• Title/Summary/Keyword: Computation Time

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RAG-based Image Segmentation Using Multiple Windows (RAG 기반 다중 창 영상 분할 (1))

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.22 no.6
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    • pp.601-612
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    • 2006
  • This study proposes RAG (Region Adjancency Graph)-based image segmentation for large imagery in remote sensing. The proposed algorithm uses CN-chain linking for computational efficiency and multi-window operation of sliding structure for memory efficiency. Region-merging due to RAG is a process to find an edge of the best merge and update the graph according to the merge. The CN-chain linking constructs a chain of the closest neighbors and finds the edge for merging two adjacent regions. It makes the computation time increase as much as an exact multiple in the increasement of image size. An RNV (Regional Neighbor Vector) is used to update the RAG according to the change in image configuration due to merging at each step. The analysis of large images requires an enormous amount of computational memory. The proposed sliding multi-window operation with horizontal structure considerably the memory capacity required for the analysis and then make it possible to apply the RAG-based segmentation for very large images. In this study, the proposed algorithm has been extensively evaluated using simulated images and the results have shown its potentiality for the application of remotely-sensed imagery.

The Analytic Gradient with a Reduced Molecular Orbital Space for the Equation-of-Motion Coupled-Cluster Theory: Systematic Study of the Magnitudes and Trends in Simple Molecules

  • Baek, Gyeong Gi;Jeon, Sang Il
    • Bulletin of the Korean Chemical Society
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    • v.21 no.7
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    • pp.720-726
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    • 2000
  • The analytic gradient method for the equation-of-motion coupled-cluster singles and doubles (EOM-CCSD) energy has been extended to employ a reduced molecular orbital (MO) space. Not only the innermost core MOs but also some of the outermost virtua l MOs can be dropped in the reduced MO space, and a substantial amount of computation time can be reduced without deteriorating the results. In order to study the magnitudes and trends of the effects of the dropped MOs, the geometries and vibrational properties of the ground and excited states of BF, CO, CN, N2, AlCl, SiS, P2, BCl, AIF, CS, SiO, PN and GeSe are calculated with different sizes of molecular orbital space. The 6-31 G* and the aug-cc-pVTZ basis sets are employed for all molecules except GeSc for which the 6-311 G* and the TZV+f basis sets are used. It is shown that the magnitudes of the drop-MO effects are about $0.005\AA$ in bond lengths and about 1% on harmonic frequencies and IR intensities provided that the dropped MOs correspond to (1s), (1s,2s,2p), an (1s,2s,2p,3s,3p) atomic orbitals of the first, the second, and the third row atoms, respectively. The geometries and vibrational properties of the first and the second excited states of HCN and HNC are calculated by using a drastically reduced virtual MO space as well as with the well defined frozen core MO space. The results suggest the possibility of using a very smalI MO space for qualitative study of valence excited states.

Study on Aerodynamic Optimization Design Process of Multistage Axial Turbine

  • Zhao, Honglei;Tan, Chunqing;Wang, Songtao;Han, Wanjin;Feng, Guotai
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2008.03a
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    • pp.130-135
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    • 2008
  • An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

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A Lightweight and Privacy-Preserving Answer Collection Scheme for Mobile Crowdsourcing

  • Dai, Yingling;Weng, Jian;Yang, Anjia;Yu, Shui;Deng, Robert H.
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.8
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    • pp.2827-2848
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    • 2021
  • Mobile Crowdsourcing (MCS) has become an emerging paradigm evolved from crowdsourcing by employing advanced features of mobile devices such as smartphones to perform more complicated, especially spatial tasks. One of the key procedures in MCS is to collect answers from mobile users (workers), which may face several security issues. First, authentication is required to ensure that answers are from authorized workers. In addition, MCS tasks are usually location-dependent, so the collected answers could disclose workers' location privacy, which may discourage workers to participate in the tasks. Finally, the overhead occurred by authentication and privacy protection should be minimized since mobile devices are resource-constrained. Considering all the above concerns, in this paper, we propose a lightweight and privacy-preserving answer collection scheme for MCS. In the proposed scheme, we achieve anonymous authentication based on traceable ring signature, which provides authentication, anonymity, as well as traceability by enabling malicious workers tracing. In order to balance user location privacy and data availability, we propose a new concept named current location privacy, which means the location of the worker cannot be disclosed to anyone until a specified time. Since the leakage of current location will seriously threaten workers' personal safety, causing such as absence or presence disclosure attacks, it is necessary to pay attention to the current location privacy of workers in MCS. We encrypt the collected answers based on timed-release encryption, ensuring the secure transmission and high availability of data, as well as preserving the current location privacy of workers. Finally, we analyze the security and performance of the proposed scheme. The experimental results show that the computation costs of a worker depend on the number of ring signature members, which indicates the flexibility for a worker to choose an appropriate size of the group under considerations of privacy and efficiency.

Deep Learning based Image Recognition Models for Beef Sirloin Classification (딥러닝 이미지 인식 기술을 활용한 소고기 등심 세부 부위 분류)

  • Han, Jun-Hee;Jung, Sung-Hun;Park, Kyungsu;Yu, Tae-Sun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.3
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    • pp.1-9
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    • 2021
  • This research examines deep learning based image recognition models for beef sirloin classification. The sirloin of beef can be classified as the upper sirloin, the lower sirloin, and the ribeye, whereas during the distribution process they are often simply unified into the sirloin region. In this work, for detailed classification of beef sirloin regions we develop a model that can learn image information in a reasonable computation time using the MobileNet algorithm. In addition, to increase the accuracy of the model we introduce data augmentation methods as well, which amplifies the image data collected during the distribution process. This data augmentation enables to consider a larger size of training data set by which the accuracy of the model can be significantly improved. The data generated during the data proliferation process was tested using the MobileNet algorithm, where the test data set was obtained from the distribution processes in the real-world practice. Through the computational experiences we confirm that the accuracy of the suggested model is up to 83%. We expect that the classification model of this study can contribute to providing a more accurate and detailed information exchange between suppliers and consumers during the distribution process of beef sirloin.

Verification and validation of isotope inventory prediction for back-end cycle management using two-step method

  • Jang, Jaerim;Ebiwonjumi, Bamidele;Kim, Wonkyeong;Cherezov, Alexey;Park, Jinsu;Lee, Deokjung
    • Nuclear Engineering and Technology
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    • v.53 no.7
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    • pp.2104-2125
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    • 2021
  • This paper presents the verification and validation (V&V) of a calculation module for isotope inventory prediction to control the back-end cycle of spent nuclear fuel (SNF). The calculation method presented herein was implemented in a two-step code system of a lattice code STREAM and a nodal diffusion code RAST-K. STREAM generates a cross section and provides the number density information using branch/history depletion branch calculations, whereas RAST-K supplies the power history and three history indices (boron concentration, moderator temperature, and fuel temperature). As its primary feature, this method can directly consider three-dimensional core simulation conditions using history indices of the operating conditions. Therefore, this method reduces the computation time by avoiding a recalculation of the fuel depletion. The module for isotope inventory calculates the number densities using the Lagrange interpolation method and power history correction factors, which are applied to correct the effects of the decay and fission products generated at different power levels. To assess the reliability of the developed code system for back-end cycle analysis, validation study was performed with 58 measured samples of pressurized water reactor (PWR) SNF, and code-to-code comparison was conducted with STREAM-SNF, HELIOS-1.6 and SCALE 5.1. The V&V results presented that the developed code system can provide reasonable results with comparable confidence intervals. As a result, this paper successfully demonstrates that the isotope inventory prediction code system can be used for spent nuclear fuel analysis.

A New Head Pose Estimation Method based on Boosted 3-D PCA (새로운 Boosted 3-D PCA 기반 Head Pose Estimation 방법)

  • Lee, Kyung-Min;Lin, Chi-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.6
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    • pp.105-109
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    • 2021
  • In this paper, we evaluate Boosted 3-D PCA as a Dataset and evaluate its performance. After that, we will analyze the network features and performance. In this paper, the learning was performed using the 300W-LP data set using the same learning method as Boosted 3-D PCA, and the evaluation was evaluated using the AFLW2000 data set. The results show that the performance is similar to that of the Boosted 3-D PCA paper. This performance result can be learned using the data set of face images freely than the existing Landmark-to-Pose method, so that the poses can be accurately predicted in real-world situations. Since the optimization of the set of key points is not independent, we confirmed the manual that can reduce the computation time. This analysis is expected to be a very important resource for improving the performance of network boosted 3-D PCA or applying it to various application domains.

Comparative Analysis of Filtering Techniques for Vegetation Points Removal from Photogrammetric Point Clouds at the Stream Levee (하천 제방의 영상 점군에서 식생 점 제거 필터링 기법 비교 분석)

  • Park, Heeseong;Lee, Du Han
    • Ecology and Resilient Infrastructure
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    • v.8 no.4
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    • pp.233-244
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    • 2021
  • This study investigated the application of terrestrial light detection and ranging (LiDAR) to inspect the defects of the vegetated levee. The accuracy of vegetation filtering techniques was compared by applying filtering techniques on photogrammetric point clouds of a vegetated levee generated by terrestrial LiDAR. Representative 10 vegetation filters such as CIVE, ExG, ExGR, ExR, MExG, NGRDI, VEG, VVI, ATIN, and ISL were applied to point cloud data of the Imjin River levee. The accuracy order of the 10 techniques based on the results was ISL, ATIN, ExR, NGRDI, ExGR, ExG, MExG, VVI, VEG, and CIVE. Color filters show certain limitations in the classification of vegetation and ground and classify grass flower image as ground. Morphological filters show a high accuracy of the classification, but they classify rocks as vegetation. Overall, morphological filters are superior to color filters; however, they take 10 times more computation time. For the improvement of the vegetation removal, combined filters of color and morphology should be studied.

Analysis of Massive Scholarly Keywords using Inverted-Index based Bottom-up Clustering (역인덱스 기반 상향식 군집화 기법을 이용한 대규모 학술 핵심어 분석)

  • Oh, Heung-Seon;Jung, Yuchul
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.11
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    • pp.758-764
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    • 2018
  • Digital documents such as patents, scholarly papers and research reports have author keywords which summarize the topics of documents. Different documents are likely to describe the same topic if they share the same keywords. Document clustering aims at clustering documents to similar topics with an unsupervised learning method. However, it is difficult to apply to a large amount of documents event though the document clustering is utilized to in various data analysis due to computational complexity. In this case, we can cluster and connect massive documents using keywords efficiently. Existing bottom-up hierarchical clustering requires huge computation and time complexity for clustering a large number of keywords. This paper proposes an inverted index based bottom-up clustering for keywords and analyzes the results of clustering with massive keywords extracted from scholarly papers and research reports.

Computation of the Bow Deck Design Pressure against the Green Water Impact (Green Water 충격에 대비한 선수갑판 설계압력의 산출)

  • Kim, Yong Jig;Shin, Ki-Seok;Lee, Seung-Chul;Ha, Youngrok;Hong, Sa Young
    • Journal of the Society of Naval Architects of Korea
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    • v.56 no.4
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    • pp.343-351
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    • 2019
  • Green water impact may sometimes cause some structure damages on ship's bow deck. Prediction of proper design pressure against the green water impact is an essential task to prevent the possible damages on bow deck. This paper presents a computational method of the bow deck's design pressure against the green water impact. Large heave and pitch motions of ship are calculated by the time domain nonlinear strip method. Green water flow and pressure on bow deck are simulated by the predictor-corrector second kind upstream finite difference method. This green water simulation method is based on the shallow water wave equations expanded for moving bottom conditions. For various kind of ships such as container ship, VLCC, oil tanker and bulk carrier, the green water design pressures on bow deck are computed and discussed. Also, the obtained results of design pressure on bow deck are compared with those of the classification society rules and discussed.