• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.1
• /
• pp.9-18
• /
• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• Journal of the Korean earth science society
• /
• v.42 no.5
• /
• pp.504-513
• /
• 2021

Different physical processes, according to the atmospheric boundary layer types, were used in the Local Data Assimilation and Prediction System (LDAPS) of the Unified Model (UM) used by the Korea Meteorological Administration (KMA). Therefore, it is important to verify the atmospheric boundary layer types in the numerical model to improve the accuracy of the models performance. In this study, the atmospheric boundary layer types were verified using observational data. To classify the atmospheric boundary layer types, summer intensive observation data from radiosonde, flux observation instruments, Doppler wind Light Detection and Ranging(LIDAR) and ceilometer were used. A total number of 201 observation data points were analyzed over the course 61 days from June 18 to August 17, 2019. The most frequent types of differences between LDAPS and observed data were type 1 in LDAPS and type 2 in observed(each 53 times). And type 3 difference was observed in LDAPS and type 5 and 6 were observed 24 and 15 times, respectively. It was because of the simulation performance of the Cloud Physics such as that associated with the simulation of decoupled stratocumulus and cumulus cloud. Therefore, to improve the numerical model, cloud physics aspects should be considered in the atmospheric boundary layer type classification.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.12
• /
• pp.489-498
• /
• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.149-155
• /
• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.3
• /
• pp.262-276
• /
• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Proceedings of the Acoustical Society of Korea Conference
• /
• spring
• /
• pp.74-77
• /
• 1999

본 논문에서는 본 연구실에서 개발한 주소 음성인식 시스템의 인식 속도를 개선시키기 위하예 새로운 가변 프루닝 문턱치를 적용하는 방법을 제안하고 실험을 통하여 그 유효성을 확인하였다. 기존의 가변 프루닝 문턱치는 일정 프레임이 경과하면 일정 값을 가진 문턱치를 계속하여 감소시켜나가는 방법을 반복하기 때문에, 불필요한 탐색공간을 탐색하게 된다. 본 논문에서 새로이 제안하는 가변 프루닝 문턱치를 채용하는 방법은 처음 일정 구간이 경과되면 일정 문턱치를 감소시키나, 다음 일정 프레임에서는 탐색되어야할 후보에 따라서 문턱치를 변화시켜 프루닝시키기 때문에 탐색공간을 효과적으로 감소시킬 수 있다. 제안된 방법의 유효성을 확인하기 위하여, 본 연구실에서 개발한 한국어 주소 입력 시스템에 적용하였다. 이 시스템은 48개의 연속 HMM 유사음소단위(Phoneme Like Units; PLUs)를 인식의 기본단위로 하고, .사용환경 변화에 의한 인식성능의 저하를 최소화하기 위해 최대사후 확률추정법(Maximum A Posteriori Probability Estimation; MAP)을 사용하며, 인식알고리즘으로는OPDP(One Pass Dynamic Programming)법을 이용하고 있다. 남성화자 3인에 의한 75개의 연결주소명을 이용하여 인식 실험을 수행한 결과 고정 프루닝 문턱치를 적용한 경우 인식률은 평균 $96.0\%$, 인식 시간은 5.26초였고, 기존의 가변 프루닝 문턱치의 경우 인식률은 평균 $96.0\%$, 인식 시간은 5.1초인 데 비하여, 새로운 가변 프루닝 문턱치를 적용찬 경우에는 인식률 저하없이 인식 시간이 4.34초로, 기존에 비해 각각 0.92초, 0.76초 인식 시간이 감소되어 제안한 방법의 유효성을 확인할 수 있었다.는 달리 각 산란 영역에서 그 지수는 1씩 작은 값을 갖는다.향에 따라 음장변화가 크게 다를 것이 예상되므로 이를 규명하기 위해서는 궁극적으로 3차원적인 음장분포 연구가 필요하다. 음향센서를 해저면에 매설할 경우 수충의 수온변화와 센서 주변의 수온변화 사이에는 어느 정도의 시간지연이 존재하게 되므로 이에 대한 영향을 규명하는 것도 센서의 성능예측을 위해서 필요하리라 사료된다.가지는 심부 가스의 개발 성공률을 증가시키기 위하여 심부 가스가 존재하는 지역의 지질학적 부존 환경 및 조성상의 특성과 생산시 소요되는 생산비용을 심도에 따라 분석하고 생산에 수반되는 기술적 문제점들을 정리하였으며 마지막으로 향후 요구되는 연구 분야들을 제시하였다. 또한 참고로 현재 심부 가스의 경우 미국이 연구 개발 측면에서 가장 활발한 활동을 전개하고 있으며 그 결과 다수의 신뢰성 있는 자료들을 확보하고 있으므로 본 논문은 USGS와 Gas Research Institute(GRI)에서 제시한 자료에 근거하였다.ऀĀ耀Ā삱?⨀؀Ā Ā?⨀ጀĀ耀Ā?돀ꢘ?⨀硩?⨀ႎ?⨀?⨀넆돐쁖잖⨀쁖잖⨀／ࠐ?⨀焆덐瀆倆Āⶇ퍟ⶇ퍟ĀĀĀĀ磀鲕좗?⨀肤?⨀⁅Ⴅ?⨀쀃잖⨀䣙熸ጁ↏?⨀

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.586-596
• /
• 2011

In the IT industry, processing units of IT applications are getting smaller and high efficient. Furthermore, the realization of various smart functions is highly feasible now due to advances in sensing technology. The service infrastructures on high efficient and compact mobile devices are applied to various areas. These also could be possessed by users and is built into the devices. Currently, studies on the UX(User Experience) field to attempt an analysis and prediction of user's information are continuing with reference to the UI(User Interface). However, research on the common framework of classification and storing the user-information, and standardization of form has not been attempted yet. In this study, we proposed the AUX(Associative user Experience) model and process structure to store various empirical data by users. The AUX model expressed a diversity of user's empirical data using extended E-TCPN model. And also, we expressed the data structure using XML with reference to the application of AUX model. This expressed model and separation of process structure guarantee its specialty, productivity and flexibility through the humanistic characteristics of users and the independence of technical process structure. The AUX model maps out the AUX information process architecture and expressed the process with the improved MPP algorithm, to analyze of its performance. The simulation of movements applying to MPP traffic allocation of VOD is used to analyze of its performance. The playback deviation of MPP Graphic Allocation Algorism where the AUX model was applied was improved by 10.41% more than the one where it was not applied. As a result of that, playback performance has improved due to the conversion of AUX with accessing media, content of users and dynamic traffic allocation such as MPI and CPI.

• Journal of Internet Computing and Services
• /
• v.14 no.4
• /
• pp.25-33
• /
• 2013

The procedure of the arbitration which is the tag collision is essential because the multiple tags response simultaneously in the same frequency to the request of the Reader. This procedure is known as Anti-collision and it is a key technology in the RFID system. In this paper, we propose the 4-Bit Pattern Slot Allocation(4-BPSA) algorithm for the high-speed identification of the multiple tags. The proposed algorithm is based on the tree algorithm using the time slot and identify the tag quickly and efficiently through accurate prediction using the a slot as a 4-bit pattern according to the slot allocation scheme. Through mathematical performance analysis, We proved that the 4-BPSA is an O(n) algorithm by analyzing the worst-case time complexity and the performance of the 4-BPSA is improved compared to existing algorithms. In addition, we verified that the 4-BPSA is performed the average 0.7 times the query per the Tag through MATLAB simulation experiments with performance evaluation of the algorithm and the 4-BPSA ensure stable performance regardless of the number of the tags.

• Journal of the Korean GEO-environmental Society
• /
• v.16 no.5
• /
• pp.55-66
• /
• 2015

Big tidal differences, which range from 3.0 m to 8.0 m, exist with regional locations at south and west shores of Korea. Under this ocean circumstance, since a large scour may occur due to multi-directional tidal current and transverse stress of the wind, the scour surrounding the wind turbine structure can make instability of the system due to unexpected system vibration. The hydraulic resistance capacity of soils consolidated under different pressures are evaluated by Erosion Function Apparatus (EFA) under unidirectional and bi-directional flows in this study. It was found that the flow direction change affects significantly on the sour rate and critical shear stress, regardless of soil types while the consolidation pressure affects mainly cohesive soil. Among geotechnical parameters, the undrained shear strength can be well-correlated with the hydraulic resistance capacity, regardless soil type while the shear wave velocity shows the proportional relationships with the hydraulic resistance capacities of fine grained soil and coarse grained soil, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.3A
• /
• pp.232-239
• /
• 2011

IEEE 802.11n standard provides a framework for new link adaptation. A station can request that another station provide a Modulation and Coding Scheme (MCS) feedback, to fully exploit channel variations on a link. However, if the time elapsed between MCS feedback request and the data frame transmission using the MCS feedback becomes bigger, the previously received feedback information may be obsolete. In that case, the effectiveness of the feedback-based link adaptation is compromised. If a station can estimate how fast the channel quality to the target station changes, it can improve accuracy of the link adaptation. The contribution of this paper is twofold. First, through a thorough NS-2 simulation, we show how the coherence time affects the performance of the MCS feedback based link adaptation of 802.11n networks. Second, this paper proposes an effective algorithm for coherence time estimation. Using Allan variance information statistic, a station estimates the coherence time of the receiving link. A proposed link adaptation scheme considering the coherence time can provide better performance.