• Archives of Plastic Surgery
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• v.51 no.1
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• pp.30-35
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• 2024

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices. Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80-20 training-validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland-Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer. Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively. Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4460-4467
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• 2010

Recent advances in computer technology have brought more dependence on software to railway systems and changed to computer systems. Hence, the reliability and safety assurance of the vital software running on the embedded railway system is going to tend toward very critical task. Accordingly, various software test and validation activities are highly recommended in the international standards related railway software. In this paper, we presented an automated analysis tool and standard for software testing coverage in railway system, and presented its result of implementation. We developed the control flow analysis tool estimating test coverage as an important quantitative item for software safety verification in railway software. Also, we proposed judgement standards due to railway S/W Safety Integrity Level(SWSIL) based on analysis of standards in any other field for utilizing developed tool widely at real railway industrial sites. This tool has more advantage of effective measuring various test coverages than other countries, so we can expect railway S/W development and testing technology of real railway industrial sites in Korea.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.595-610
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• 1999

This paper presents a visual simulation tool for verification and validation(V&V) of design implications of the Object Behavior Chart developed in accordance with the existing real-time object's behavior design method. This tool can simulates the dynamic interactions using the executable simulation machine, that is EFSM(Extended Finite State Machine) and can detect various logical and temporal errors in the visual object behavior charts before a concrete implementation is made. For this, a LOTOS prototype specification is automatically generated from the visual Object Behavior Chart, and is translated into an EFSM. This system is implemented in Visual C++ version 4.2 and currently runs on PC Windows 95 environment. For simulation purpose, LOTOS was chosen because of it's excellence in specifying communication protocols. Our research contributes to the support tools for seamlessly integrating methodology-based graphical models and formal-based simulation techniques, and also contributes to the automated V&V of the Visual Models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3371-3396
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• 2016

Authentication protocols for multi-server architectures have gained momentum in recent times due to advancements in computing technologies and associated constraints. Lu et al. recently proposed a biometrics and smartcards-based authentication scheme for multi-server environment. The careful analysis of this paper demonstrates Lu et al.'s protocol is susceptible to user impersonation attacks and comprises insufficient data. In addition, this paper proposes an improved authentication with key-agreement protocol for multi-server architecture based on biometrics and smartcards. The formal security of the proposed protocol is verified using the widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our protocol can withstand active and passive attacks. The formal and informal security analysis, and performance analysis sections determines that our protocol is robust and efficient compared to Lu et al.'s protocol and existing similar protocols.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.20-27
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• 2024

Determining the number of operators who set up the machines in a human-machine system is crucial for maximizing the benefits of automated production machines. A man-machine chart is an effective tool for identifying bottlenecks, improving process efficiency, and determining the optimal number of machines per operator. However, traditional man-machine charts are lacking in accounting for idle times, such as interruptions caused by other material handling equipment. We present an adjusted man-machine chart that determines the number of machines per operator, incorporating idleness as a penalty term. The adjusted man-machine chart efficiently deploys and schedules operators for the hole machining process to enhance productivity, where operators have various idle times, such as break times and waiting times by forklifts or trailers. Further, we conduct a simulation validation of traditional and proposed charts under various operational environments of operators' fixed and flexible break times. The simulation results indicate that the adjusted man-machine chart is better suited for real-world work environments and significantly improves productivity.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.28-37
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• 2006

Recent studies in molecular biology and proteomics have identified a significant number of novel diagnostic, prognostic, and therapeutic disease markers. However, validation of these markers in clinical specimens with traditional histopathological techniques involves low throughput and is time consuming and labor intensive. Tissue microarrays (TMAs) offer a means of combining tens to hundreds of specimens of tissue onto a single slide for simultaneous analysis. This capability is particularly pertinent in the field of cancer for target verification of data obtained from cDNA micro arrays and protein expression profiling of tissues, as well as in epidemiology-based investigations using histochemical/immunohistochemical staining or in situ hybridization. In combination with automated image analysis, TMA technology can be used in the global cellular network analysis of tissues. In particular, this potential has generated much excitement in cardiovascular disease research. The following review discusses recent advances in the construction and application of TMAs and the opportunity for developing novel, highly sensitive diagnostic tools for the early detection of cardiovascular disease.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.8
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• pp.631-640
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• 2020

Currently, aircraft simulator has drawn a great attention because it has significant advantages of economic, temporal, and spatial costs compared with pilot training with real aircraft. Among the components of the aircraft simulator, flight dynamic model plays a key role in simulating the flight of an actual aircraft. Hence, it is important to verify the fidelity of flight dynamic model with an automated tool. In this paper, we develop a software to automatically verify the fidelity of the flight mechanics model for the efficient development of the aircraft simulator. After designing the software structure and GUI based on the requirements derived from the fidelity verification process, the software is implemented with C # language in Window-based environment. Experimental results on CTSW models show that the developed software is effective in terms of function, performance and user convenience.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.22-31
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• 2000

The VB5.2 interface of B-lSDN, that is located between an access network and a service node, allows dynamic allocation and release of ATM resources. In this paper, we propose the B-ANCC protocol of the VB5.2 interface to minimize the overall connection setup delay by introducing the access network. The B-ANCC protocol enhances the B-BCC protocol and adopts a parallel interworking function with signaling protocols in the service node. To confirm the correctness of the proposed B-ANCC protocol, we validate it using the automated validation tool, SPIN. We analyze and simulate the sequential interworking model based on the B-BCC protocol and the parallel interworking model based on the B-ANCC protocol, in terms of a connection setup delay and a completion ratio. It is shown that our proposed parallel interworking model with B-ANCC reduces a setup delay and improves a completion ratio compared to the sequential interworking model with B-BCC.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.