In this study, the adsorption/desorption performance of toluene was evaluated using zeolite adsorbent to replace activated carbon with one-off and ignition characteristics. For the proper operation of the VOCs adsorption/desorption and condensate recovery steps, the operating range by various adsorption/desorption temperatures was selected. The adsorbent is a bead-type zeolite, which was put into an adsorption tower of 10 LPM scale. As a result, it was demonstrated that 0.079 mg/g was adsorbed at a low temperature (20°C) during adsorption. In the case of desorption, it was found that VOCs adsorbed on the adsorbent were completely recovered after the desorption operation at 220°C for about 160 minutes. However, in the heating rate step for desorption, it was not possible to maintain an appropriate heating rate by filling the tower with zeolite. This was complemented by applying a copper plate with high thermal conductivity, and it was shown that the time was shortened by about 10 minutes or more. When VOCs are emitted at high concentrations during the desorption process, they can be reused as energy resources through low-temperature maintenance, and a condensation method was attempted. The efficiency of condensing chiller (cooler) with temperature control and liquid nitrogen condensing was compared. It was found that the chiller condensing efficiency increased as the temperature decreased. In the case of liquid nitrogen condensation, the liquid nitrogen temperature was maintained at -196°C, showing a stable efficiency of 90%.

In this paper, considering the limits of existing smart pots, we introduce the research result of the new smart pot that can easily grow various pet plants. Through Arduino control kit which is the one of the open source computing platform, electric circuit and equipment to fulfil the functions of the smart pot is newly designed and made. Especially the head of pump which is main function in smart pot and the structure of electric equipment is theoretically calculated and Finite Element Analysis (FEA) tool is used for static analysis to prevent damage to the pots. After that, new smart pot is made with the researched design materials and perform the various functional investigations to grow the indoor plants.

Total of 325 estuaries in Korea were surveyed to analyze the effect of presence of sluice gate on the estuary environment and fish community from 2016 to 2018. Fish community in closed and open estuaries showed differences generally, and the relative abundance (RA) of primary freshwater species in the closed and migratory species in the open estuaries were high. The result of classifying species by habitat characteristics in closed and open estuaries showed similar tendencies at the estuaries of south sea and west sea. The relative abundances of primary freshwater species in the closed estuaries at the estuaries of south sea and west sea were the highest, but estuarine and migratory species were high in both closed and open estuaries at the estuaries of east sea. Primary freshwater species showed higher abundances in the closed estuaries with reduced salinity due to blocking of seawater since they are not resistant to salt. However, primary freshwater species in open estuaries at east sea was higher than that of the closed estuaries, which is considered to be the result of reflecting the characteristics (tide, sand bar, etc.) of the east sea. Korea Estuary Fish Assessment Index (KEFAI) was showed to be higher at open estuaries than closed in all sea areas (T-test, P<0.001), the highest KEFAI was observed in closed estuaries at south sea, and open estuaries in east sea. Fish community of closed and open estuaries in each sea areas showed statistically significant differences (PERMANOVA, East, Pseudo-F=3.0198, P=0.002; South, Pseudo-F=22.00, P=0.001; West, Pseudo-F=14.067, P=0.001). Fish assemblage similarity by sea areas showed a significant differences on fish community in closed and open estuaries at east sea, south sea, and west sea (SIMPER, Group dissimilarity, 85.85%, 88.36%, and 88.05%). This study provided information on the characteristics and distribution of fish community according to the types of estuaries. The results of this study can be used as a reference for establishing appropriate management plans according to the sea areas and type in the management and restoration of estuaries for future.

PURPOSES : Road surface conditions are vital to traffic safety, management, and operation. To ensure traffic operation and safety during periods of snow and ice during the winter, each local government allocates considerable resources for monitoring that rely on field-oriented manual work. Therefore, a smart monitoring and management system for autonomous snow removal that can rapidly respond to unexpected abrupt heavy snow and black ice in winter must be developed. This study addresses a smart technology for automatically monitoring and detecting road surface conditions in an experimental environment using convolutional neural networks based on a CCTV camera and infrared (IR) sensor data. METHODS : The proposed approach comprises three steps: obtaining CCTV videos and IR sensor data, processing the dataset acquired to apply deep learning based on convolutional neural networks, and training the learning model and validating it. The first step involves a large dataset comprising 12,626 images extracted from the acquired CCTV videos and the synchronized surface temperature data from the IR sensor. In the second step, image frames are extracted from the videos, and only foreground target images are extracted during preprocessing. Hence, only the area (each image measuring 500 × 500) of the asphalt road surface corresponding to the road surface is applied to construct an ideal dataset. In addition, the IR thermometer sensor data stored in the logger are used to calculate the road surface temperatures corresponding to the image acquisition time. The images are classified into three categories, i.e., normal, snow, and black-ice, to construct a training dataset. Under normal conditions, the images include dry and wet road conditions. In the final step, the learning process is conducted using the acquired dataset for deep learning and verification. The dataset contains 10,100 (80%) data points for deep learning and 2,526 (20%) points for verification. RESULTS : To evaluate the proposed approach, the loss, accuracy, and confusion matrix of the addressed model are calculated. The model loss refers to the loss caused by the estimated error of the model, where 0.0479 and 0.0401 are indicated in the learning and verification stages, respectively. Meanwhile, the accuracies are 97.82% and 98.00%, respectively. Based on various tests that involve adjusting the learning parameters, an optimized model is derived by generalizing the characteristics of the input image, and errors such as overfitting are resolved. This experiment shows that this approach can be used for snow and black-ice detections on roads. CONCLUSIONS : The approach introduced herein is feasible in road environments, such as actual tunnel entrances. It does not necessitate expensive imported equipment, as general CCTV cameras can be applied to general roads, and low-cost IR temperature sensors can be used to provide efficiency and high accuracy in road sections such as national roads and highways. It is envisaged that the developed system will be applied to in situ conditions on roads.