• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.4
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• pp.255-262
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• 2016

There has been intense research on self-diagnosis systems in railway applications, since stability and reliability have become more and more significant issues. Wired sensors have been widely used in the railway vehicles, but because of the difficulty in their maintenance and accessibility, they ar not considered for self-diagnosis systems. To have a self-monitoring system, wireless data transmission and self-powered sensors are required. For this purpose, a thermoelectric energy harvesting module that can generate electricity from temperature gradient between the bogie axle box and ambient environment was introduced in this work. The temperature gradient was measured under actual operation conditions, and the behavior of the thermoelectric module with an external load resistance and booster circuits was studied. The proposed energy harvesting system can be applied for wireless sensor nodes in railroad vehicles with optimization of thermal management.

• Tunnel and Underground Space
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• v.29 no.1
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• pp.1-11
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• 2019

This study investigated use cases of machine learning technology in domestic medical, manufacturing, finance, automobile, urban sectors and those in overseas mining industry. Through a literature survey, it was found that the machine learning technology has been widely utilized for developing medical image information system, real-time monitoring and fault diagnosis system, security level of information system, autonomous vehicle and integrated city management system. Until now, the use cases have not found in the domestic mining industry, however, several overseas projects have found that introduce the machine learning technology to the mining industry for improving the productivity and safety of mineral exploration or mine development. In the future, the introduction of the machine learning technology to the mining industry is expected to spread gradually.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.137-145
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• 2008

In general, a large-scale wireless sensor network(WSNs) is composed of hundreds of or thousands of sensor nodes. In this large-scale wireless sensor networks, it is required to maintain and manage the networks to lower management cost and obtain high energy efficiency. Users should be provided with sensing service at the level of quality for users through an efficient system. In evaluating the result data quality provided from this network to users, the number of sensors related to event detection has an important role. Accordingly, the network protocol which can provide proper QoS at the level of users demanding quality should be designed in a way such that the overall system function has not to be influenced even if some sensor nodes are in error. The energy consumption is minimized at the same time. The protocol suggested in this article is based on the LEACH protocol and is a role-based self-Organization one that is appropriate for large-scale networks which need constant monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.93-102
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• 2017

Recently increasing usage of smartphones makes the Internet of Things (IoT) a leading technology that can collect and share data through sensor networks and wireless communication such as low-power Bluetooth (BLE). BLE-based application can provide operators more precise information on Automatic Door system by remotely diagnosing the system faults through wireless sensor networks and sensors around the Automatic Door. In this paper, a smart device with extended BLE module is implemented which can monitor and Control the system states and faults remotely without on-site diagnostic. while maintaining system integrity so that increase efficiency of time and costs for system management. We can use the results of this research as a basis in evaluating reliability of data between devices, extending communication module in Controller of obsolete Door systems, and establishing centralized monitoring systems in near future with multi-channel Door Controls.

• Journal of Environmental Science International
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• v.22 no.6
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• pp.733-744
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• 2013

This study was to investigate characteristics of Seoho watershed in Suwon city. $BOD_5$ and SS were selected due to the one of the important factors of the water qualities. Monitoring was conducted monthly for four years during the non-rainfall time. Also, we have been monitored $BOD_5$, $COD_{Mn}$, SS, TN and TP with two times sampling after the rainfalls. The highest concentrations of $BOD_5$ and SS were observed in downstream compare with upstream and midstream during the non-rainfall time. No change was observed in $BOD_5$ and $COD_{Mn}$ during the non-rainfall time and after the rainfalls. The monitoring result indicated that the concentration of SS was the highest in downstream after the rainfalls. We have collected the samples two times after the rainfalls. The rainfall intensity in first sampling was two times higher than second sampling. TN and TP concentrations were increased with increasing the rainfall intensity at all stream. The ESB (Ecological Score of Benthic macroinverterbrate community) index was used to evaluate the statement of stream. ESB results were identified that the upstream is protected waters and the down and midstream is reformed waters. EBS analysis results indicated that the Seoho watershed was ${\beta}$-mesosaprobic at all stream.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.121-128
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• 2014

As the permeability is an important parameter to characterize the ease with which a porous medium transmits fluids, it is usually obtained by fluid flow experiment using core samples. In order to measure the permeability, however, an experimental apparatus is required and it might take long measurement time, especially for tight samples. In this study, the relationship between permeability and porosity as well as drying rate has been investigated to predict the permeability without a series of measuring experiments. Porosity is measured by drying monitoring method, which measures weight variation continuously while drying surface-dried saturated sample, and drying rate is obtained from weight variation ratio with respect to the water saturation. The total of 6 Berea sandstone samples, which have a permeability range of 70 to 670 mD, were used in this work, and a new and empirical equation which could predict permeability of porous sandstone by using porosity and drying rate were obtained through regression analysis.

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.5
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• pp.410-419
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• 2009

Generally, air pollutant emission at workplace is estimated by two methods: indirect methods using emission factors and direct methods based on CEMS (Continuous Emission Monitoring System). CAPSS (Clean Air Policy Support System) is a representative indirect method and the national air pollutant database of Korea. However, characteristics of some workplaces may create a gap between CAPSS and CEMS data. For improving of emission data accuracy, emission data of CEMS (named CleanSYS) equipped at 138 target workplaces were compared with those of CAPSS. As a result, $SO_x$ and $PM_{10}$ emission levels obtained by CAPSS were lower than those of CleanSYS. $SO_x$ and $PM_{10}$emission ratios were 61.5% and 71.2% lower respectively, showing the biggest gaps. On the other hand, $NO_x$ emission of CAPSS was higher by 10.4%. $SO_x$ showed the biggest difference in 'Energy industry combustion' and $NO_x$ did in 'Production Process' within the SCC category. $PM_{10}$ presented a large gap in 'Manufacturing industry combustion.' The differences in $SO_x$ between the two systems occurred because some large-size facilities lack pollution controllers or efficient pollution controllers. Based on this study, CAPSS emission database of Korea will improve accuracy through adopting CEMS emission system, which enables more efficient national atmospheric policies and workplace management.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.272-281
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• 2023

Ecosystem observatories are burgeoning globally in an endeavour to detect national and global scale trends in the state of biodiversity and ecosystems in an era of rapid environmental change. In this paper we highlight the additional importance of regional scale outcomes of such infrastructure, through an introduction to the Great Western Woodlands TERN (Terrestrial Ecosystem Research Network) SuperSite, and key findings from three gradient plot networks that are part of this infrastructure. The SuperSite was established in 2012 in the 160,000 km² Great Western Woodlands region, in a collaboration involving 12 organisations. This region is globally significant for its largely intact, diverse landscapes, including the world's largest Mediterranean-climate woodlands and highly diverse sandplain shrublands. The dominant woodland eucalypts are fire-sensitive, requiring hundreds of years to regrow after fire. Old-growth woodlands are highly valued by Indigenous and non-Indigenous communities, and managing impacts of climate change and the increasing extent of intense fires are key regional management challenges. Like other TERN SuperSites, the Great Western Woodlands TERN SuperSite includes a core eddy-covariance flux tower measuring exchanges of carbon, water and energy between the vegetation and atmosphere, along with additional environmental and biodiversity monitoring around the tower. The broader SuperSite incorporates three gradient plot networks. Two of these represent aridity gradients, in sandplains and woodlands, informing regional climate adaptation and biodiversity management by characterising biodiversity turnover along spatial climate gradients and acting as sentinels for ecosystem change over time. For example, the sandplains transect has demonstrated extremely high spatial turnover rates in plant species, that challenge traditional approaches to biodiversity conservation. The third gradient plot network represents a 400-year fire-age gradient in Eucalyptus salubris woodlands. It has enabled characterisation of post-fire recovery of vegetation, birds and invertebrates over multi-century timeframes, and provided tools that are directly informing management to reduce stand-replacing fires in eucalypt woodlands. By building regional partnerships and applying globally or nationally consistent methodologies to regional scale questions, ecological observatories have the power not only to detect national and global scale trends in biodiversity and ecosystems, but to directly inform environmental decisions that are critical at regional scales.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1038-1046
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• 2017

In this paper, we propose the design of LED Streetlight Platform with capabilities of weather monitoring and color temperature control. Several previous works are focused on the energy efficiency or data management of streetlight system, but no work has been done on the lighting performance, especially when natural phenomenon such as fog or haze appears on the street and obstructs the visibility of drivers and pedestrians. To solve such issue, we propose the use of two LED lamps with different correlated color temperature, which will be activated interchangeably according to the condition on the street. We also present the design of communication scheme between each devices in the system. Moreover, our experimental results show the LED Streetlight Platform can perform well and the data can be displayed properly at the website.