• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.859-873
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• 2023

Wildfires cause a lot of environmental and economic damage to the Earth over time. Various experiments have examined the harmful effects of wildfires. Also, studies for detecting wildfires and pollutant emissions using satellite remote sensing have been conducted for many years. The wildfire product for the Geostationary Environmental Monitoring Spectrometer (GEMS), Korea's first environmental satellite sensor, has not been provided yet. In this study, a false-color composite for better expression of wildfire smoke was created from GEMS and used in a U-Net model for wildfire detection. Then, a classification model was constructed to distinguish yellow dust from the wildfire smoke candidate pixels. The proposed method can contribute to disaster monitoring using GEMS images.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.827-835
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• 2023

Drone light detection and ranging (LiDAR) is a state-of-the-art surveying technology that enables close investigation of the top of the mountain slope or the inaccessible slope, and is being used for field surveys in mountainous terrain. To build topographic information using Drone LiDAR, a preprocessing process is required to effectively separate ground and non-ground points from the acquired point cloud. Therefore, in this study, the point group data of the mountain topography was acquired using an aerial LiDAR mounted on a commercial drone, and the application and accuracy of the cloth simulation filtering algorithm, one of the ground separation techniques, was verified. As a result of applying the algorithm, the separation accuracy of the ground and the non-ground was 84.3%, and the kappa coefficient was 0.71, and drone LiDAR data could be effectively used for landslide field surveys in mountainous terrain.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.300-308
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• 2006

$\underline{Purpose}$: To develop a wireless CCTV system in semi-beam's eye view (BEV) to monitor daily patient setup in radiation therapy. $\underline{Materials\;and\;Methods}$: In order to get patient images in semi-BEV, CCTV cameras are installed in a custom-made acrylic applicator below the treatment head of a linear accelerator. The images from the cameras are transmitted via radio frequency signal (${\sim}2.4\;GHz$ and 10 mW RF output). An expected problem with this system is radio frequency interference, which is solved utilizing RF shielding with Cu foils and median filtering software. The images are analyzed by our custom-made software. In the software, three anatomical landmarks in the patient surface are indicated by a user, then automatically the 3 dimensional structures are obtained and registered by utilizing a localization procedure consisting mainly of stereo matching algorithm and Gauss-Newton optimization. This algorithm is applied to phantom images to investigate the setup accuracy. Respiratory gating system is also researched with real-time image processing. A line-laser marker projected on a patient's surface is extracted by binary image processing and the breath pattern is calculated and displayed in real-time. $\underline{Results}$: More than 80% of the camera noises from the linear accelerator are eliminated by wrapping the camera with copper foils. The accuracy of the localization procedure is found to be on the order of $1.5{\pm}0.7\;mm$ with a point phantom and sub-millimeters and degrees with a custom-made head/neck phantom. With line-laser marker, real-time respiratory monitoring is possible in the delay time of ${\sim}0.17\;sec$. $\underline{Conclusion}$: The wireless CCTV camera system is the novel tool which can monitor daily patient setups. The feasibility of respiratory gating system with the wireless CCTV is hopeful.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.58-68
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• 2006

This work proposes a 10b 250MS/s $1.8mm^2$ 85mW 0.13um CMOS A/D Converter (ADC) for high-performance integrated systems such as next-generation DTV and WLAN simultaneously requiring low voltage, low power, and small area at high speed. The proposed 3-stage pipeline ADC minimizes chip area and power dissipation at the target resolution and sampling rate. The input SHA maintains 10b resolution with either gate-bootstrapped sampling switches or nominal CMOS sampling switches. The SHA and two MDACs based on a conventional 2-stage amplifier employ optimized trans-conductance ratios of two amplifier stages to achieve the required DC gain, bandwidth, and phase margin. The proposed signal insensitive 3-D fully symmetric capacitor layout reduces the device mismatch of two MDACs. The low-noise on-chip current and voltage references can choose optional off-chip voltage references. The prototype ADC is implemented in a 0.13um 1P8M CMOS process. The measured DNL and INL are within 0.24LSB and 0.35LSB while the ADC shows a maximum SNDR of 54dB and 48dB and a maximum SFDR of 67dB and 61dB at 200MS/s and 250MS/s, respectively. The ADC with an active die area of $1.8mm^2$ consumes 85mW at 250MS/s at a 1.2V supply.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Journal of IKEEE
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• v.18 no.4
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• pp.552-558
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• 2014

This paper suggests how to design a ZigBee-chip-based communication module to remotely measure radiation level. The suggested communication module consists of two control processors for the chip as generally required to configure a ZigBee system, and one chip module to configure a ZigBee RF device. The ZigBee-chip-based communication module for remote radiation measurement consists of a wireless communication controller; sensor and high-voltage generator; charger and power supply circuit; wired communication part; and RF circuit and antenna. The wireless communication controller is to control wireless communication for ZigBee and to measure radiation level remotely. The sensor and high-voltage generator generates 500 V in two consecutive series to amplify and filter pulses of radiation detected by G-M Tube. The charger and power supply circuit part is to charge lithium-ion battery and supply power to one-chip processors. The wired communication part serves as a RS-485/422 interface to enable USB interface and wired remote communication for interfacing with PC and debugging. RF circuit and antenna applies an RLC passive component for chip antenna to configure BALUN and antenna impedance matching circuit, allowing wireless communication. After configuring the ZigBee-chip-based communication module, tests were conducted to measure radiation level remotely: data were successfully transmitted in 10-meter and 100-meter distances, measuring radiation level in a remote condition. The communication module allows an environment where radiation level can be remotely measured in an economically beneficial way as it not only consumes less electricity but also costs less. By securing linearity of a radiation measuring device and by minimizing the device itself, it is possible to set up an environment where radiation can be measured in a reliable manner, and radiation level is monitored real-time.