• Tuberculosis and Respiratory Diseases
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• 제86권1호
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• pp.23-32
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• 2023

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

• Advances in Energy Research
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• 제5권1호
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• pp.1-12
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• 2017

The world today has reached a certain level where it is impossible to get the quality education at the tertiary level without the use of Information and Communication Technology (ICT). ICT has made life better, communication easier and faster, teaching and learning more practical through computers and other technology based learning tools. However, despite these benefits ICT has equally contributed immensely to environmental problems. Therefore there is the need to use ICT resources efficiently in universities for environmental sustainability so as to save both the university environment and the world at large from the effects of global warming. This paper evaluates the carbon footprints from the use of ICT devices and comes up with a proposed green ICT framework to reduce the carbon footprints in universities. The framework contains techniques and approaches to achieve greenness in the data center, personal computers (PCs) and monitors, and printing in order to make ICT more environmentally friendly, cheaper, safer and ultimately more efficient. Concerned experts in their respective departments at Asia Pacific University of Technology and Innovation (APU) Malaysia evaluated the proposed framework. It was found to be effective for achieving efficiency, reducing energy consumption and carbon emissions.

• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• 한국유기농업학회지
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• 제14권3호
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• pp.267-285
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• 2006

This study diagnosed the managerial ability of farmers in the Gokseong area and the Gwangyang area, and analysis was conducted especially placing focus on the difference in managerial ability on whether environmental friendly agriculture has been implemented or not. According to the analysis results, the average score of all the farmers was 63.9 out of the full score of 100 which can be seen to be a medium to high standard. Also, in the diagnosis results according to whether environmental friendly agriculture has been implemented or not, the ability scores of farmers practicing environmental friendly agriculture were higher than those who were not, but large differences between the item scores for information ability and cooperative ability were shown. This is connoting a very important meaning in terms of the point that correspondence is being made to the conditions required to possess market response capabilities to the high information systemization society to unfold in the future. As a result, the important issue can be seen to be personnel cultivation in order to possess self response capabilities through the development of education programs that can improve this kind of managerial ability and revision of support systems.

• 환경영향평가
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• 제10권2호
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• pp.135-145
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• 2001

This study was conducted to investigate the practical using of Geographic Information System(GIS) technology which are computer-based systems that are used to store and manipulate geographic information on the air pollution control and management in the macro city. For this study 130 samples were corrected by passive sampler in Seoul (25 distincts) distributed by TM-coordinate during November in 1997 to January 1998, and analysed by GC/MSD for 16 VOCs e.g., toluene, benzene and display using Arc/view GIS(version 3.2, ESRI Inc, U.S.A) for windows. The most VOCs concentration distribution in November, 1997 was higher than that of January, 1998 except benzene and 1,1,2-trichroloethan, bromobenzene. And products of the distribution of VOCs concentration display using GIS technology was effective as well as other display methods(e.g., contouring method, pie or column chart, graduated symbols), especially in mapping and symbolization capabilities for spatial pollutant status evaluation were very effective than other display methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1279-1300
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• 2017

In the interweave cognitive networks, the interference from the primary user degrades the performance of the cognitive user transmissions. In this paper, we propose an adaptive cooperation scheme in the interweave cognitive networks to improve the performance of the cognitive user transmissions. In the proposed scheme for the bidirectional communication of two end-source cognitive users, the bidirectional communication is completed through the non-relay direct transmission, the one-way relaying cooperation transmission, and the two-way relaying cooperation transmission depending on the limited feedback from the end-sources. For the performance analysis of the proposed scheme, we derive the outage probability and the finite-SNR diversity multiplexing tradeoff (f-DMT) in a closed form, considering the imperfect spectrum sensing, the interference from the primary user, and the power allocation between the relay and the end-sources. The results show that compared with the direct transmissions (DT), the pure one-way relaying transmissions (POWRT), and the pure two-way relaying transmissions (PTWRT), the proposed scheme has better outage performance. In terms of the f-DMT, the proposed scheme outperforms the full cooperation transmissions of the POWRT and PTWRT.

• Journal of Mechanical Science and Technology
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• 제17권12호
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Coupled systems mechanics
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• 제8권6호
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• pp.523-535
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• 2019

Artificial Intelligence (AI) is anticipated to be the future of technology. Hence, AI has been applied in various fields over the years and its applications are expected to grow in number with the passage of time. There has been a growing need for accurate, direct, and quick prediction of geotechnical and foundation engineering models especially since the success of each project relies on numerous amounts of data. In this study, two applications of AI in the field of geotechnical and foundation engineering are presented - spatial interpolation of standard penetration test (SPT) data and prediction of consolidation of clay. SPT and soil profile data may be predicted and estimated at any location and depth at a site that has no available borehole test data using artificial intelligence techniques such as artificial neural networks (ANN) based on available geospatial information from nearby boreholes. ANN can also be used to accelerate the calculation of various theoretical methods such as the one-dimensional consolidation theory of clay with high efficiency by using lesser computation resources. The results of the study showed that ANN can be a valuable, powerful, and practical tool in providing various information that is needed in geotechnical and foundation design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.