• Korean Journal of Computational Design and Engineering
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• v.7 no.3
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• pp.148-156
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• 2002

Most Rapid Prototyping (RP) processes adopt a solid Computer Aided Design (CAD) model, which will be sliced into thin layers of constant thickness in the building direction. Each cross-sectional layer is successively deposited and, simultaneously, bonded onto the previous layer; and eventually the stacked layers from a physical part of the model. A new RP process, the transfer-type Variable Lamination Manufacturing process using expandable polystyrene foam sheet (VLM-ST), has been developed to reduce building time and to improve the surface finish of parts with the thick layers and a sloping surface. This paper describes the generation of Unit Shape Layer (USL), the cutting path data of the linen. hotwire cutter for the VLM-ST process. USL is a three-dimensional layer with a thickness of more than 1 mm and a side slope, and it is the basic unit of cutting and building in the VLM-ST process. USL includes data such as layer thickness, positional coordinates, side angles of each layer, hotwire cutting speed, the heat input to the hotwire, and reference shape. The procedure of generating USL is as follows: (1)Generation of the mid-slice from the CAD model, (2)Conversion of the mid-slice into a simply connected domain, (3)Generation to the reference shape for the mid-slice, (4)Calculation of the rotation angle of the hotwire of the cutting system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.210-211
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• 2007

The objective of this paper is to effectively detect partial discharges in underground power cables. In this field, we have been usually applied several sensors for such partial discharges. This study used a type of beyond compare antenna based on the influence of background noises. Also, we designed a new structure that is able to easily apply in the adhesion of planar loop types for underground power cables in measurement sensitiveness elevation. A high frequency simulation tool (CST-MWS) was applied to the antenna used in this study, and it was used to evaluate certain characteristics. We fabricated an antenna using the simulation data obtained from a specific test. After checking the sensitivity of this Planar Loop Sensor in the Lab, it was tested in an actual site. This paper analyzed the data as a part of time and frequency domain using an oscilloscope and spectrum analyzer, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.152-159
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• 2005

This paper describes the application of a model-based system design method critical to complex intelligent systems, PSARE, to a welding robot development to define its top level architecture. The PSARE model consists of requirement model which describes the core processes(function) of the system, enhanced requirement model which adds technology specific processes to requirement model and allocates them to architecture model, and architecture model which describes the structure and interfaces and flows of the modules of the system. This paper focuses on the detailed procedure and method rather than the detailed domain model of the welding robot. In this study, only the top level architecture of a welding robot was defined using the PSARE method. However, the method can be repeatedly applied to the lower level architecture of the robot until the process which the robot should perform can be clearly defined. The enhanced data flow diagram in this model separates technology independent processes and technology specific processes. This approach will provide a useful base not only for improvement of a class of welding robots but also for development of increasingly complex intelligent real-time systems.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.201-201
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• 2016

The asymptotic extreme value distributions of maxima are a natural choice when designing against future extreme events like flood peaks or wave heights, given a stationary time series. The generalized extreme value distribution (GEV) is often utilised in this context because it is seen as a convenient single expression for extreme event analysis. However, the GEV has a drawback because the location of the distribution bound relative to the data is a discontinuous function of the GEV shape parameter. That is, for annual maxima approximated by the Gumbel distribution, the data is also consistent with a GEV distribution with an upper bound (no lower bound) or a GEV distribution with a lower bound (no upper bound). A more consistent single extreme value expression for design purposes is proposed as the Weibull distribution of smallest extremes, as applied to transformed annual maxima. The Weibull distribution limit holds here for sufficiently large sample sizes, irrespective of the extreme value domain of attraction applicable to the untransformed maxima. The Gumbel, Type 2, and Type 3 extreme value distributions thus become redundant, together with the GEV, because in reality there is only a single asymptotic extreme value distribution required for design purposes - the Weibull distribution of minima as applied to transformed maxima. An illustrative synthetic example is given showing transformed maxima from the normal distribution approaching the Weibull limit much faster than the untransformed sample maxima approach the normal distribution Gumbel limit. Some New Zealand examples are given with the Weibull distribution being applied to reciprocal transformations of annual flood maxima, where the untransformed maxima follow apparently different extreme value distributions.

• Current Optics and Photonics
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• v.2 no.6
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• pp.532-539
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• 2018

Multiple LED arrays can be utilized in visible-light communication (VLC) to improve communication efficiency, while maintaining smart illumination functionality through dimming control. This paper proposes a modulation scheme called "Spatial Intensity Modulation" (SIM), where the effective number of turned-on LEDs is employed for data modulation and dimming control in VLC systems. Unlike the conventional pulse-amplitude modulation (PAM), symbol intensity levels are not determined by the amplitude levels of a VLC signal from each LED, but by counting the number of turned-on LEDs, illuminating with a single amplitude level. Because the intensity of a SIM symbol and the target dimming level are determined solely in the spatial domain, the problems of conventional PAM-based VLC and related MIMO VLC schemes, such as unstable dimming control, non uniform illumination functionality, and burdens of channel prediction, can be solved. By varying the number and formation of turned-on LEDs around the target dimming level in time, the proposed SIM scheme guarantees homogeneous illumination over a target area. An analysis of the dimming capacity, which is the achievable communication rate under the target dimming level in VLC, is provided by deriving the turn-on probability to maximize the entropy of the SIM-based VLC system. In addition, a practical design of dimmable SIM scheme applying the multilevel inverse source coding (MISC) method is proposed. The simulation results under a range of parameters provide baseline data to verify the performance of the proposed dimmable SIM scheme and applications in real systems.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.143-153
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• 2019

In this study, numerical simulations for the prediction of added resistance for KVLCC2 with varying wave steepness are performed using a Computational Fluid Dynamics (CFD) method and a 3-D linear potential method, and then the non-linearities of added resistance and ship motions are investigated in regular short and long waves. Firstly, grid convergence tests in short and long waves are carried out to establish an optimal mesh system for CFD simulations. Secondly, numerical simulations are performed to predict ship added resistance and vertical motion responses in short and long waves and the results are verified using the available experimental data. Finally, the non-linearities of added resistance and ship motions with unsteady wave patterns in the time domain are investigated with the increase in wave steepness in both short and long waves. The present systematic study demonstrates that the numerical results have a reasonable agreement with the experimental data and emphasizes the non-linearity in the prediction of the added resistance and the ship motions with the increasing wave steepness in short and long waves.

• International Journal of Contents
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• v.17 no.1
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• pp.1-10
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• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.1-6
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• 2023

Text mining (TM) is most widely used to process the various unstructured text documents and process the data present in the various domains. The other name for text mining is text classification. This domain is most popular in many domains such as movie reviews, product reviews on various E-commerce websites, sentiment analysis, topic modeling and cyber bullying on social media messages. Cyber-bullying is the type of abusing someone with the insulting language. Personal abusing, sexual harassment, other types of abusing come under cyber-bullying. Several existing systems are developed to detect the bullying words based on their situation in the social networking sites (SNS). SNS becomes platform for bully someone. In this paper, An Enhanced text mining approach is developed by using Ensemble Algorithm (ETMA) to solve several problems in traditional algorithms and improve the accuracy, processing time and quality of the result. ETMA is the algorithm used to analyze the bullying text within the social networking sites (SNS) such as facebook, twitter etc. The ETMA is applied on synthetic dataset collected from various data a source which consists of 5k messages belongs to bullying and non-bullying. The performance is analyzed by showing Precision, Recall, F1-Score and Accuracy.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.751-760
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• 2023

Enhancing productivity in earthwork projects is crucial, significantly affecting both time and cost efficiencies. However, existing research in this domain predominantly relies on qualitative data and methodologies, which may not suffice given its critical significance. This study employed the fuzzy DEMATEL method to conduct a structural analysis of variables affecting productivity in construction projects. The findings reveal that plan changes possess the most substantial overall influence on earthwork productivity, with a comprehensive strength rating of 4.58. Additionally, it was observed that precipitation data exerted the most pronounced positive impact, with a rating of 0.48. These insights are anticipated to aid in identifying and prioritizing areas for productivity enhancement in construction projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.