• Journal of the Korean Society of Safety
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• v.33 no.5
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• pp.21-27
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• 2018

The purpose of this study is to develop a SOP (Standard Operating Procedure) for a distribution board that can monitor the leakage current of a load distribution line in real time. The developed distribution board was fabricated by applying IEC 61439-1. It consists of the distribution board and an alarm device. The work process for making the distribution board was compliant with the KEMC (Korea Electrical Manufacturers Cooperative) regulations. And the AC distribution board range is 1,000 V. In addition, the voltage in DC is less than 1500 V. The distribution board receives a 3-phases and 4-wires power supply system and can supply power to the load of a maximum of 32 single or three phase distribution circuits. Also, leakage current measured on the power distribution board was used by sensors installed. The SOP of the developed distribution board consists of the installation standards for the short circuit alarm device and sensor, the surge protection device, switches and indication lamps, and other devices. The operation procedure was prepared so that each manufacturing step of the distribution board must be confirmed by the persons in charge of preparation, production, quality control and approval before moving forward to the next step.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1191-1193
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• 2003

Water quantity and quality monitoring at Taipei Watershed Management Bureau (WRATB) is not only a daily business but also a long term job. WRATB is responsible for providing high quality drinking water to about four millions population in Taipei. The quality of drinking water provided by WRATB is among one of the best in Taiwan. The total area is 717 square kilometers. The water resource pollution is usually divided into two categories, point source pollution and nonpoint source pollution. Garbage disposal is the most important component of the point source pollution, especially those by tourist during holidays and weekends. Pesticide pollution, fertilizer pollution, and natural pollution are the major contributions for nonpoint source pollution. The objective of this paper is to implement remote sensing, geographic information systems, and global positioning systems to monitor water quantity and water quality at WRATB. There are 12 water quality monitoring stations and four water gauge stations at WRATB. The coordinates of the 16 stations were determined by GPS devices and created into the base maps. MapObjects and visual BASIC were implemented to create application modules for water quality and quantity monitoring. Water quality of the two major watersheds at WRATB was put on Internet for public review monthly. The GIS software, ArcIMS, can put location maps and attributes of all 16 stations on Internet for general public review and technical implementations at WRATB. Inquiry and statistic charts automatic manipulations for the past 18 years are also available. Garbage disposal by community and tourist were also managed by GIS and GPS. The storage, collection, and transportation of garbage were reviewed by ArcMap file format. All garbage cart and garbage can at WRATB can be displayed on the base maps. Garbage disposal by tourist during holidays and weekends can be managed by a PDA with a GPS device and a digital camera. Man power allocation for tourist garbage disposal management can be done in an integration of GIS and GPS. Monitoring of water quality and quantity at WRATB can be done on Internet and by a PDA.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.535-545
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• 2020

This study aims to conserve and monitor energy use in public sewage treatment plants by utilizing data from the SCADA system and by controlling the aeration rate required for maintaining effluent water quality. Power consumption in the sewage treatment process was predicted using the equipment's uptime, efficiency, and inherent power consumption. The predicted energy consumption was calibrated by measured data. Additionally, energy efficiency indicators were proposed based on statistical data for energy use, capacity, and effluent quality. In one case study, a sewage treatment plant operated via the SBR process used ~30% of energy consumed in maintaining the bioreactors and treated water tanks (included decanting pump and cleaning systems). Energy consumption analysis with the K-ECO Tool-kit was conducted for unit processing. The results showed that about 58.7% of total energy consumed was used in the preliminary and biological treatment rotating equipment such as the blower and pump. In addition, the energy consumption rate was higher to the order of 19.2% in the phosphorus removal process, 16.0% during sludge treatment, and 6.1% during disinfection and discharge. In terms of equipment energy usage, feeding and decanting pumps accounted for 40% of total energy consumed following 27% for blowers. By controlling the aeration rate based on the proposed feedback control system, the DO concentration was reduced by 56% compared pre-controls and the aeration amount decreased by 28%. The overall power consumption of the plant was reduced by 6% via aeration control.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.440-445
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• 2007

This paper deals with assessment of risk component for electrical safety and investigation on the spot of computer room in elementary middle high school. The investigation was carried out side by side for floor, outlet, panel board, earth leakage circuit breaker at computer room In order to assess electrical safety at computer room, grounding simulator and power quality monitor have been used. Potential rise has been measured and analyzed for ground rod and grounding grid by using the simulator. Phase and neutral-line current have been monitored in real time. As a consequence, it is desirable for us to install conductive tile at floor of computer room Grounding grid had better than ground rod for electrical safety. Neutral-line current was produced by unbalanced phase current.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3079-3089
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• 2015

In this paper, a smart constrained application protocol (CoAP)-based gateway with a border router is proposed for home safety services to remotely monitor the trespass, fire, and indoor air quality. The smart CoAP gateway controls a home safety sensor node with a pyroelectric infrared motion sensor, a fire sensor, a humidity and temperature sensor, and a non-dispersive infrared CO2 sensor and gathers sensing data from them. In addition, it can convert physical sensing data into understandable information and perform packet conversion as a border router for seamless connection between a low-power wireless personal area network (6LoWPAN) and the Internet (IPv6). Implementation and laboratory test results verify the feasibility of the smart CoAP gateway which especially can provide about 97.20% data throughput.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.214-221
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• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.619-628
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• 2012

The ZigBee network has been considered to monitor electricity usage of home appliances in the smart grid network. ZigBee, however, may suffer from a coexistence problem with wireless local area network (WLAN). In this paper, to resolve the coexistence problem between ZigBee network and WLAN, we propose a new protocol constructing a cognitive smart grid network for supporting monitoring of home appliances. In the proposed protocol, home appliances first estimates the transmission timing and channel information of WLAN by reading request to send/clear to send (RTS/CTS) frames of WLAN. Next, based on the estimated information, home appliances transmit a data at the same time as WLAN transmission. To manage the interference between WLAN and smart grid network, we propose a cognitive beamforming algorithm. The beamforming algorithm is designed to guaranteeing zero interference to WLAN while satisfying a required rate for smart metering. We also propose an energy efficient rate adaptation algorithm. By slowing down the transmission rate while satisfying an imperceptible impact of quality of service (QoS) of the receiver, the home appliance can significantly save transmit power. Numerical results show that the proposed multiple antenna technique provides reliable communications for smart metering with reduced power comparing to the simple transmission technique.

• Journal of Welding and Joining
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• v.30 no.4
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• pp.24-30
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• 2012

Laser welding by fiber laser accompanied by a lot of spatter and humping bead. This is because the deep and narrow keyhole usually form due to high beam quality. So the weld bead is formed defects, because the plasma jet with a high vapor pressure make the molten pool on keyhole wall scattered. For such a reason, unstable behavior of keyhole is difficult to monitor laser welding by using the laser induced plasma. Mostly, fiber laser welding of thick plates most be influenced by this effect. Therefore, fiber laser welding has been difficult to apply the sole. Thus, laser welding monitoring based on plasma measurements have much difficulty in measurements and analysis of signal. In this study, influence of the plasma emission signal according to welding speed and laser power in fiber laser welding analysed by using RMS and FFT analysis. We can verify that RMS value of the plasma emission signal changes with welding parameters in fiber laser welding, and aspect ratio greater than 1, the peak of FFT frequency had been moved in accordance with welding parameter.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.