• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.158-169
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• 2012

Sabah has experienced a rapid decline in the extent of forest cover. The precise impact of habitat loss on the conservation status of the plants of Sabah is uncertain. In this study we use the niche modelling algorithm MAXENT to construct preliminary, revised and final ecological niche models for Dipterocarpus lamellatus and Dipterocarpus ochraceus and combined these models with data on current land-use to derive conservation assessments for each species. Preliminary models were based on herbarium data alone. Ground surveys were conducted to evaluate the performance of these preliminary models, and a revised niche model was generated from the combined herbarium and ground survey data. The final model was obtained by constraining the predictions of the revised models by filters. The range overlap between the preliminary and revised models was 0.47 for D. lamellatus and 0.39 for D. ochraceus, suggesting poor agreement between them. There was substantial variation in estimates of habitat loss for D. ochraceus, among the preliminary, revised and constrained models, and this has the potential to lead to incorrect threat assessments. From these estimates of habitat loss, the historic distribution and estimates of population size we determine that both species should be classified as Critically Endangered under IUCN Red List guidelines. Our results suggest that ground-truthing of ecological niche models is essential, especially if the models are being used for conservation decision making.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1054-1061
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• 2016

Development of an accurate infrared signature (IR) measurement system is expected to contribute in the development of low observable technology and the spectroscopic analysis of electromagnetic radiation. Application of a spectroradiometer (SR) allows for the measurement of detailed infrared signature from the exhaust plume due to its own heat source. Establishment of a measurement system using a micro-turbo engine is intended to simulate the modelling of the aircraft plume. The engine was installed on a test stand to measure the engine performance. The IR signature was measured by placing the SR perpendicular to the axis line of the exhaust plume. Reference data from the blackbody were also measured to calibrate the raw data, and the infrared signature of the background was also measured for comparison with that of the plume. The calibrated spectral radiance was obtained through the data reduction process and the results were analyzed in specific bands. The experiments revealed that the measurement system established here showed sufficient performance for further comprehensive analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.393-402
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• 2018

While interacting with a sloping structure, an ice floe may fracture in different patterns. For example, it can be local bending failure or global splitting failure depending on the contact properties, geometry and confinement of the ice floe. Modelling these different fracture patterns as a natural outcome of numerical simulations is rather challenging. This is mainly because the effects of crack propagation, crack branching, multi fracturing modes and eventual fragmentation within a solid material are still questions to be answered by the on-going research in the Computational Mechanic community. In order to simulate the fracturing of ice floes with arbitrary geometries and confinement; and also to simulate the fracturing events at such a large scale yet with sufficient efficiency, we propose a semi-analytical/empirical and semi-numerical approach; but with focus on the global splitting failure mode in this paper. The simulation method is validated against data we collected during the Oden Arctic Technology Research Cruise 2015 (OATRC2015). The data include: 1) camera images based on which we specify the exact geometry of ice floes before and after an impact and fracturing event; 2) IMU data based on which the global dynamic force encountered by the icebreaker is extracted for the impact event. It was found that this method presents reasonably accurate results and realistic fracturing patterns upon given ice floes.

• Journal of the Korean Society of Costume
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• v.59 no.6
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• pp.1-15
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• 2009

The purpose of this study is to examine the effect of the cause marketing on fashion brand credibility, purchase and words of mouth ("WOM") intentions. This study adopted a survey method with the questionnaire. Data were collected from respondents who were graduated from higher than elementary school students. The data were analyzed by using statistic methods such as frequency analysis, factor analysis, reliability test and structural equation modelling. The results of the data analysis of this study are as follows; firstly, brand familiarity and customer attitude have a directly positive effect on the credibility, purchase and WOM intentions. Fashion business companies should work up the ways of communications with the customers besides cause marketing for the brand familiarity. It is necessary for customers to be known about how the fashion business companies participate in cause marketing to enhance the value of positive brand attitude. Secondly, while the brand familiarity on the credibility, purchase and WOM intentions are effective to female customers, the brand attitude is effective to male customers. Thirdly, even though it was showed that the credibility of fashion brands have no significant effects on purchase and WOM intention, it is due to its own nature of fashion business and it is founded in preceding research that high-involved fashion products may have different results in comparison with the practical products.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.230-238
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• 1999

This paper describes a research work of developing computer-aided design of lead frame, semiconductor, with blanking operation which is very precise for progressive working. Approach to the system is based on the knowledge-based rules. Knowledge for the system is formulated from plasticity theories, experimental results and the empirical knowledge of field experts. This system has been written in AutoLISP on the AutoCAD using a personal computer and in I-DEAS Drafting Programming Language on the I-DEAS Master Series Drafting with Workstation, HP9000/715(64) and tool kit on the ESPRIT. Transference of data among AutoCAD, I-DEAS Master Series Drafting, and ESPRIT is accomplished by DXF(drawing exchange format) and IGES(initial graphics exchange specification) methods. This system is composed of six modules, which are input and shape treatment, production feasibility check, strip-layout, die-layout, modelling, and post-processor modules. The system can design process planning and Die design considering several factors and generate NC data automatically according to drawings of die-layout module. As forming process of high precision product and die design system using 2-D geometry recognition are integrated with technology of process planning, die design, and CAE analysis, standardization of die part in die design and process planning of high pression product for semiconductor lead frame is possible to set. Results carried out in each module will provide efficiencies to the designer and the manufacturer of lead frame, semiconductor.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.61-72
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• 2013

The relationship between export growth and economic growth in developing countries has been one of the main issues in the growth theory field. Many of empirical studies have been done during the last three decades in order to investigate the export-led growth hypothesis using either time-series or cross-sectional data mainly in developing countries. This paper applies cointegration and error correction models to test causal relationship between export growth and economic growth in the Korean 8 manufacturing industries using the industrial time-series quarterly data over 1975-2010. The export-output relationship is tested by including industrial capital stock and the industrial labor force as exogenous variables. The cointegration and error-correction modelling technique with industrial export and output data have showed the strong evidence that there is a bi-directional causality between industrial export and industrial output in 6 manufacturing industries except wood & pulp and nonmetallic industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1337-1358
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• 2015

In this paper, we present an analytical and simulated study on the performance of adaptive vehicular ad hoc networks (VANET) priority based on Transmission Distance Reliability Range (TDRR) and data type. VANET topology changes rapidly due to its inherent nature of high mobility nodes and unpredictable environments. Therefore, nodes in VANET must be able to adapt to the ever changing environment and optimize parameters to enhance performance. However, there is a lack of adaptability in the current VANET scheme. Existing VANET IEEE802.11p's Enhanced Distributed Channel Access; EDCA assigns priority solely based on data type. In this paper, we propose a new priority scheme which utilizes Markov model to perform TDRR prediction and assign priorities based on the proposed Markov TDRR Prediction with Enhanced Priority VANET Scheme (MarPVS). Subsequently, we performed an analytical study on MarPVS performance modeling. In particular, considering five different priority levels defined in MarPVS, we derived the probability of successful transmission, the number of low priority messages in back off process and concurrent low priority transmission. Finally, the results are used to derive the average transmission delay for data types defined in MarPVS. Numerical results are provided along with simulation results which confirm the accuracy of the proposed analysis. Simulation results demonstrate that the proposed MarPVS results in lower transmission latency and higher packet success rate in comparison with the default IEEE802.11p scheme and greedy scheduler scheme.

• Advances in environmental research
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• v.6 no.4
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• Journal of KIISE
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• v.44 no.8
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• pp.782-790
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• 2017

The service, 'Hot Search Keywords', provides a list of the most hot search terms of different web services such as Naver or Daum. The service, bases the changes in rank of a specific search keyword on changes in its users' interest. This paper introduces a temporal modelling framework for predicting the rank change of hot search keywords using past rank data and machine learning. Past rank data shows that more than 70% of hot search keywords tend to disappear and reappear later. The authors processed missing rank value, using deletion, dummy variables, mean substitution, and expectation maximization. It is however crucial to calculate the optimal window size of the past rank data. We proposed an optimal window size selection approach based on the minimum amount of time a topic within the same or a differing context disappeared. The experiments were conducted with four different machine-learning techniques using the Naver, Daum, and Nate 'Hot Search Keywords' datasets, which were collected for 2 years.

• Journal of IKEEE
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• v.20 no.4
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• pp.398-411
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• 2016

SVM, a machine learning method, is widely used in image processing for it's excellent generalization performance. However, to add other data to the pre-trained data of the system, we need to train the entire system again. This procedure takes a lot of time, especially in embedded environment, and results in low performance of SVM. In this paper, we implemented an SVM trainer and classifier in an FPGA to solve this problem. We parlallelized the repeated operations inside SVM and modified the exponential operations of the kernel function to perform fixed point modelling. We implemented the proposed hardware on Xilinx ZC 706 evaluation board and used TSR algorithm to verify the FPGA result. It takes about 5 seconds for the proposed hardware to train 2,000 data samples and 16.54ms for classification for $1360{\times}800$ resolution in 100MHz frequency, respectively.