• Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.48-54
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• 2014

In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level "rough" Gauss map with rather sparse point distribution and another lower level "fine" Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.314-319
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• 2009

In this paper, the network performance of a turbo coded optical code division multiple access (COMA) system with cross-layer, which is between physical and network layers, concept is analyzed and simulated We consider physical and MAC layers in a cross-layer concept. An intensity-modulated/direct-detection (IM/DD) optical system employing pulse position modulation (PPM) is considered In order to increase the system performance, turbo codes composed of parallel concatenated convolutional codes (PCCCs) is utilized. The network performance is evaluated in terms of bit error probability (BEP). From the simulation results, it is demonstrated that turbo coding offers considerable coding gain with reasonable encoding and decoding complexity. Also, it is confirmed that the performance of such an optical COMA network can be substantially improved by increasing the interleaver length and the number of iterations in the decoding process. The results of this paper can be applied to implement the indoor optical wireless LANs.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.389-390
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• 1996

The CCDs are widely used in astronomical observations either in direct imaging use or spectroscopic mode. However, the areas of available sensors are too small for large imaging format. One possibility to obtain large detection area is to assemble mosaics of CCD, and drive them simultaneously. Parallel driving of many CCDs together rules out the possibility of individual tuning; however, such optimisation is very important, when the ultimate low light level performance is required, particularly for new, or mixed devices. In this work, a new concept is explored for an entirely novel approach, where the drive waveforms are multiplexed and interleaved. This simultaneously reduces the number of leadout connections and permits individual optimisation efficiently. The digital controller can be designed within a single EPLD (Erasable Programmable Logic Device) chip produced by a CAD software package, where most of the digital controller circuits are integrated. This method can minimise the component. count., and improve the system efficiency greatly, based on earlier works by Han et a1. (1996, 1994). The system software has an open architecture to permit convenient modification by the user, to fit their specific purposes. Some variable system control parameters can be selected by a user with a wider range of choice. The digital controller design concept allows great flexibility of system parameters by the software, specifically for the compatibility to deal with any number of mixed CCDs, and in any format, within the practical limit.

• Interdisciplinary Bio Central
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• v.3 no.4
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• pp.14.1-14.9
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• 2011

Protein homology detection is an important issue in comparative genomics. Because of the exponential growth of sequence databases, fast and efficient homology detection tools are urgently needed. Currently, for homology detection, sequence comparison methods using local alignment such as BLAST are generally used as they give a reasonable measure for sequence similarity. However, these methods have drawbacks in offering overall sequence similarity, especially in dealing with eukaryotic genomes that often contain many insertions and duplications on sequences. Also these methods do not provide the explicit models for speciation, thus it is difficult to interpret their similarity measure into homology detection. Here, we present a novel method based on Word Conservation Score (WCS) to address the current limitations of homology detection. Instead of counting each amino acid, we adopted the concept of 'Word' to compare sequences. WCS measures overall sequence similarity by comparing word contents, which is much faster than BLAST comparisons. Furthermore, evolutionary distance between homologous sequences could be measured by WCS. Therefore, we expect that sequence comparison with WCS is useful for the multiple-species-comparisons of large genomes. In the performance comparisons on protein structural classifications, our method showed a considerable improvement over BLAST. Our method found bigger micro-syntenic blocks which consist of orthologs with conserved gene order. By testing on various datasets, we showed that WCS gives faster and better overall similarity measure compared to BLAST.

• Computers and Concrete
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• v.14 no.2
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• pp.145-161
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• 2014

The article presents the statistical method of grouping the results of the compressive strength of concrete in continuous production. It describes the method of dividing the series of compressive strength results into batches of statistically stable strength parameters at specific time intervals, based on the standardized concept of "concrete family". The article presents the examples of calculations made for two series of concrete strength results, from which sets of decreased strength parameters were separated. When assessing the quality of concrete elements and concrete road surfaces, the principal issue is the control of the compressive strength parameters of concrete. Large quantities of concrete mix manufactured in a continuous way should be subject to continuous control. Standardized approach to assessing the concrete strength proves to be insufficient because it does not allow for the detection of subsets of the decreased strength results, which in turn makes it impossible to make adjustments to the concrete manufacturing process and to identify particular product or area on site with decreased concrete strength. In this article two independent methods of grouping the test results of concrete with statistically stable strength parameters were proposed, involving verification of statistical hypothesis based on statistical tests: Student's t-test and Mann - Whitney - U test.

• KIEAE Journal
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• v.9 no.5
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• pp.97-104
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• 2009

In the process of urban development, the increase in the number and the complexity of urban facilities gives rise to a variety of problems, such as increase in construction and maintenance cost. In particular, taking into account the fact that an emergency situation in an urban facility can cause substantial loss of property as well as casualties, it becomes important to intelligently perceive states of facilities and properly execute countermeasures through real-time monitoring. In recent years, practitioners and researchers have made efforts to improve current passive and manpower-dependent facility management systems to be more active and intelligent, by applying diverse ubiquitous computing technologies for the u-City project. In this study, after discussing major drawbacks of the conventional facilities management, the concept and the model of a sensor-based integrated intelligent management system for urban facilities are proposed. The proposed model, by analyzing and processing real-time sensor data from urban facilities, not only supports the management of individual facilities, but also enables the detection of complex facility-related events and the process of their countermeasures. This active and intelligent management of urban facilities is expected to overcome the limitation of the conventional facilities management, and provide more suitable facility management services for the u-City development.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.602-617
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• 2014

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.237-240
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• 2003

Gas turbines are extensively used in flight propulsion, electrical power generation, and other industrial applications. During its life span, a turbine blade is taken out periodically for repair and maintenance. This includes re-coating the blade surface and re-drilling the cooling holes/channels. A successful laser re-drilling requires the measurement of a hole within the accuracy of ${\pm}0.15mm$ in position and ${\pm}3^{\circ}$ in orientation. Detection of gas turbine blade/vane cooling hole position and orientation thus becomes a very important step for the vane/blade repair process. The industry is in urgent need of an automated system to fulfill the above task. This paper proposes approaches and algorithms to detect the cooling hole position and orientation by using a vision system mounted on a robot arm. The channel orientation is determined based on the alignment of the vision system with the channel axis. The opening position of the channel is the intersection between the channel axis and the surface around the channel opening. Experimental results have indicated that the concept of cooling hole identification is feasible. It has been shown that the reproducible detection of cooling channel position is with +/- 0.15mm accuracy and cooling channel orientation is with +/$-\;3^{\circ}$ with the current test conditions. Average processing time to search and identify channel position and orientation is less than 1 minute.