• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.178-183
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• 2012

The design of the integrated quench protection (QP) system for the high current superconducting magnet (SCM) has been fabricated and tested for the toroidal field (TF) coil system of the Korea Superconducting Tokamak Advanced Research (KSTAR) device. The QP system is capable of protecting the TF SCM, which consists of 16 identical coils serially connected with a stored energy of 495 MJ at the design operation level at 35.2 kA per turn. Given that the power supply for the TF coils can only ramp up and maintain the coil current, the design of the QP system includes two features. The first is a basic fast discharge function to protect the TF SCM by a dump resistor circuit with a 7 s time constant in case of coil quench event. The second is a slow discharge function with a time constant of 360 s for a daily TF discharge or for a stop demand from the tokamak control system. The QP system has been successfully tested up to 40 kA with a short circuit and up to 34 kA with TF SCM in the second campaign of KSTAR. This paper describes the characteristics of the TF QP systems and test results of the plasma experiment of KSTAR in 2009.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.287-292
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• 2021

In this paper, we propose an efficient CNN application method by analyzing the effect of activation function on the failure diagnosis of the inductive motor stator. Generally, the main purpose of the inductive motor stator failure diagnosis is to prevent the failure by rapidly diagnosing the minute turn short. In the application of activation function, experiments show that the Sigmoid function is 23.23% more useful in accuracy of diagnosis than the ReLu function, although it is shown that ReLu has superiority in overall fixer failure in utilizing the activation function.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.305-312
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• 2021

Background: Butterflies make an important part for plant-pollinator guild. These are nectar feeder or occasionally pollen feeder and thus proboscis of the butterfly species are considered as one of the most important variable in relation to the collection of food from plants. In butterfly-plant association, nectar source is principally determined by quality of nectar, corolla length, and nectar quantity. For the butterfly, nectar uptake is determined by proboscis length because flowers with long corolla restrict butterfly species containing shorter proboscis. Empirical studies proved that butterfly species with high wing loading visit clustered flowers and species with low wing loading confined their visit to solitary or less nectar rich flowers. The present study tries to investigate the flower preference of butterfly species from Lycaenidae family having very short proboscis, lower body length, lower body weight and wing span than the most species belonging from Nymphalidae, Pieridae, Papilionidae, and Hesperiidae. Results: Butterflies with shorter proboscis cannot access nectar from deeper flower. Although they mainly visit on less deeper flower to sucking nectar, butterflies with high wing loading visits clustered flowers to fulfill their energy requirements. In this study, we demonstrated flower choice of seven butterfly species belonging to Lycanidiae family. The proboscis length maintains a positive relationship with body length and body weight. Body length maintains a positive relationship with body weight and wing span. Wing span indicate a strong positive relationship with body weight. This study proved that these seven butterfly species namely Castalius rosimon (CRN), Taracus nara (TNA), Zizinia otis (ZOT), Zizula hylax (ZHY), Jamides celeno (JCE), Chilades laius (CLA), and Psuedozizeeria maha (PMA) visit frequently in Tridax procumbens (TPR), Ocimum americanum (OAM) and Syndrella nodiflora (SNO). The species do not visit Lantana camara (LCA) and Catharanthus roseus (CRO) plants. Conclusion: The present study proved that butterfly species visits frequently in Tridax procumbens (TPR), Ocimum americanum (OAM) but less frequently in Syndrella nodiflora (SNO). So, that study determined the butterfly species helps in pollination of these herbs that in turn helps the conservation of these butterfly species.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.199-224
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• 2018

Aviation's safety record continues to improve yearly, especially with respect to passenger and crew injuries and deaths. However, although the number of accidents has decreased over the decades, there are still many events, such as landings short of the runway and runway excursions, both of which pose threats to passenger and crew safety. Surviving any kind of aviation accident depends on the physiological threat and stress of the impact(s), the extent to which the physical structure surrounding the passengers and crew remains intact, and the ability of the passengers and crew to be able to escape the wreckage. The one action that both passengers and crew can carry out to help decrease the likelihood of crash-related injury or death is to assume an emergency brace position. Doing so has been demonstrated over several decades to improve survivability. While cabin crew are taught (and then might have to teach passengers in an emergency about the emergency brace position), passengers in many parts of the world never learn about the brace position unless they are involved in an emergency in which there is time to prepare for the landing. This lack of provision of information is related to the fact that most airlines do not provide information in the preflight safety briefing and some do not even provide the information in the passenger safety cards. Many countries do not require their airlines to do so, a fact, which in turn, is related to the lack of mention of the brace position in ICAO's Annex 6. Until standards and recommended practices are changed at the highest world level, passengers will continue to be deprived of this vital, life-saving information that they can use, potentially to help save their own lives.

• Animal Bioscience
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• v.34 no.7
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• pp.1146-1156
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• 2021

Objective: The aim of the study was to compare the effect of two plant additives, rich in polyphenolic compounds, supplemented to sheep diets on microorganisms and carbohydrate fermentation in rumen. Methods: In the experiment, 6 ewes of the Polish Mountain breed were fitted with ruminal cannulas. Sheep were divided into three feeding groups. The study was performed in a cross-over design of two animals in each group, with three experimental periods (n = 6 per each group). The animals were fed a control diet (CON) or additionally received 3 g of dry and milled lingonberry leaves (VVI) or oak bark (QUE). Additionally, plant material was analyzed for tannins concentration. Results: Regardless of sampling time, QUE diet increased the number of total protozoa, as well as Entodinium spp., Diplodinium spp. and Isotrichidae family, while decreased bacterial mass. In turn, a reduced number of Diplodinium spp. and increased Ophryoscolex spp. population were noted in VVI fed sheep. During whole sampling time (0, 2, 4, and 8 h), the number of protozoa in ruminal fluid of QUE sheep was gradually reduced as opposed to animals receiving CON and VVI diet, where rapid shifts in the protozoa number were observed. Moreover, supplementing sheep with QUE diet increased molar proportions of butyrate and isoacids in ruminal fluid. Unfortunately, none of the tested additives affected gas production. Conclusion: The addition of VVI or QUE in a small dose to sheep diets differently affected rumen microorganisms and fermentation parameters, probably because of various contribution of catechins in tested plant materials. However, it is stated that QUE diet seems to create more favorable conditions for growth and development of ciliates. Nonetheless, the results of the present study showed that VVI and QUE additives could serve as potential natural modulators of microorganism populations and, consequently, carbohydrate digestion in ruminants.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.3
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• pp.424-435
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• 2022

Aesthetic impression, structural stability, functionality during exercise, freedom of movement and physiological suitability should be considered when developing aquarobic wear for middle-aged women. We conducted a visual evaluation of the commercial aquarobic wear of the five brands (ARENA, TURN, RALLY, RENOMA and ELLE) with the highest market share and selected three items: one-piece type (OPT); two-piece type (TPT) and whole-body type (WBT). These are the most worn types of aquarobic wear for each brand. The G4 showed the best results in appearance evaluation of the shoulder strap width, front neckline, armhole line, side hip line and short pants length in the OPT category. The TPT had better ratings as the shoulder strap was located in the center of the shoulder, and the front and the back necklines were not too deep. The five items of WBT clothing received similar ratings for each element, so it is considered that the advantages are evenly distributed. No single design was a clear favorite. Based on the above results, continuous research on the development of aquarobic wear that is more suitable and preferable for middle-aged women should be conducted.

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1147-1161
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• 2023

The Internet of Things (IoT) requires a new processing model that will allow scalability in cloud computing while reducing time delay caused by data transmission within a network. Such a model can be achieved by using resources that are closer to the user, i.e., by relying on edge computing (EC). The amount of IoT data also grows with an increase in the number of IoT devices. However, building such a flexible model within a heterogeneous environment is difficult in terms of resources. Moreover, the increasing demand for IoT services necessitates shortening time delay and response time by achieving effective load balancing. IoT devices are expected to generate huge amounts of data within a short amount of time. They will be dynamically deployed, and IoT services will be provided to EC devices or cloud servers to minimize resource costs while meeting the latency and quality of service (QoS) constraints of IoT applications when IoT devices are at the endpoint. EC is an emerging solution to the data processing problem in IoT. In this study, we improve the load balancing process and distribute resources fairly to tasks, which, in turn, will improve QoS in cloud and reduce processing time, and consequently, response time.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.1
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• pp.112-117
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• 2024

The Internet of Things (IoT) device is a key component for Industry 4.0, which is the network in homes, factories, buildings, and infrastructures to monitor and control the systems. To demonstrate the IoT network, batteries are widely utilized as power sources, and the batteries inevitably require repeated replacement due to their limited capacity. Magneto-mechano-electric (MME) generators are one of the candidate to develop self-powered IoT systems since MME generators can harvest electricity from stray alternating current (AC) magnetic fields arising from electric power cables. Herein, we report a magneto-mechano-triboelectric generator enabled by a ferromagnetic-ferroelectric composite. In the triboelectric nylon matrix, a ferromagnetic carbonyl iron powder (CIP) was introduced to induce magnetic force near the AC magnetic field for MME harvesting. Additionally, a ferroelectric ceramic powder was also added to the MME composite material to enhance the charge-trapping capability during triboelectric harvesting. The final ferromagnetic-ferroelectric composite-based MME triboelectric harvester can generate an open-circuit voltage and a short-circuit current of 110 V and 8 μA, respectively, which were enough to turn on a light emitting diode (LED) and charge a capacitor. These results verify the feasibility of the MME triboelectric generator for not only harvesting electricity from an AC magnetic field but also for various self-powered IoT applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.