• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.298-306
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• 2016

With the advent of Internet of Things (IoT) technology that allows the communications between things and devices over the Internet, a lot of researches on the IoT services, such as smart home or healthcare, have been progressed. In the existing machine-to-machine (M2M) communications, however, since the underlying link-layer technologies, such as Bluetooth or ZigBee, do not use the Internet Protocol (IP) communication, those technologies are not suitable to provide the IoT services. Accordingly, this paper discusses how to provide the Internet services in the M2M communication, and propose an implementation of the Constrained Application Protocol (CoAP) over 6LoWPAN for providing IoT services in the BLE networks. Based on the implementation, we compared the performance between HTTP and CoAP for IoT communications. From the experimental results, we can see that the CoAP protocol gives better performance than the HTTP protocol with two times higher throughput, 21% faster transmission time, and 22% smaller amount of generated packets.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1021-1028
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• 2013

The frequency of a power system should be kept within limits to produce high-quality electricity. For a power system with a high penetration of wind generators (WGs), difficulties might arise in maintaining the frequency, because modern variable speed WGs operate based on the maximum power point tracking control scheme. On the other hand, the wind speed that arrives at a downstream WG is decreased after having passed one WG due to the wake effect. The rotor speed of each WG may be different from others. This paper proposes an algorithm for assigning the droop of each WG in a wind power plant (WPP) based on the rotor speed for the virtual inertial control considering the wake effect. It assumes that each WG in the WPP has two auxiliary loops for the virtual inertial control, i.e. the frequency deviation loop and the rate of change of frequency (ROCOF) loop. To release more kinetic energy, the proposed algorithm assigns the droop of each WG, which is the gain of the frequency deviation loop, depending on the rotor speed of each WG, while the gains for the ROCOF loop of all WGs are set to be equal. The performance of the algorithm is investigated for a model system with five synchronous generators and a WPP, which consists of 15 doubly-fed induction generators, by varying the wind direction as well as the wind speed. The results clearly indicate that the algorithm successfully reduces the frequency nadir as a WG with high wind speed releases more kinetic energy for the virtual inertial control. The algorithm might help maximize the contribution of the WPP to the frequency support.

• Smart Structures and Systems
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• v.25 no.4
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• pp.487-499
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• 2020

This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.

• Applied Chemistry for Engineering
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• v.24 no.3
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• pp.219-226
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• 2013

Organic semiconductor-based soft electronics has potential advantages for next-generation electronics and displays, which request mobile convenience, flexibility, light-weight, large area, etc. Organic field-effect transistors (OFET) are core elements for soft electronic applications, such as e-paper, e-book, smart card, RFID tag, photovoltaics, portable computer, sensor, memory, etc. An optimal multi-layered structure of organic semiconductor, insulator, and electrodes is required to achieve high-performance OFET. Since most organic semiconductors are self-assembled structures with weak van der Waals forces during film formation, their crystalline structures and orientation are significantly affected by environmental conditions, specifically, substrate properties of surface energy and roughness, changing the corresponding OFET. Organo-compatible insulators and surface treatments can induce the crystal structure and orientation of solution- or vacuum-processable organic semiconductors preferential to the charge-carrier transport in OFET.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.259-264
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• 2014

IEEE 802.15.4 has been emerging as the popular choice for various monitoring and control applications. In this paper, a fault management system for DC power-lines has been designed using IEEE 802.15.4, in order to monitor DC power-lines in real time, and to rapidly detect faults and shut off the line where such faults occur. Numbers were allocated for each node and unslotted CSMA-CA method of IEEE 802.15.4 was used, the performance of which was analyzed by a simulation. For such purpose, a total of 60 bits of the control data consisting of 16 bits of the current, 16 bits of the amplitude, 28 bits of the terminal state data were sent out, and the packet transfer rate and the transmission delay time of the fault management system for DC power-lines were measured and analyzed. When the traffic load was 330 packets per second or lower, the average delay time was shown to be shorter than 0.02 seconds, and when the traffic load was 260 packets per second or lower, the packet transfer rate was shown to be 99.99% or higher. Therefore, it was confirmed that the stringent condition of US Department of Energy (DOE) could be satisfied if the traffic load was 260 packets per second or lower, The results of this study can be utilized as basic data for the establishment of the fault management system for DC power-lines using IEEE 802.15.4.

• Journal of IKEEE
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• v.21 no.4
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• pp.368-374
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• 2017

In this Paper, we developed a low power type LED security light using LED lighting that substitutes a 220[V] commercial power source for a solar cell module instead of a halogen or a sodium lamp. in addition, a PWM type drive control circuit is designed to minimize the heat generation problem and the drive current of the LED drive controller. in developed system, The light efficiency measurement value is 93.6[lm/W], and a high precision temperature sensor is used inside the controller to control the heat generation of the LED lamp. In order to eliminate the high heat generated from the LED lamp, it is designed to disperse quickly into the atmosphere through the metal insertion type heat sink. The heat control range of LED lighting was $50-55[^{\circ}C]$. The luminous flux and the lighting speed of the LED security lamp were 0.5[s], and the beam diffusion angle of the LED lamp was about $110[^{\circ}C]$ by the light distribution curve based on the height of 6[m].

• Journal of Food Hygiene and Safety
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• v.29 no.2
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• pp.105-116
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• 2014

Surveys on the consumption of caffeinated beverages by high school students (n=886) were performed. Of the students, 97.0% consumed a variety of caffeinated beverages, including carbonated drinks (90.0%), processed milk and cocoa (79.0%), coffee (63.0), teas (52.1), energy drinks (16.4%) and nourishment drinks (15.5%). The frequency of intake per student was 8.2 times per week. Caffeine intake through the caffeinated beverages was 41.53 mg/day, which was accounted for by coffee (51.5%), carbonated drinks (19.6%), processed milk and cocoa (11.5%), teas (11.4%), energy drinks (5.0%) and nourishment drinks (1.1%). Students with high levels of stress, those who consumed snacks twice a day, and those who used a computer (or smart phone) for more than 3 hours per day showed significantly higher caffeine intake. The groups with high caffeine intake experienced heart palpitations, insomnia and pollakiuria. Students indicated that they consumed the caffeinated beverages for the taste (57.9%), waking up (18.0%), thirst (13.2%), etc. (10.9%). They tended to consume drinks with a high content of caffeine to sleep less. In addition, they rarely checked the label, and showed a lack of awareness of the caffeine contents in the beverages, which calls for education.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.485-492
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• 2021

The wind turbines with a rated capacity of 50kW or less are generally considered as small class. Small wind turbines are an attractive alternative for off-grid power system and electric home appliances, both as stand-alone application and in combination with other energy technologies such as energy storage system, photovoltaic, small hydro or diesel engines. The research objective is to develop the 50kW scale wind turbine blades in ways that resemble as closely as possible with the construction and methods of utility scale turbine blade manufacturing. The mold process based on wooden form is employed to create a hollow, multi-piece, lightweight design using carbon fiber and fiberglass with an epoxy based resin. A hand layup prototyping method is developed using high density foam molds that allows short cycle time between design iterations of aerodynamic platforms. A production process of five blades is manufactured and key components of the blade are tested by IEC 61400-23 to verify the appropriateness of the design. Also, wind system with developed blades is tested by IEC 61400-12 to verify the performance characteristics. The results of blade and turbine system test showed the available design conditions for commercial operation.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.