• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.1-10
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• 2018

This study introduces an automated Praat script allowing optimal formant analysis for children's vowels. Using Burg's algorithm in Praat, formants can be extracted by setting the maximum formant value and the number of formants. The optimal formant setting was determined by identifying the two conditions, F1 and F2, with minimum standard deviations. When applying the optimal formant setting determined by the script, the results of normality tests were not significant among all vowels except /e/ for the maximum formant value, and among the vowels /a/, /e/, /i/, /o/, /u/ and /ʌ/ for the number of formants. This indicates that when analyzing the formants of children's vowel sounds, the unilateral application of a parameter setting (the maximum formant value and the number of formants) to all vowels is problematic. The performance of the optimal formant setting script was evaluated along with 3 different algorithm in order to determine whether it properly extracts formants for children's vowels. To this end, Korean monophghongs of 6-year-old children were collected and the Praat scripts were applied to the data. Resultant Formant plots and statistical analysis showed that optimum_script and qtone_script, which links to the perceptual unit, performed very well in formant extraction compared to the remaining 2 scripts.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.251-258
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• 2014

The purpose of the neural image analysis is to trace the velocities and the directions of moving mitochondria migrating through axons. This paper proposes an automated technique for detecting axon structure. Previously, the detection process has been carried out using a partially automated technique combined with some human intervention. In our algorithm, a consolidated image is built by taking the maximum intensity value on the all image frames at each pixel Axon detection is performed through vessel enhancement filtering followed by a peak detection procedure. In order to remove errors contained in ridge points, a filtering process is devised using a local reliability measure. Experiments have been performed using real neural image sequences and ground truth data extracted manually. It has been turned out that the proposed algorithm results in high detection rate and precision.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.517-530
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• 2007

Recently web applications have growl rapidly and have become more and more complex. As web applications become more complex, there is a growing concern about their quality. But very little attentions are paid to web applications testing and there are scarce of the practical research efforts and tools. Thus, in this paper, we suggest the automated testing methods for web applications. For this, the methods generate an analysis model by analyzing the HTML codes and the source codes. Then test targets are identified and test cases are extracted from the analysis model. In addition, test drivers and test data are generated automatically, and then they are depleted on the web server to establish a testing environment. Through this process we can automate the testing processes for web applications, besides the automated methods makes our approach more effective than the existing research efforts.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.116-122
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• 2019

In recent years, small and medium-sized businesses are rapidly changing to an industrial structure where process/quality/energy data aggregates can be automatically or real-time to achieve global competitiveness. In particular, real-time information analysis produced in the production process of small businesses is evolving into a new process process that analyzes, predicts, prescribes and implements significant performance of small businesses. In this paper, we propose a platform-building model that can transform the automated production information system of small businesses into big data so that they can upgrade data that is generated by small businesses. The proposed model has the capability to support operational efficiency (consulting and training) and strategic decision making of small businesses by utilizing a variety of data on the basic information of products produced by small businesses for data collection by smart SMEs. In addition, the proposed model is characterized by close cooperation between small and medium-sized businesses with different regional characteristics and areas of information sharing and system linkage.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.510-519
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• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.711-715
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• 1995

In the concept design stage of the product design process, it is desirable that a designer makes alternative designs sufficiently, examines and analyzes them, and finally determines an appropriate design. To efficiently investigate several alternative designs, it should be facilitated to modify the model and transfer the model data to analysis program. In this research, a concept design process for tank is automated using I-DEAS feature-based modeling system from SDRC. Additionally, the facility for the pre-estimation of the performance of product, the useful volume calculation, the mass calculation, the confirmation of the allowable workspae, and the interface to analysis propram are developed using API functions of OPen-link and Open-data. Graphic User Interface (GUI) makes it extrmely easy to utilize functions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.166-171
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• 2015

In this study, an automation tool was developed for rapid evaluation of machine tool spindle designs with automated three-dimensional finite element analysis (3D FEA) using solid elements. The tool performs FEA with the minimum data of point coordinates to define the section of the spindle shaft and bearing positions. Using object-oriented programming techniques, the tool was implemented in the programming environment of a CAD system to make use of its objects. Its modules were constructed with the objects to generate the geometric model and then to convert it into the FE model of 3D solid elements at the workbenches of the CAD system using the point data. Graphic user interfaces were developed to allow users to interact with the tool. This tool is helpful for identification of a near optimal design of the spindle based on, for example, stiffness with multiple design changes and then FEAs.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• ETRI Journal
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• v.46 no.4
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• pp.633-647
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• 2024

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.493-502
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• 2014

EnergyPlus is a whole building energy simulation program that engineers, architects, and researchers use to model energy and water use in buildings. Modeling the performance of a building with EnergyPlus enables building professionals to optimize the building design to use less energy and water. This program provides energy analysis of building and needs weather data for simulation. Weather data is available for over 2,000 locations in a file format that can be read by EnergyPlus. However, only five locations are avaliable in Korea. This study intends to use AWS data for having high spatial resolution to simulate building energy. The result of this study shows the possibility of using AWS data for energy simulation of building.