IMU) and FSR(Force Sensing Resistor) sensor, we also produced a contents that can help improve the balance ability. The quantitative values of range of motion and center of body pressure measured by this program are visualized in real time so that the user can easily recognize the results. In addition, the contents were designed to be adjusted according to the direction of improving the balance ability by adjusting the difficulty level based on the measured balance information. This can be achieved by increasing the concentration and participation will by using visual feedback method that proceeds while watching moving objects according to the user's motion.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.83-92
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• 2021

There are continuous and tremendous attempts to apply various sensor systems and ICTs into the dairy science for data accumulation and improvement of dairy productivity. However, these only concerns the fields which directly affect to the dairy productivity such as the number of individuals and the milk production amount, while researches on the physiology aspects of dairy cows are not enough which are fundamentally involved in the dairy productivity. This paper proposes the basic approach for extraction of daily behavior pattern from hourly behavioral data of dairy cows to identify the health status and stress. Total four clusters were grouped by k-means clustering and the reasonability was proved by visualization of the data in each groups and the representatives of each groups. We hope that provided results should lead to the further researches on catching abnormalities and disease signs of dairy cows.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.169-176
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• 2018

In this paper, we consider duty-cycled wireless sensor networks (WSNs) in which sensor nodes are periodically dormant in order to reduce energy consumption. In such networks, as the duty cycle interval increases, the energy consumption decreases. However, a higher duty cycle interval leads to the increase in the end-to-end (E2E) delay. Many applications of WSNs are delay-sensitive and require packets to be delivered from the sensr nodes to the sink with delay requirements. Most of existing studies focus on only reducing the E2E delay, rather than considering the delay bound requirement, which makes hard to achieve the balanced performance between E2E delay and energy consumption. A few study that considered delay bound requirement require time synchronization between neighboring nodes or a specific distribution of deployed nodes. In order to address limitations of existing works, we propose a duty-cycle scheduling algorithm that aims to achieve low energy consumption, while satisfying the delay requirements. To that end, we first estimate the probability distribution for the E2E delay. Then, by using the obtained distribution we determine the maximal duty cycle interval that still satisfies the delay constraint. Simulation results show that the proposed design can satisfy the given delay bound requirements while achieving low energy consumption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of Sensor Science and Technology
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• v.15 no.2
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• pp.112-119
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• 2006

Resolution of an image sensor is very significant parameter to improve. It is hard to improve the resolution of the CMOS vision chip for edge detection based on a biological retina using a resistive network because the vision chip contains additional circuits such as a resistive network and some processing circuits comparing with general image sensors such as CMOS image sensor (CIS). In this paper, we proved the problem of low resolution by separating photo-sensing and signal processing circuits. This type of vision chips occurs a problem of low operation speed because the signal processing circuits should be commonly used in a row of the photo-sensors. The low speed problem of operation was proved by using a reset decoder. A vision chip for edge detection with $128{\times}128$ pixel array has been designed and fabricated by using $0.35{\mu}m$ 2-poly 4-metal CMOS technology. The fabricated chip was integrated with optical lens as a camera system and investigated with real image. By using this chip, we could achieved sufficient edge images for real application.

• Journal of Korea Multimedia Society
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• v.5 no.6
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• pp.646-654
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• 2002

The human sense of touch provides us with an important source of information about our surroundings. Because of its unique position at interface between our bodies and the out world, touch sensation supplies sensory data which helps us manipulate and recognize objects and warn of harmful situation. But tactile sensation was recognized less important than visual sense and auditory sense but it plays an important immersing role in virtual reality and computer game. Tactile sensation can be used to influence to objects according to power and supplied sensory feedback to the player in a virtual environment. This paper investigated the characteristics of tactile sensation of human being and proposed method of sturdy using force sensing sensor, simple force modeling and data structure form for virtual reality and computer game. As a result, force distribution, depth, center point can be calculated using sensor output and this information is very effective to specific position for actions and reactions. This study can used as basic information for tactile sensation and it's application in computer game and virtual realty.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.227-239
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• 2014

This study aims to evaluate performance of various image fusion methods based on the spectral responses of high-resolution optical satellite sensors such as KOMPSAT-2, QuickBird and WorldView-2. The image fusion methods used in this study are GIHS, GIHSA, GS1 and AIHS. A quality evaluation of each image fusion method was performed with both quantitative and visual analysis. The quantitative analysis was carried out using spectral angle mapper index (SAM), relative global dimensional error (spectral ERGAS) and image quality index (Q4). The results indicates that the GIHSA method is slightly better than other methods for KOMPSAT-2 images. On the other hand, the GS1 method is suitable for Quickbird and WorldView-2 images.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.59-70
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• 2010

Each pixels become got pixel value for color of only one from among colors because of bayer pattern that light receiving device of image sensor which is used in HHP and digital camera writes only one color. Information of the missing pixels could infer perfect color image from using information of neighbor pixels by using CFA(Color Filter Array). In this paper, we derive relation between the average of the data from the light receiving device of image sensor and each color channel data. And by using this relation, a new efficient edge color fuzzy method for color interpolation is proposed. Also, missing luminance signal channel interpolation was fuzzy interpolation along any edges direction for reducing color noise and interpolating efficiently it. And in this paper, the proposed method has been proved improving average 2.4dB than the conventional method by using PSNR. Also, resolution of the image of the proposed method was similar to the original image by visual images, we has been verified to be decreased a chromatic aberration than image of conventional algorithms with simulation result.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.