The Monitoring and Management of an Operating Environment to Enhance the Safety of a Container-Type Energy Storage System.

The implementation of an energy storage system (ESS) as a container-type package is common due to its ease of installation, management, and safety. The control of the operating environment of an ESS mainly considers the temperature rise due to the heat generated through the battery operation. However, the relative humidity of the container often increases by over 75% in many cases because of the operation of the air conditioner which pursues temperature-first control. Humidity is a major factor which can cause safety issues such as fires owing to insulation breakdown caused by condensation. However, the importance of humidity control in ESS is underestimated compared to temperature control. In this study, temperature and humidity monitoring and management issues were addressed for a container-type ESS by building sensor-based monitoring and control systems. Furthermore, a rule-based air conditioner control algorithm was proposed for temperature and humidity management. A case study was conducted to compare the conventional and proposed control algorithms and verify the feasibility of the proposed algorithm. The results showed that the proposed algorithm reduced the average humidity by 11.4% compared to the value achieved with the existing temperature control method while also maintaining the temperature.

Occupancy-Based Energy Consumption Estimation Improvement through Deep Learning.

The energy consumed in buildings constitutes more than half of the total electricity consumption and is highly correlated with the number of occupants; therefore, it is necessary to use occupancy information in energy consumption analysis. However, the number of occupants may not be accurate owing to measurement errors caused by various factors, such as the locations of sensors or cameras and the communication environment. In this study, occupancy was measured using an object recognition camera, the number of people was additionally collected by manual aggregation, measurement error in occupancy count was analyzed, and the true count was estimated using a deep learning model. The energy consumption based on occupancy was predicted using the measured and estimated values. To this end, deep learning was used to predict energy consumption after analyzing the correlation between occupancy and energy consumption. Results showed that the performance of occupancy estimation was 1.9 based on RMSE, which is a 71.1% improvement compared to the original occupancy sensing. The RMSE of predicted energy consumption based on the estimated occupancy was 56.0, which is a 5.2% improvement compared to the original energy estimation.

Survey of Zoonotic Trematode Metacercariae in Fish from Irrigation Canal of Togyo-jeosuji (Reservoir) in Cheorwon-gun, Gangwon-do, Republic of Korea.

The infection status of zoonotic trematode metacercariae (ZTM) was investigated in total 568 freshwater fishes (19 species) from the irrigation canal of Togyo-jeosuji (Reservoir) in Cheorwon-gun, Gangwon-do, the Republic of Korea for 3 years (2018-2020). All fishes were examined using the artificial digestion method. The metacercariae of Clonorchis sinensis (CsMc) were detected in 180 (43.8%) out of 411 fish of positive species, and their infection intensity was 38 per fish infected (PFI). Especially, in 2 fish species, i.e., Pseudorasbora parva and Puntungia herzi, the prevalence was 82.1% and 31.3%, and the infection intensity with CsMc was 88 and 290 PFI, respectively. Metagonimus spp. metacercariae (MsMc) were found in 403 (74.1%) out of 544 fish of positive species, and their infection intensity was 62 PFI. In the pale chub, Zacco platypus, the prevalence of MsMc was 98.6%, and their infection intensity was 144 PFI. Centrocestus armatus metacercariae were detected in 171 (38.9%) out of 440 fish of positive species, and their infection intensity was 1,844 PFI. Echinostoma spp. metacercariae were found in 94 (19.6%) out of 479 fish of positive species, and their infection intensity was 3 PFI. Metorchis orientalis metacercariae were detected in 43 (29.3%) out of 147 fish of positive species, and their infection intensity was 4 PFI. By the present study, it has been confirmed that some species of ZTM, including CsMc and MsMc, are prevalent in fishes from the irrigation canal of Togyo-jeosuji in Cheorwon-gun, Gangwon-do, Korea.

Effect of organic buffer layer in the electrical properties of amorphous-indium gallium zinc oxide thin film transistor.

In this research, we reported on the fabrication of top-contact amorphous-indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) with an organic buffer layer between inorganic gate dielectric and active layer in order to improve the electrical properties of devices. By inserting an organic buffer layer, it was possible to make an affirmation of the improvements in the electrical characteristics of a-IGZO TFTs such as subthreshold slope (SS), on/off current ratio (I(ON/OFF)), off-state current, and saturation field-effect mobility (muFE). The a-IGZO TFTs with the cross-linked polyvinyl alcohol (c-PVA) buffer layer exhibited the pronounced improvements of the muFE (17.4 cm2/Vs), SS (0.9 V/decade), and I(ON/OFF) (8.9 x 10(6)).