• Journal of Communications and Networks
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• v.14 no.2
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• pp.162-168
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• 2012

This paper considers the scenario in which a set of nodes share a common channel. Some nodes have a rechargeable battery and the others are plugged to a reliable power supply and, thus, have no energy limitations. We consider two source-destination pairs and apply the concept of cognitive radio communication in sharing the common channel. Specifically, we give high-priority to the energy-constrained source-destination pair, i.e., primary pair, and low-priority to the pair which is free from such constraint, i.e., secondary pair. In contrast to the traditional notion of cognitive radio, in which the secondary transmitter is required to relinquish the channel as soon as the primary is detected, the secondary transmitter not only utilizes the idle slots of primary pair but also transmits along with the primary transmitter with probability p. This is possible because we consider the general multi-packet reception model. Given the requirement on the primary pair's throughput, the probability p is chosen to maximize the secondary pair's throughput. To this end, we obtain two-dimensional maximum stable throughput region which describes the theoretical limit on rates that we can push into the network while maintaining the queues in the network to be stable. The result is obtained for both cases in which the capacity of the battery at the primary node is infinite and also finite.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.9
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• pp.723-734
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• 2018

A structural weight estimation methodology for the multicopter design process is presented. In general, a multicopter is composed of an airframe, motors, propellers, battery and so on. Among these, the weight of motors, propellers and battery can be obtained from the weight trends with respect to design parameters. However, the structural weight is hard to be estimated due to the various configurations and design concepts of multicopters. Moreover, the airframe weights of most commercial multicopter products are not provided. Thus, an accurate airframe weight model is required for the reliable mutlcopter design process. Firstly, the standard configuration of multicopters is defined. Then, we proposed the structural weight estimation method using the number and diameter of propellers determined from the initial step of sizing process. Finally, we validated our suggested method using the commerical products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.920-926
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• 2011

In this paper, a solar power emotional LED lightening street lamps with multi control sun tracker is presented. The proposed system has a multiple control sun tracking function and high quality emotional LED lamps. The system is designed to absorb maximum sun lights by temperature sensor and humidity sensor of control circuits. A battery charge-discharge controller is developed for high efficient usage of battery charger for utilization of new and renewal energy. An interface circuit for remote monitoring and controlling is included in the developed system. The proposed multi tracking solar power emotional LED street lamps is better than conventional systems in aspect of tracking operation and energy efficiency, and expected to be a leading model for next generation solar power street lamp system, because it is a new technology combining sun tracking solar power system and emotional lightening system.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.145-154
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• 2022

The authors are making a prototype flexible board of a radio-frequency transmitter for measuring an electromyogram (EMG) of a flying moth and plan to apply for an experimental station license from the Ministry of Internal Affairs and Communications of Japan in the summer of 2022. The goal is to create a continuous low-dose exposure standard that incorporates scientific and physiological functional assessments to replace the current standard based on lethal dose 50. This paper describes the technical evaluation of the hardware. The signal of a bipolar EMG electrode is amplified by an operational amplifier. This potential is added to a voltage-controlled crystal oscillator (27 MHz, bandwidth: 4 kHz), frequency-converted, and transmitted from an antenna about 10 cm long (diameter: 0.03 mm). The power source is a 1.55-V wristwatch battery that has a total weight of about 0.3 g (one dry battery and analog circuit) and an expected operating time of 20 minutes. The output power is -7 dBm and the effective isotropic radiated power is -40 dBm. The signal is received by a dual-whip antenna (2.15 dBi) at a distance of about 100 m from the moth. The link margin of the communication circuit is above 30 dB within 100 m. The concepts of this hardware and the measurement data are presented in this paper. This will be the first biological data transmission from a moth with an official license. In future, this telemetry system will improve the detection of physiological abnormalities of moths.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.1
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• pp.19-33
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• 2023

Objectives: The purpose of this study is to systematically identify situations where exposure levels are expected to be high by structuring domestic lead measurement data according to exposure processes and activities. Methods: Occupational exposure data on lead was collected from the results of the Evaluation of Reliability of Working Environment Measurement conducted by the government from 2019 to 2020. Lead exposure characteristics were analyzed by PROC (process category) and activity. The Risk Characterization Ratios (RCRs) of five PROCs according to ventilation type and lead content were evaluated using the MEASE (Metal's EASE) model. Results: The exposure data on lead (n=250) was classified into 12 PROCs and 12 activities, with an average concentration of 0.040 mg/m³ and about 14% exceeding the occupational exposure limit of 0.05 mg/m³. Processes with high exposure levels were PROC 7 (industrial spraying), 23 (open processing and transfer operations of molten metal), 24 (mechanical treatment), 25 (welding), and 26 (handling of powder containing lead). The results of evaluating RCR for the five PROCs were greater than 1 or close to 1 even if local exhaust ventilation was used. Conclusions: There is a possibility that the concentration of exposure is high in the casting and tapping of molten metal containing lead, mechanical treatment such as fracturing and abrasion, handling of powder, spraying, battery manufacturing, and waste battery recycling processes. It is necessary to implement chemical management policies for workplaces with such processes.

• Journal of Animal Science and Technology
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• v.51 no.5
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• pp.421-426
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• 2009

This study was conducted to clarify the characteristics of vocalization of laying hen related with space in battery cage. The size of cages were classified into control (0.30 m ${\times}$ 0.14 m ${\times}$ 0.55 m, length ${\times}$ width ${\times}$ height), small (0.21 m ${\times}$ 0.14 m ${\times}$ 0.55 m) and large (0.30 m ${\times}$ 0.30 m ${\times}$ 0.55 m) size. Vocalization of 16 individuals of laying hen in each group of Hy-Line Brown (80 week old) were recorded 3 hours per day (10:00am~11:00am, 3:00pm~4:00pm and 7:00pm~8:00pm) using digital recorder and microphone during October 2008 and February 2009. Characteristics of frequency, intensity and duration of vocalization were analyzed by GLM (general linear model) and Duncan's multi-test. There were differences in basic and maximum frequency, and intensity based on analysis of spectrogram and spectrum among different cage sizes. Vocalization of laying hen would be one of the indicators to understand the stress caused by rearing space in batter cage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.587-593
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• 2021

Since 2017, a total of 29 fire accidents have occurred in energy storage systems (ESSs) as of June 2020. The common mode voltage (CMV) is one of the electrical hazards that is assumed to be a cause of those fire accidents. Several cases of CMV that violate the allowable insulation level of a battery section are being reported in actual ESS operation sites with △-Y winding connections. Thus, this paper evaluates the characteristics of CMV. An ESS site was modeled with an AC grid, PCS, and battery sections using PSCAD/EMTDC software. As a result of a simulation based on the proposed model, it was confirmed that characteristics of CMV vary significantly and are similar to actual measurements, depending on the grounding method of the internal transformer for PCS. The insulation level of the battery section may be severely degraded as the value of CMV exceeds the rated voltage in case of a grounding connection. It was found that the value of CMV dramatically declines when the internal transformer for PCS is operated as non-grounding connection, so it meets the standard insulation level.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.1
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• pp.16-22
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• 2024

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.