• ETRI Journal
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• v.37 no.2
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• pp.251-261
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• 2015

We present a novel method for real-time automatic license plate detection in high-resolution videos. Although there have been extensive studies of license plate detection since the 1970s, the suggested approaches resulting from such studies have difficulties in processing high-resolution imagery in real-time. Herein, we propose a novel cascade structure, the fastest classifier available, by rejecting false positives most efficiently. Furthermore, we train the classifier using the core patterns of various types of license plates, improving both the computation load and the accuracy of license plate detection. To show its superiority, our approach is compared with other state-of-the-art approaches. In addition, we collected 20,000 images including license plates from real traffic scenes for comprehensive experiments. The results show that our proposed approach significantly reduces the computational load in comparison to the other state-of-the-art approaches, with comparable performance accuracy.

• ETRI Journal
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• v.44 no.2
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• pp.232-243
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• 2022

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.496-498
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• 2023

Brain tumor segmentation problem has challenges in the tumor diversity of location, imbalance, and morphology. Attention mechanisms have recently been used widely to tackle medical segmentation problems efficiently by focusing on essential regions. In contrast, the fusion approaches enhance performance by merging mutual benefits from many models. In this study, we proposed a 3D dual fusion attention network to combine the advantages of fusion approaches and attention mechanisms by residual self-attention and local blocks. Compared to fusion approaches and related works, our proposed method has shown promising results on the BraTS 2018 dataset.

• Structural Engineering and Mechanics
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• v.26 no.2
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• pp.191-210
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• 2007

The three different approaches (reformulations) of evolution strategies (ESs) have been proposed in the literature as extensions of the technique for solving discrete problems. This study implements an extensive research on application, evaluation and comparison of them in discrete optimum design of steel frames. A unified formulation is first developed to explain these approaches, so that differences and similarities between their inherent search mechanisms can clearly be identified. Two examples from practical design of steel frames are studied next to measure their performances in locating the optimum. Extensive numerical experimentations are performed in both examples to facilitate a statistical analysis of their convergence characteristics. The results obtained are presented in the histograms demonstrating the distribution of the best designs located by each approach. In addition, an average improvement of the best design during the course of evolution is plotted in each case to compare their relative convergence rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.1979-1995
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• 2015

Network convergence is regarded as the development tendency of the future wireless networks, for which self-organization paradigms provide a promising solution to alleviate the upgrading capital expenditures (CAPEX) and operating expenditures (OPEX). Self-optimization, as a critical functionality of self-organization, employs a decentralized paradigm to dynamically adapt the varying environmental circumstances while without relying on centralized control or human intervention. In this paper, we present comprehensive surveys of heterogeneous networks (HetNets) and investigate the enhanced self-optimization models. Self-optimization approaches such as dynamic mobile access network selection, spectrum resource allocation and power control for HetNets, etc., are surveyed and compared, with possible methodologies to achieve self-optimization summarized. We hope this survey paper can provide the insight and the roadmap for future research efforts in the self-optimization of convergence networks.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1195-1209
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• 2009

In this paper, we point out some errors in a recent paper by Cordero and Torregrosa [7]. We prove the convergence of the variants of Newton's method for solving the system of nonlinear equations using two different approaches. Several examples are given, which illustrate the cubic convergence of these methods and verify the theoretical results.

• Journal of Environmental Health Sciences
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• v.43 no.3
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• pp.223-232
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• 2017

Objectives: In response to increased interest in the safety of children's products, a risk management system is being prepared through exposure assessment of hazardous chemicals. To estimate exposure levels, risk assessors are using deterministic and probabilistic approaches to statistical methodology and a commercialized Monte Carlo simulation based on tools (MCTool) to efficiently support calculation of the probability density functions. This study was conducted to analyze and discuss the usage patterns and problems associated with the results of these two approaches and MCTools used in the case of probabilistic approaches by reviewing research reports related to exposure assessment for children's products. Methods: We collected six research reports on exposure and risk assessment of children's products and summarized the deterministic results and corresponding underlying distributions for exposure dose and concentration results estimated through deterministic and probabilistic approaches. We focused on mechanisms and differences in the MCTools used for decision making with probabilistic distributions to validate the simulation adequacy in detail. Results: The estimation results of exposure dose and concentration from the deterministic approaches were 0.19-3.98 times higher than the results from the probabilistic approach. For the probabilistic approach, the use of lognormal, Student's T, and Weibull distributions had the highest frequency as underlying distributions of the input parameters. However, we could not examine the reasons for the selection of each distribution because of the absence of test-statistics. In addition, there were some cases estimating the discrete probability distribution model as the underlying distribution for continuous variables, such as weight. To find the cause of abnormal simulations, we applied two MCTools used for all reports and described the improper usage routes of MCTools. Conclusions: For transparent and realistic exposure assessment, it is necessary to 1) establish standardized guidelines for the proper use of the two statistical approaches, including notes by MCTool and 2) consider the development of a new software tool with proper configurations and features specialized for risk assessment. Such guidelines and software will make exposure assessment more user-friendly, consistent, and rapid in the future.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.19-28
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• 2011

In today and near future, most of the commercially manufactured IT products will be evolved into software convergence product. Recently, the embedded software products is called as 'Software Convergence Products.' This phenomenon does not simply show the trendy fashion, but has the seriously implication that the functionalities of IT product is accomplished and evolved via software technology, not via mechanical nor electrical means. It will become true that the quality of the convergence product is dominantly governed by the software it uses. Meanwhile, we are facing with the threatening fact that software defects in the mass products will requires tremendous amounts of cost proportional to the quantity of the product. We can remind ourselves of the disasters that have been already happened, such as Automotive recalls, Smart-phone recalls, and others. In software engineering, there have been large amount of work done in software quality improvement for the past couple of decades. Software process improvement, and testings are the representative ones. But we are facing with limitations of those traditional approaches in current convergence industry; exponentially increasing software sizes and rapid changes in software technology. In this paper, we analyze the characteristics of the software convergence industry, the limitations of the traditional Software quality improvement approaches. We suggest a new approaches in software quality improvement in different angles of thought and philosophy.

• Asia Marketing Journal
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• v.15 no.3
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• pp.1-14
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• 2013

Our research brings managerial insights for developing new digital convergence of devices and services. To explain the phenomenon of device and service convergence, we combine two different approaches from separate research fields: a perceptual mapping technique generally used for segmentation in marketing and associative network analysis mobilized to understanding network structure of core and peripheral as well as the information mediating role of nodes in network science. By combining these two approaches, we provide an in-depth analysis of the associations among devices and services by assessing the centrality of device and service nodes in an associative network. This is done by examining the connections between these services and devices as well as investigating the role of mediation in the combined device-service associative network. Our results based on bi-partite network analysis of survey responses from 250 Internet Protocol (IP) television viewers show which device and which service will play the major role in future device and service convergence as well as which characteristics and functionalities have to be incorporated into future convergence. Among the devices, the mobile handset with the betweenness centrality of 0.26 appears to be the device that would lead future device convergence. Among the services, wireless broadband with the betweenness centrality of 0.276 appears to be the service on which future service convergence needs to be developed. This result is quite unexpected, since wireless broadband has a lower penetration rate than other services, such as fixed broadband and cable TV. In addition, we indicate the possibility of converging devices, such as personal digital assistant (PDA) and mobile handset, and services, such as IPTV and mobile Internet, into wireless broadband services in the future.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.21-27
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• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.