• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1066-1073
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• 2013

This paper illustrates newly an outage cost impact index function(OCIIF). The assessment of the OCIIF is described using the Web Based Online Real-time Outage Cost Assessment and Information System(WOROCAIS) for power system outage cost assessment in Korea. The proposed OCIIF is not absolute but relative outage cost impact index function in view point of outage time using web based survey method for outage cost assessment. While conventional methodology does not consider short time outage cost assessment, the proposed OCIIF reflects short time outage. SCOF(Sector Customer Outage Function) in stead of the traditional SCDF(Sector Customer Damage Function) is defined and proposed newly in this paper. Based the SCOF, AVLL(Average Value of Loss Load) is newly proposed. The OCIIF is demonstrated by WOROCAIS in case study around 2,000 sample data surveyed by KEPCO in South Korea in recent.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.41-52
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• 2018

This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2053-2067
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• 2023

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods.

• Journal of Construction Engineering and Project Management
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• v.9 no.4
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• pp.16-33
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• 2019

The increasing size and complexity of modern construction projects demands mature capabilities of onsite personnel with regard to recognizing unsafe situations. Construction safety training is paper or computer-based and suffers from a distinct gap between the classroom training environment and real-world construction sites; even trained personnel can find it difficult to recognize many of the potential safety hazards at their jobsites even after receiving construction safety training. Immersive technologies can overcome the current limitations in construction safety training by reducing the gap between the classroom and a real construction environment. This research developed and tested a new Augmented Reality (AR)-based assessment tool to evaluate the hazard recognition skills of students majoring in construction management as part of a construction safety course. The quantitative and qualitative results of this research confirmed that AR-based assessment can become a very effective assessment tool to evaluate safety knowledge and skills in a construction safety course, outperforming both paper and computer-based assessment methods. The students preferred AR-based assessment because it provides a realistic visual context for real world safety hazards.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.61-66
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• 2015

In this paper, we draw a security assessment by analyzing possible vulnerabilities of the designated PC service which is supposed for strengthening security of current online identification methods that provide various areas such as the online banking and a game and so on. There is a difference between the designated PC service and online identification methods. Online identification methods authenticate an user by the user's private information or the user's knowledge-based information, though the designated PC service authenticates a hardware-based unique information of the user's PC. For this reason, high task significance services employ with online identification methods and the designated PC service for improving security multiply. Nevertheless, the security assessment of the designated PC service has been absent and possible vulnerabilities of the designated PC service are counterfeiter and falsification when the hardware-based unique-information is extracted on the user's PC and sent an authentication server. Therefore, in this paper, we analyze possible vulnerabilities of the designated PC service and draw the security assessment.

• Steel and Composite Structures
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• v.20 no.6
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• pp.1345-1368
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• 2016

This paper presents the probabilistic-based assessment of composite steel-concrete structures through an innovative framework. This framework combines model identification and reliability assessment procedures. The paper starts by describing current structural assessment algorithms and the most relevant uncertainty sources. The developed model identification algorithm is then presented. During this procedure, the model parameters are automatically adjusted, so that the numerical results best fit the experimental data. Modelling and measurement errors are respectively incorporated in this algorithm. The reliability assessment procedure aims to assess the structure performance, considering randomness in model parameters. Since monitoring and characterization tests are common measures to control and acquire information about those parameters, a Bayesian inference procedure is incorporated to update the reliability assessment. The framework is then tested with a set of composite steel-concrete beams, which behavior is complex. The experimental tests, as well as the developed numerical model and the obtained results from the proposed framework, are respectively present.

• Proceedings of the Korea Concrete Institute Conference
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• 1996.10a
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• pp.532-539
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• 1996

Existing assessment methodologies present a considerable problem because of fuzzy situation of deterioration mechanism of concrete bridges; namely, qualitative, subjective or inconsistent. This paper discusses current assessment methods in aspect of uncertainty. The expert system, COBDA, is developed for consistent and fast assessment of deteriorantion of concrete bridges. Briefly introduced in this paper are the structure of expert system and several methodologies for decision making of deterioration situation and providing repair option. COBDA is configured by PROLOG for logic approach and expert system shell based on Bayesian subjective probability. The methodologies are illustrated and discussed by comparison of condition assessment results in a case study.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1589-1598
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• 1999

Conventional MSE or PSNR based methods for objective picture quality assessment of moving picture coder are not well correlated with subjective human evaluation. In recent years, the design of better objective quality assessment has attracted much intention and several picture quality metrics based on the properties of Human Visual System has been proposed. This paper proposes new metric which is appropriate for objective picture quality assessment of block based moving picture coder by considering frequency sensitivity, inter-intra channel masking and several distortion artifacts caused by block based coding. The experimental results show that the proposed method is good correlated with subjective assessment.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.593-600
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• 2021

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.

• International Journal of Railway
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• v.2 no.2
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• pp.50-59
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• 2009

Numerous train operating companies provide training by means of driving simulators. A detailed analysis in the course of the rail research project 2TRAIN has shown that the simulation technology, the purposes of training and the overall concept of simulator-based training are rather diverse (Schmitz & Maag, 2008). A joint factor however are weak assessment capabilities and the fact that the simulator training is often not embedded into the overall competence management. This fact hinders an optimal use of the simulators. Therefore, 2TRAIN aims at the development of enhanced training and assessment tools. Taking into account that several simulators are already in use, the focus lays on the extension of existing simulation technology instead of developing entirely new systems. This extension comprises (1) a common data simulation interface (CDSI), (2) a rule-based expert system (ExSys), (3) a virtual instructor (VI), and (4) an _assessment database (AssDB). The foundation of this technical development is an assessment concept (PERMA concept) that is based on performance markers. The first part of the paper presents this assessment concept and a process model for the two major steps of driver performance assessment, i.e. (1) the specification of exercise and assessment and (2) the assessment algorithm and execution of the assessment. The second part describes the rationale and the functionalities of the simulator add-on tools. Finally, recommendations for further technical improvement and appropriate usage are given. based on the results of a pilot study.