• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Korean Journal of Weed Science
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• v.8 no.3
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• pp.250-257
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• 1988

The study was intended to know any relations between the rice tolerance to bensulfuron and varietal speciation in seed protein composition or any enzymatical allelies with or without chemical treatment. Rice varieties used were UCP-28, Chinsurah Boro II, Fukunohama, Fadehpur-2, IR 14252-13-2-2-5 as the tolerant group, and HP 93(3) FA, HP94(9) FA, Padilabou Alumbis, KH-17854, and IR 1846-2841-1 as the susceptible, respectively. Electrophoretic methods used were SDS-PAGE for seed protein, 7% PAGE for isozymes (acid phosphatase, peroxidase, malate dehydrogenase, and esterase from rice seedling) and variation in isoenzyme profiles (malate dehydrogenase, peroxidase, and esterase) as affected by different concentrations of bensulfuron(0, $10^{-6}$, $10^{-5}$ and $3{\times}10^{-5}M$) was also studied. The results are summarized as follows. -Among 16 bands separated in seed proteins, two different rice groups selected in terms of tolerance to bensulfuron were clustered in dissimilarity, which was based on relatively larger area in whole peaks and higher activities in N, O, P bands for the tolerant group. -Among isozymes obtained from rice seedlings without chemical treatments, the following specificities were obtained. The tolerant varieties had the relatively higher activity in D band out of 4 peroxidase bands. Malate dehydrogenase was separated into 3 bands and only tolerant varieties had A band and higher activities in Band C bands. Esterase was separated into 3-4 bands with higher activities in A and B bands for tolerant varieties. There were one major band accompanied by 2-3 minor bands for acid phosphatase in which only tolerant varieties had the B band. -The effect of Bensulfuron concentration on the isozyme activities showed that the activity of C band in peroxidase was not present in tolerant varieties which was contrary to the increased activities in susceptible varieties. However, D band was gradually disappeared only in susceptible varieties as the concentration of bensulfuron was increased. For malate dehydrogenase in the susceptible varieties, major bands D, E and F kept consistantly higher activities while minor bands A, B and C disappeared sensitively. Among 5 bands of esterase separated, D band was present only in the tolerant varieties while E band only in the susceptible. The activities in A, C, E bands were sharply decreased in the susceptible varieties as the concentration of bensulfuron was increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.59-70
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• 2020

The purpose of this study is to construct the ACD model for the block trading volume duration. The ACD model based on the block trading volume duration is referred to as Volume ACD (VACD) in this study. By integrating with GARCH-type models, the VACD based GARCH type models, which include VACD-GARCH, VACD-IGARCH and VACD-FIGARCH models, are set up. This study selects Chunghwa Telecom (CHT) Inc., offering the America Depository Receipt (ADR) in NYSE, to investigate the block trading volume duration in Taiwanese equity market. The empirical results indicate that the long memory in volume duration series increases dependence at level of volatility clustering by VACD (2,1)-FIGARCH (3,d,1) model. Moreover, the VACD (2,1)-IGARCH (1,1) exhibits relatively better performance of prediction on capturing block trading volume duration. This volatility model is more appropriate in this study to portray the change of the CHT Inc. prices and provides more information about the volatility process for investment strategy, which can be a reference indicator of financial asset pricing, hedging strategy and risk management.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.57-63
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• 2017

In the paper, we propose a new reliable technique for measuring food intake based on image automatically without user intervention. First, food and bowl image before and after meal is obtained by user. The food and the bowl are divided into each region by the K-means clustering, Otsu algorithm, Morphology, etc. And the volume of food is measured by a proportional expression based on the information of the container such as it's entrance diameter, depth, and bottom diameter. Finally, our method calculates the volume of the consumed food by the difference between before and after meal. The proposed technique has higher accuracy than existing method for measuring food intake automatically. The experiment result shows that the average error rate is up to 7% for three types of containers. Computer simulation results indicate that the proposed algorithm is a convenient and accurate method of measuring the food intake.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.401-412
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• 2005

인터넷을 통한 정보 전달 방법은 Ethernet과 ATM, CAN과 같은 다양한 통신 전달 프로토콜 및 방법을 통해 이루어지고 있다. 현재 연구된 네트웍상의 시간 지연 현상에 대한 연구는 일부네트웍 모델을 바탕으로 연구되고 있으나 다양한 통신 환경 하에서 발생하는 시간 지연 현상에 대해 최적의 모델링 방법을 제시해 주고 있지 못하고 있다. 따라서 다양한 네트웍 환경에 적합하도록 인터넷기반 비동기 샘플치 시스템 모델에 대한 연구가 필요하다. 아울러 인터넷을 통해 구성된 폐루프 시스템은 기존 제어 시스템과 다른 동작 특성과 외란 특성을 가지므로 인터넷 환경에 적합하게 설계된 견실 제어 방법이 필요하다. 따라서 안정성이 극히 요구되는 각종 산업기기 등에 대한 웹기반 정밀 원격 제어를 원활히 수행하기 위해서는 웹 환경에 최적화된 강인 제어 이론 개발이 필요하다. 따라서, 본 논문에서는 원격지 플랜트에 대한 실시간 원격 제어를 안정적 및 효율적으로 수행하도록 인터넷상의 데이터 전송시 시간 지연 현상 분석 및 인터넷 기반 제어시스템의 전달 특성 분석하였다.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.68.3-69
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• 2020

We develop a series of N-body data challenges, functional to the final analysis of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 (DR16) galaxy sample, primarily based on high-fidelity catalogs constructed from the Outer Rim simulation. We generate synthetic galaxy mocks by populating Outer Rim halos with a variety of halo occupation distribution (HOD) schemes of increasing complexity, spanning different redshift intervals. We then assess the performance of three complementary redshift space distortion (RSD) models in configuration and Fourier space, adopted for the analysis of the complete DR16 eBOSS sample of Luminous Red Galaxies (LRGs). We find that all the methods are mutually consistent, with comparable systematic errors on the Alcock-Paczynski parameters and the growth of structure, and robust to different HOD prescriptions - thus validating the robustness of the models and the pipelines used for the baryon acoustic oscillation (BAO) and full shape clustering analysis. Our study is relevant for the final eBOSS DR16 'consensus cosmology', as the systematic error budget is informed by testing the results of analyses against these high-resolution mocks. In addition, it is also useful for future large-volume surveys, since similar mock-making techniques and systematic corrections can be readily extended to model for instance the DESI galaxy sample.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.581-587
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• 2013

The amounts of carbon (C), nitrogen (N) and phosphorus (P) in relation to dry weight (D.W.) were measured in zooplankton from the large four rivers (Han R., Geum R., Yeongsan R. and Seomjin R.) during 2004~2008. The stoichiometry of total zooplankton in four river systems was highly variable. The ranges of average C, N and P-contents were $70{\sim}620mgC\;mg^{-1}$ D.W., $7.1{\sim}85.5{\mu}gN\;mg^{-1}$ D.W. and $2.5{\sim}7.4{\mu}gP\;mg^{-1}$ D.W., respectively. The mean C :N: P atomic ratios reflected large spatial differences. The C : P and N : P ratios of the zooplankton community ranged from 38 to 392 : 1 and from 4 to 65 : 1 in all sampling sites. Self-Organizing Map (SOM) was applied to the survey data, and the study sites were clearly classified into 3 clusters. Clustering was largely affected by the distribution pattern of C, N, P-contents, which is related with characteristics of river systems on the basis of stoichiometry.

• Korean Journal of Breeding Science
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• v.41 no.4
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• pp.437-443
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• 2009

In this study, we used the random amplified polymorphic DNA (RAPD) technique to evaluate the genetic relationships among 29 grapevine cultivars (Vitis spp.). Sixty selective primers detected a total of 558 polymorphic bands. By UPGMA (unweighted pair-group method arithmetic average) cluster analysis with 558 polymorphic bands, the 29 grapevine cultivars were divided into six major groups at 58.8% genetic similarity. The "Super Hamburg" was clustered in group I. Group II consisted of "Wonkyo RA-23", "Muscat Hamburg", "Tano Red", and "Tankeumchu". Group III consisted of "Alden", "Wonkyo RA -21", "Wonkyo RA-30", and "Dutchess". Group IV included 14 grapevine cultivars ("Heukgoosul", "Heukbosuk", "Suok", "Wonkyo RA-29", "Wonkyo RA-22", "Kyoho", "Pione", "Beniizu", "Golden Muscat", "Jinok", "Doonuri", "Campbell Early", "Delaware", and "Schuyler"). Group V consisted of "Hongdan", "Tamnara", "Hongisul", and "Himrod seedless". Group VI included 2 cultivars ("Cheongsoo", and "S. 9110").