• Title/Summary/Keyword: 보정 정보

Search Result 2,527, Processing Time 0.031 seconds

Quantity Estimation Method for High-Performance Insulated Wall Panels with Complex Details Using BIM Family Libraries (BIM의 패밀리 라이브러리를 이용한 복잡한 상세를 갖는 고단열 벽체 판넬의 물량 산출 방법)

  • Mun, Ju-Hyun
    • Journal of the Korea Institute of Building Construction
    • /
    • v.24 no.4
    • /
    • pp.447-458
    • /
    • 2024
  • This study investigates the effectiveness of Building Information Modeling(BIM) software, specifically SketchUp and Revit, in reducing errors during quantity take-off(QTO) for complex building elements. While 3D modeling offers advantages, existing software may not fully account for manufacturing discrepancies, such as variations in concrete cover thickness and reinforcing bar radius. To address this limitation, this research proposes a BIM-based QTO method for high-insulation wall panels with intricate details. The method utilizes a BIM family library, focusing on key parameters like concrete cover thickness and inner radius of shear reinforcement. A case study compared the cross-sectional details of a wall panel modeled in Revit with the actual manufactured specimen. The analysis revealed a 12% reduction in modeled concrete cover thickness and a 1.27 times larger modeled inner radius of the shear bar compared to the real-world values. The proposed method incorporates these manufacturing variations into the Revit model of the high-insulation wall panel. Software like Navisworks facilitates the identification and correction of any material interferences arising from these adjustments. Furthermore, the method employs a unit wall concept(1m2) to account for the volume of various materials, including insulation and splice sleeves at joints. This allows for the identification of a similar existing family within the BIM library(e.g., "Double RC wall with embedded insulation") that reflects the actual material quantities used in the wall panel. By incorporating these manufacturing-induced variations, the proposed method offers a more accurate QTO process for complex high-insulation wall panels. The "Double RC wall with embedded insulation" family within the Revit program serves as a valuable tool for material quantity estimation in such scenarios.

Design of Poly-Fuse OTP IP Using Multibit Cells (Multibit 셀을 이용한 Poly-Fuse OTP IP 설계)

  • Dongseob kim;Longhua Li;Panbong Ha;Younghee Kim
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
    • /
    • v.17 no.4
    • /
    • pp.266-274
    • /
    • 2024
  • In this paper, we designed a low-area 32-bit PF (Poly-fuse) OTP IP, a non-volatile memory that stores data required for analog circuit trimming and calibration. Since one OTP cell is constructed using two PFs in one select transistor, a 1cell-2bit multibit PF OTP cell that can program 2bits of data is proposed. The bitcell size of the proposed 1cell-2bit PF OTP cell is 1/2 of 12.69㎛ × 3.48㎛ (=44.161㎛2), reducing the cell area by 33% compared to that of the existing PF OTP cell. In addition, in this paper, a new 1 row × 32 column cell array circuit and core circuit (WL driving circuit, BL driving circuit, BL switch circuit, and DL sense amplifier circuit) are proposed to meet the operation of the proposed multbit cell. The layout size of the 32bit OTP IP using the proposed multibit cell is 238.47㎛ × 156.52㎛ (=0.0373㎛2) is reduced by about 33% compared that of the existing 32bit PF OTP IP using a single bitcell, which is 386.87㎛ × 144.87㎛ (=0.056㎛2). The 32-bit PF OTP IP, designed with 10 years of data retention time in mind, is designed with a minimum programmed PF sensing resistance of 10.5㏀ in the detection read mode and of 5.3 ㏀ in the read mode, respectively, as a result of post-layout simulation of the test chip.

Hyperspectral Imaging Information System for Analyzing the Urchin Barren Phenomenon to Ensure the Safety of Seaweed-Derived Biomass (해조류 유래 바이오매스 안전성 확보를 위한 갯녹음 현상 분석 초분광영상 정보 시스템)

  • Yong-Suk Kim;Sang-Mok Chang
    • Clean Technology
    • /
    • v.30 no.3
    • /
    • pp.175-187
    • /
    • 2024
  • Seaweeds are widely distributed along national coastlines around the world, and the biomass derived from them is an important marine biological organism. Seaweed is a crucial component of a healthy marine ecosystem. However, changes in marine environments have led to the occurrence of urchin barrens, and the damage caused by this phenomenon is steadily increasing. As a result, investigations into the distribution and spread of urchin barrens in the coastal areas of South Korea are being conducted regularly so efficient detection technologies are essential. One of the technologies that can swiftly and accurately analyze extensive areas is detection technology based on hyperspectral image information systems. This study aims to present the latest hyperspectral imaging technology for investigating the current status of urchin barrens and the methods for classifying this technology, including principles, preprocessing techniques, and correction methods. This study also proposes a classification technique for urchin barrens along the coast of Jeju Island that uses hyperspectral images and categorizes the urchin barrens into initial, intermediate, and advanced stages. The results showed that approximately 17.5% of the experimental areas were in the advanced stage. Based on this, various management and restoration methods tailored to different categories of urchin barren can be proposed.

A Study on the Availability of the On-Board Imager(OBI) and Cone-Beam CT(CBCT) in the Verification of Patient Set-up (온보드 영상장치(On-Board Imager) 및 콘빔CT(CBCT)를 이용한 환자 자세 검증의 유용성에 대한 연구)

  • Bak, Jino;Park, Sung-Ho;Park, Suk-Won
    • Radiation Oncology Journal
    • /
    • v.26 no.2
    • /
    • pp.118-125
    • /
    • 2008
  • Purpose: On-line image guided radiation therapy(on-line IGRT) and(kV X-ray images or cone beam CT images) were obtained by an on-board imager(OBI) and cone beam CT(CBCT), respectively. The images were then compared with simulated images to evaluate the patient's setup and correct for deviations. The setup deviations between the simulated images(kV or CBCT images), were computed from 2D/2D match or 3D/3D match programs, respectively. We then investigated the correctness of the calculated deviations. Materials and Methods: After the simulation and treatment planning for the RANDO phantom, the phantom was positioned on the treatment table. The phantom setup process was performed with side wall lasers which standardized treatment setup of the phantom with the simulated images, after the establishment of tolerance limits for laser line thickness. After a known translation or rotation angle was applied to the phantom, the kV X-ray images and CBCT images were obtained. Next, 2D/2D match and 3D/3D match with simulation CT images were taken. Lastly, the results were analyzed for accuracy of positional correction. Results: In the case of the 2D/2D match using kV X-ray and simulation images, a setup correction within 0.06 for rotation only, 1.8 mm for translation only, and 2.1 mm and 0.3 for both rotation and translation, respectively, was possible. As for the 3D/3D match using CBCT images, a correction within 0.03 for rotation only, 0.16 mm for translation only, and 1.5 mm for translation and 0.0 for rotation, respectively, was possible. Conclusion: The use of OBI or CBCT for the on-line IGRT provides the ability to exactly reproduce the simulated images in the setup of a patient in the treatment room. The fast detection and correction of a patient's positional error is possible in two dimensions via kV X-ray images from OBI and in three dimensions via CBCT with a higher accuracy. Consequently, the on-line IGRT represents a promising and reliable treatment procedure.

Estimation for Ground Air Temperature Using GEO-KOMPSAT-2A and Deep Neural Network (심층신경망과 천리안위성 2A호를 활용한 지상기온 추정에 관한 연구)

  • Taeyoon Eom;Kwangnyun Kim;Yonghan Jo;Keunyong Song;Yunjeong Lee;Yun Gon Lee
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.2
    • /
    • pp.207-221
    • /
    • 2023
  • This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

A digital Audio Watermarking Algorithm using 2D Barcode (2차원 바코드를 이용한 오디오 워터마킹 알고리즘)

  • Bae, Kyoung-Yul
    • Journal of Intelligence and Information Systems
    • /
    • v.17 no.2
    • /
    • pp.97-107
    • /
    • 2011
  • Nowadays there are a lot of issues about copyright infringement in the Internet world because the digital content on the network can be copied and delivered easily. Indeed the copied version has same quality with the original one. So, copyright owners and content provider want a powerful solution to protect their content. The popular one of the solutions was DRM (digital rights management) that is based on encryption technology and rights control. However, DRM-free service was launched after Steve Jobs who is CEO of Apple proposed a new music service paradigm without DRM, and the DRM is disappeared at the online music market. Even though the online music service decided to not equip the DRM solution, copyright owners and content providers are still searching a solution to protect their content. A solution to replace the DRM technology is digital audio watermarking technology which can embed copyright information into the music. In this paper, the author proposed a new audio watermarking algorithm with two approaches. First, the watermark information is generated by two dimensional barcode which has error correction code. So, the information can be recovered by itself if the errors fall into the range of the error tolerance. The other one is to use chirp sequence of CDMA (code division multiple access). These make the algorithm robust to the several malicious attacks. There are many 2D barcodes. Especially, QR code which is one of the matrix barcodes can express the information and the expression is freer than that of the other matrix barcodes. QR code has the square patterns with double at the three corners and these indicate the boundary of the symbol. This feature of the QR code is proper to express the watermark information. That is, because the QR code is 2D barcodes, nonlinear code and matrix code, it can be modulated to the spread spectrum and can be used for the watermarking algorithm. The proposed algorithm assigns the different spread spectrum sequences to the individual users respectively. In the case that the assigned code sequences are orthogonal, we can identify the watermark information of the individual user from an audio content. The algorithm used the Walsh code as an orthogonal code. The watermark information is rearranged to the 1D sequence from 2D barcode and modulated by the Walsh code. The modulated watermark information is embedded into the DCT (discrete cosine transform) domain of the original audio content. For the performance evaluation, I used 3 audio samples, "Amazing Grace", "Oh! Carol" and "Take me home country roads", The attacks for the robustness test were MP3 compression, echo attack, and sub woofer boost. The MP3 compression was performed by a tool of Cool Edit Pro 2.0. The specification of MP3 was CBR(Constant Bit Rate) 128kbps, 44,100Hz, and stereo. The echo attack had the echo with initial volume 70%, decay 75%, and delay 100msec. The sub woofer boost attack was a modification attack of low frequency part in the Fourier coefficients. The test results showed the proposed algorithm is robust to the attacks. In the MP3 attack, the strength of the watermark information is not affected, and then the watermark can be detected from all of the sample audios. In the sub woofer boost attack, the watermark was detected when the strength is 0.3. Also, in the case of echo attack, the watermark can be identified if the strength is greater and equal than 0.5.

Estimation of Chlorophyll-a Concentration in Nakdong River Using Machine Learning-Based Satellite Data and Water Quality, Hydrological, and Meteorological Factors (머신러닝 기반 위성영상과 수질·수문·기상 인자를 활용한 낙동강의 Chlorophyll-a 농도 추정)

  • Soryeon Park;Sanghun Son;Jaegu Bae;Doi Lee;Dongju Seo;Jinsoo Kim
    • Korean Journal of Remote Sensing
    • /
    • v.39 no.5_1
    • /
    • pp.655-667
    • /
    • 2023
  • Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R2), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R2 of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

Analysis of the Impact of Satellite Remote Sensing Information on the Prediction Performance of Ungauged Basin Stream Flow Using Data-driven Models (인공위성 원격 탐사 정보가 자료 기반 모형의 미계측 유역 하천유출 예측성능에 미치는 영향 분석)

  • Seo, Jiyu;Jung, Haeun;Won, Jeongeun;Choi, Sijung;Kim, Sangdan
    • Journal of Wetlands Research
    • /
    • v.26 no.2
    • /
    • pp.147-159
    • /
    • 2024
  • Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

Improving Usage of the Korea Meteorological Administration's Digital Forecasts in Agriculture: I. Correction for Local Temperature under the Inversion Condition (기상청 동네예보의 영농활용도 증진을 위한 방안: I. 기온역전조건의 국지기온 보정)

  • Kim, Soo-Ock;Kim, Dae-Jun;Kim, Jin-Hee;Yun, Jin I.
    • Korean Journal of Agricultural and Forest Meteorology
    • /
    • v.15 no.2
    • /
    • pp.76-84
    • /
    • 2013
  • An adequate downscaling of the official forecasts of Korea Meteorological Administration (KMA) is a prerequisite to improving the value and utility of agrometeorological information in rural areas, where complex terrain and small farms constitute major features of the landscape. In this study, we suggest a simple correction scheme for scaling down the KMA temperature forecasts from mesoscale (5 km by 5 km) to the local scale (30 m by 30 m) across a rural catchment, especially under temperature inversion conditions. The study area is a rural catchment of 50km2 area with complex terrain and located on a southern slope of Mountain Jiri National Park. Temperature forecasts for 0600 LST on 62 days with temperature inversion were selected from the fall 2011-spring 2012 KMA data archive. A geospatial correction scheme which can simulate both cold air drainage and the so-called 'thermal belt' was used to derive the site-specific temperature deviation across the study area at a 30 m by 30 m resolution from the original 5 km by 5 km forecast grids. The observed temperature data at 12 validation sites within the study area showed a substantial reduction in forecast error: from ±2C to ±1C in the mean error range and from 1.9C to 1.6C in the root mean square error. Improvement was most remarkable at low lying locations showing frequent cold pooling events. Temperature prediction error was less than 2C for more than 80% of the observed inversion cases and less than 1C for half of the cases. Temperature forecasts corrected by this scheme may accelerate implementation of the freeze and frost early warning service for major fruits growing regions in Korea.

Thermal Properties of Granite from the Central Part of Korea (한국 중부 지역의 화강암 열물성)

  • Kim, Jongchan;Lee, Youngmin;Koo, Min-Ho
    • Economic and Environmental Geology
    • /
    • v.47 no.4
    • /
    • pp.441-453
    • /
    • 2014
  • Thermal and physical properties were measured on 206 Jurassic granite samples obtained from three boreholes in the central part of Korea. Thermal conductivity(λ), thermal diffusivity(α), and specific heat(Cp) were measured in a laboratory; the average values are λ=2.813 W/mK, α=1.296mm2/sec, and Cp=0.816 J/gK, respectively. In addition, porosity(ϕ), and dry and saturated density(ρ) were measured in the laboratory; the average values are ϕ=0.01, ρ(dry)=2.662g/cm3 and ρ(saturated)=2.67g/cm3, respectively. Thermal diffusivity of 10 granite samples were measured with increasing temperature from 25C to 200C. In this study, we found that thermal diffusivity at 200C is about 30% lower than thermal diffusivity at 25C. In correlation analysis, thermal conductivity increases with increasing thermal diffusivity. However, thermal conductivity does not show good correlation with porosity and density. Consequently, we know that thermal conductivity of granite would be more influenced by mineral composition than by porosity. We also derived ρ=2.393×ϕ+2.705 from density and porosity data. XRD and XRF analysis were performed to investigate effects of mineral and chemical composition on thermal conductivity. From those results, we found that thermal conductivity increases with increasing quartz and SiO2, and decreases with increasing albite and Al2O3. Regression analysis using those mineral and chemical composition were carried out ; we found K=0.0294VQuartz+1.93 for quartz, K=0.237WSiO214.09 for SiO2, and K=0.053WSiO20.476WAl2O3+6.52 for SiO2 and Al2O3. Specific gravities were measured on 10 granite samples in the laboratory. The measured specific gravity depends on chemical compositions of granite. Therefore, specific gravity can be estimated by the felsic-mafic index(F) that is calculated from chemical composition. The estimated specific gravity ranges from 2.643 to 2.658. The average relative error between measured and estimated specific gravities is 0.677%.