• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.55-65
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• 2016

Detecting an object which is located at seabed is an important issue for various areas. This paper presents an approach to detection of an object that is placed at seabed in the shallow water. A conventional scheme is to employ a side-scan sonar to obtain images of a detection area and to use image processing schemes to recognize an object. Since this approach relies on high frequency signals to get clear images, its detection range becomes shorter and the processing time is getting longer. In this paper, we consider an active sonar system that is repeatedly sending a linear frequency modulated signal of 6~20 kHz in the shallow water of 100m depth. The proposed approach is to model consecutively received reflected signals and to measure their modeling error magnitudes which decide the existence of an object placed on seabed depending on relative magnitude with respect to threshold value. The feature of this approach is to only require an assumption that the seabed consists of an homogeneous sediment, and not to require a prior information on the specific properties of the sediment. We verify the proposed approach in terms of detection probability through computer simulation.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1861-1871
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• 1994

This paper presents efficient real time software implementation methods for the grayscale morphological composite function processing (FP) system. The proposed method is based on a matrix representation of the composite FP system using a basis matrix composed of structuring elements. We propose a procedure to derive the basis matrix for composite FP systems with any grayscale structuring element (GSE). It is shown that composite FP operations including morphological opening and closing are more efficiently accomplished by a local matrix operation with the basis matrix rather than cascade operations, eliminating delays and requiring less memory storage. In the second part of this paper, a VLSI implementation architecture for grayscale morphological operators is presented. The proposed implementation architecture employs a bit-serial approach which allows grayscale morphological operations to be decomposed into bit-level binary operation unit for the p-bit grayscale singnal. It is shown that this realization is simple and modular structure and thus is suitable for VLSI implementation.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.178-182
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• 2000

During the past five years, there has born tremendous interest worldwide in the possibility of exploiting chaos in wideband communication systems. Many different demodulation techniques have been proposed up to date. They can be divided into two basic categories. In the first approach, like the conventional coherent demodulation techniques, the chaotic signal has to be recovered from the received noisy signal by synchronization. However, the chaotic synchronization techniques published to data are so sensitive to the channel noise and distortion that these techniques can not be used in radio communications. In the second approach, the demodulation is carried out nonsynchronization. This paper surveys the different chaotic communication techniques that can be implemented nonsynchronization and compares the threshold and BER of the different methods. Finally, FM-DCSK is introduced the first step for apply to wideband chaos digital CDMA, where the data is not limited by the inherent nonperiodic property of the chaotic signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

• Smart Structures and Systems
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• v.6 no.3
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• pp.277-290
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• 2010

Internationally the load carrying capacity of bridges is decreasing due to material deterioration while at the same time increasing live loads mean that they are often exposed to stresses for which they were not designed. However there are limited resources available to ensure that these bridges are fit for purpose, meaning that new approaches to bridge maintenance are required that optimize both their service lives as well as maintenance costs. Wireless sensor networks (WSNs) provide a tool that could support such an optimized maintenance program. In many situations WSNs have advantages over conventional wired monitoring systems in terms of installation time and cost. In order to evaluate the potential of these systems two WSNs were installed starting in July 2007 on the Humber Bridge and on a nearby approach bridge. As part of a corrosion prevention strategy, a relative humidity and temperature monitoring system was installed in the north anchorage chambers of the main suspension bridge where the main cables of the bridge are anchored into the foundation. This system allows the Bridgemaster to check whether the maximum relative humidity threshold, above which corrosion of the steel wires might occur, is not crossed. A second WSN which monitors aspects of deterioration on a reinforced concrete bridge located on the approach to the main suspension bridge was also installed. Though both systems have provided useful data to the owners, there are still challenges that must be overcome in terms of monitoring corrosion of steel, measuring live loading and data management before WSNs can become an effective tool for bridge managers.

• Environmental Engineering Research
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• v.25 no.3
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• pp.345-355
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• 2020

This study has been carried out to evaluate the leaching behavior of Tunisian phosphogypsum (PG) tailings in Skhira city (southern Tunisia). Two PG samples, including old and freshly deposited samples, were characterized in terms of physical, geotechnical, mechanical, chemical and mineralogical properties. Special attention was paid to their leaching behavior when subjected to standard leaching tests. Our results indicated that both samples are mainly composed of more than 31.85% CaO and 31.4% SO3, indicating the predominance of gypsum. This was further confirmed by XRD patterns that revealed the presence of characteristic reflections of gypsum, brushite, quartz and Maladrite. Compressive strength after 90 d exceeded 769 kPa, but still lower than that of natural sand (1,800 kPa). Leaching test was proposed as an appropriate method to determine the released contaminants from PG. The obtained results showed that Fluorine and Phosphorus are the most released elements from PG with 40 and 30%, respectively. The released Se, Cd, and Zn were the only trace elements that exceeded the threshold limits. It seemed that leached element concentrations were independent aging or particle size of the PG. Based on the assessment of leaching behavior, an integrated management approach of the PG deposits was proposed.