• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.347-359
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• 2010

Ortho-photos provide valuable spatial and spectral information for various Geographic Information System (GIS) and mapping applications. The absence of relief displacement and the uniform scale in ortho-photos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for ortho-photo generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true ortho-photo generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the Z-buffer method has been one of the most popular methods for true ortho-photo generation. However, it is quite sensitive to the relationship between the cell size of the Digital Surface Model (DSM) and the Ground Sampling Distance (GSD) of the imaging sensor. Another critical issue of true ortho-photo generation using high resolution satellite imagery is the scan line search. In other words, the perspective center corresponding to each ground point should be identified since we are dealing with a line camera. This paper introduces alternative methodology for true ortho-photo generation that circumvents the drawbacks of the Z-buffer technique and the existing scan line search methods. The experiments using real data are carried out while comparing the performance of the proposed and the existing methods through qualitative and quantitative evaluations and computational efficiency. The experimental analysis proved that the proposed method provided the best success ratio of the occlusion detection and had reasonable processing time compared to all other true ortho-photo generation methods tested in this paper.

• Safety and Health at Work
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• v.1 no.1
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• pp.19-28
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• 2010

Industrial Safety and Health Law (ISH Law) of Japan requires abnormalities identified in evaluations of worker health and working environments are reported to occupational physicians, and employers are advised of measures to ensure appropriate accommodations in working environments and work procedures. Since the 1980s, notions of a risk assessment and occupational safety and health management system were expected to further prevent industrial accidents. In 2005, ISH Law stipulated workplace risk assessment using the wording "employers shall endeavor." Following the amendment, multiple documents and guidelines for risk assessment for different work procedures were developed. They require ISH Laws to be implemented fully and workplaces to plan and execute measures to reduce risks, ranking them from those addressing potential hazards to those requiring workers to wear protective articles. A governmental survey in 2005 found the performance of risk assessment was 20.4% and common reasons for not implementing risk assessments were lack of adequate personnel or knowledge. ISH Law specifies criminal penalties for both individuals and organizations. Moreover, under the Labor Contract Law promulgated in 2007, employers are obliged to make reasonable efforts to ensure employee health for foreseeable and avoidable risks. Therefore, enterprises neglecting even the non-binding provisions of guidelines are likely to suffer significant business impact if judged to be responsible for industrial accidents or occupational disease. To promote risk assessment, we must strengthen technical, financial, and physical support from public-service organizations, encourage the dissemination of good practices to reduce risks, and consider additional employer incentives, including relaxed mandatory regulations.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.263-270
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• 2021

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.315-329
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• 2018

Recently, RC transfer slab systems have been used widely to construct high-rise wall-type apartments for securing parking space or public space. However, it is problem that the design method and structural performance evaluation method developed for thin RC flat slab are still used in the design of the transfer slab whose thickness is very thick and therefore structural behavior is expected to be different from RC flat slab. Thus, for the rational design of the transfer slab, the ultimate shear behavior of the RC transfer slab system is required to be analyzed properly. Accordingly, in the present study, the two-way shear behavior of the transfer slab was analyzed using nonlinear FEM according to various design parameters such as thickness of the transfer slab, strength of concrete, shear span ratio, and reinforcement ratio. In addition, the two-way shear strength evaluations of RC transfer slab by the existing evaluation methods were verified by comparing those with the results of nonlinear FEM analysis.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1303-1314
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• 2019

This paper studies the harmonic characteristics of ship electric propulsion systems and their treatment methods. It also adopts effective measures to suppress and prevent ship power systems from affecting ship operation due to the serious damage caused by harmonics. Firstly, the harmonic characteristics of a ship electric propulsion system are reviewed and discussed. Secondly, aiming at problems such as resonant frequency and filter characteristics variations, resonance point migration, and unstable filtering performances in conventional passive filters, a method for fully tuning of a passive dynamic tunable filter (PDTF) is proposed to realize harmonic suppression. Thirdly, to address the problems of the uncontrollable inductance L of traditional air gap iron core reactors and the harmonics of power electronic impedance converters (PEICs), this paper proposes an electromagnetic coupling reactor with impedance transformation and harmonic suppression characteristics (ECRITHS), with the internal filter (IF) designed to suppress the harmonics generated by PEICs. The ECRITHS is characterized by both harmonic suppression and impedance change. Fourthly, the ECRITHS is investigated. This investigation includes the harmonic suppression characteristics and impedance transformation characteristics of the ECRITHS at the fundamental frequency, which shows the good performance of the ECRITHS. Simulation and experimental evaluations of the PDTF are carried out. Multiple PDTFs can be configured to realize multi-order simultaneous dynamic filtering, and can effectively eliminate the current harmonics of ship electric propulsion systems. This is done to reduce the total harmonic distortion (THD) of the supply currents to well below the 5% limit imposed by the IEEE-519 standard. The PDTF also can eliminate harmonic currents in different geographic places by using a low voltage distribution system. Finally, a detailed discussion is presented, with challenges and future implications discussed. The research results are intended to effectively eliminate the harmonics of ship electric power propulsion systems and to improve the power quality of ship power systems. This is of theoretical and practical significance for improving the power quality and power savings of ship power systems.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.85-95
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• 2018

With the popularity of smartphones, most peoples have been using mobile cameras to capture photographs. However, due to insufficient amount of lights in a low lighting condition, unwanted noises can be generated during image acquisition. To remove the noise, a method of using deep convolutional neural networks is introduced. However, this method still lacks the ability to describe textures and edges, even though it has made significant progress in terms of visual quality performance. Therefore, in this paper, the HOG (Histogram of Oriented Gradients) images that contain information about edge orientations are used. More specifically, a method of learning deep convolutional neural networks is proposed by stacking noise and HOG images into an input tensor. Experiment results confirm that the proposed method not only can obtain excellent result in visual quality evaluations, compared to conventional methods, but also enable textures and edges to be improved visually.

• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.1-17
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• 2021

Pseudo relevance feedback (PRF) is a powerful query expansion (QE) technique that prepares queries using the top k pseudorelevant documents and choosing expansion elements. Traditional PRF frameworks have robustly handled vocabulary mismatch corresponding to user queries and pertinent documents; nevertheless, expansion elements are chosen, disregarding similarity to the original query's elements. Word embedding (WE) schemes comprise techniques of significant interest concerning QE, that falls within the information retrieval domain. Deep averaging networks (DANs) defines a framework relying on average word presence passed through multiple linear layers. The complete query is understandably represented using the average vector comprising the query terms. The vector may be employed for determining expansion elements pertinent to the entire query. In this study, we suggest a DANs-based technique that augments PRF frameworks by integrating WE similarities to facilitate Arabic information retrieval. The technique is based on the fundamental that the top pseudo-relevant document set is assessed to determine candidate element distribution and select expansion terms appropriately, considering their similarity to the average vector representing the initial query elements. The Word2Vec model is selected for executing the experiments on a standard Arabic TREC 2001/2002 set. The majority of the evaluations indicate that the PRF implementation in the present study offers a significant performance improvement compared to that of the baseline PRF frameworks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1293-1300
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• 2012

With the rapid evolution of mobile devices and the development of wireless networks, users of mobile social network service on smartphone have been increasing. Also the security of personal information as a result of real-time communication and information-sharing are becoming a serious social issue. In this paper, a framework that can be linked with a social network services platform is designed using OpenAPI. In addition, we propose an authentication and detection mechanism to enhance the level of personal information security. The authentication scheme is based on an user ID and password, while the detection scheme analyzes user-designated input patterns to verify in advance whether personal information protection guidelines are met, enhancing the level of personal information security in a social network service environment. The effectiveness and validity of this study were confirmed through performance evaluations at the end.