Search Results (15)

The proliferation of abandoned houses in rural South Korea poses significant challenges to sustainable rural development, driven by declining birth rates, aging populations, and urban migration. However, effective policy implementation is hindered by the lack of understanding of the negative externalities caused by abandoned houses. This study fills this gap by estimating the negative externalities associated with abandoned rural houses using discrete choice experiments. Surveys targeting individuals planning rural relocations and potential tourists considering rural stays were conducted to quantify the external costs. Our findings reveal that the marginal willingness to pay associated with abandoned houses is negative and decreases with an increasing number of abandoned houses nearby, both in the context of house purchases and rural stays. Extrapolating these results to the national level, we estimate the aggregate negative externalities value to be approximately 4.2 trillion KRW per year, highlighting significant negative externalities in rural areas nationwide. The implications of our analysis underscore the severity of negative externalities from abandoned houses, which may surpass the value of housing services, discourage migration, and prompt residents to leave rural communities, thus exacerbating the issue. Our study emphasizes the necessity for further research and policy interventions to address the negative externalities associated with abandoned rural houses, offering insights into the potential use of discrete choice experiments in similar contexts. Full article

In order to save the time and material costs associated with refrigeration system performance evaluations, a reduced-order model (ROM) using highly accurate numerical analysis results and some experimental values was developed. To solve the shortcomings of these traditional methods in monitoring complex systems, a simplified reduced-order system model was developed. To evaluate the performance of the refrigeration system compressor, the temperature of several points in the system where the compressor actually operates was measured, and the measured values were used as input values for ROM development. A lot of raw data to develop a highly accurate ROM were acquired from a VRF system installed in a building for one year, and in this study, specific operating conditions were selected and used as input values. In this study, the ROM development process can predict the performance of compressors used in air conditioning systems, and the research results on optimizing input data required for ROM generation were observed. The input data are arranged according to the design of experiments (DOE), and the accuracy of ROM according to data arrangement is compared through the experiment results. Full article

R134a, a vehicle refrigerant used in the vehicle heat pump system, is regulated according to the Montreal Protocol. Refrigerants such as R1234yf, R744, and R290 in vehicle heat pump systems are being investigated to identify their alternatives. Because developing a new system exclusively for new refrigerants is costly, an empirical test was conducted on the R1234yf refrigerant in a heat pump system designed for the R134a refrigerant in an actual vehicle system. The heating, cooling, and battery-cooling modes were tested for the amount of refrigerant charge, and operability tests were conducted for the compressor load; heating, ventilation, air conditioning (HVAC) air flow rate; coolant temperature; and flow rate of each mode. The optimal refrigerant charge in heating mode was 0.7 kg, and the optimal refrigerant charge in the cooling and battery-cooling modes was 0.9 kg. To yield the highest coefficient of performance of the system, the compressor load was 50%, the HVAC fan was 12 V, and the coolant flow rate was 10 LPM. The most efficient system operation was possible at a coolant temperature of 30 °C in the cooling and heating modes and at 20 °C in battery-cooling mode. Full article

In the transition zone of railway tracks, track irregularities occur frequently due to differential settlement, which arises from the difference between the vertical supporting stiffness of the abutment and the backfill. This is disadvantageous because it increases the maintenance requirements and deteriorates the ride quality. To address this challenge, this study proposes a strategy involving the application of cement-treated gravel reinforced with geogrids and rigid facing walls. The reinforced subgrade for railways (RSR), which can reduce residual settlement through the initial construction of the backfill reinforced with geogrids and the subsequent development of the rigid facing wall, was constructed at the transition zone with cement-treated gravel as the backfill material. The long-term behaviors during and after construction on the RSR for a period of 16 months were evaluated by analyzing the surface and ground settlements, horizontal earth pressure, and geogrid strain. The minor net settlement of the reinforced backfill converges at the early stage of subgrade construction, and the horizontal earth pressure was approximately reduced to the level of 54–63% of the Rankine active earth pressure. Full article

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO₃)₂) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO₃)₂ molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an $84.6\%$ balanced accuracy for the cross-batch testing task. Full article

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded. Full article

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task. Full article

Solar cells are important alternatives to fossil fuels for energy generation in today’s world, where the demand for alternative, renewable sources of energy is increasing. However, solar cells, which are installed outdoors, are susceptible to pollution by environmental factors. A solution to overcome this limitation involves coating solar cell surfaces with functional coatings. In this study, we propose a transmittance control method for a tensioned web in a roll-to-roll, transparent, water-repellent film coating. First, we analyzed the effects of process conditions on the transmittance and contact angle of the transparent water-repellent film during roll-to-roll slot-die coating. It was confirmed that the tension was the most dominant factor, followed by the coating gap. Through the tension control, the transmittance was changed by 3.27%, and the contact angle of the DI water was changed by 17.7°. In addition, it was confirmed that the transmittance was changed by 0.8% and the contact angle of DI water by 3.9° via the coating gap control. Based on these results, a transmittance prediction model was developed according to the tension and coating gap, and was then verified experimentally. Finally, a water-repellent film with a high transmittance of 89.77% was obtained using this model. Full article

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data. Full article

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches. Full article

Slot-die coatings are advantageous when used for coating large-area flexible devices; in particular, the coating width can be controlled and simultaneous multi-layer coatings can be processed. To date, the effects of ink widening and the coating gap on the coating thickness have only been considered in a few studies. To this end, we developed two mathematical models to accurately estimate the coating width and thickness that consider these two effects. We used root mean square deviation (RMSD) to experimentally verify the developed method. When the coating gap was increased, the coating width increased and the coating thickness decreased. Experimental results showed that the estimated performances of the coating width and thickness models were as high as 98.46% and 95.8%, respectively. We think that the developed models can be useful for determining the coating conditions according to the ink properties to coat a functional layer with user-defined widths and thicknesses in both lab- and industrial-scale roll-to-roll slot-die coating processes. Full article

In roll-to-roll (R2R) processing, web uniformity is a crucial factor that can guarantee high coating quality. To understand web defects due to thermal deformation, we analyzed the effect of web unevenness on the coating quality of an yttria-stabilized zirconia (YSZ) layer, a brittle electrolyte of solid oxide fuel cells (SOFCs). We used finite-element analysis to study thermal and mechanical deformations at different drying temperature levels. A YSZ layer was also coated using R2R slot-die coating to observe the effect of web unevenness on coating quality. Web unevenness was generated by thermal deformation due to conduction and convection heat from the dryer. Because of varying web unevenness with time, the YSZ layer developed cracks. At higher drying temperatures, more coating defects with larger widths were generated. Results indicated that web unevenness at the coating section led to coating defects that could damage the SOFCs and decrease yield in the R2R process. Coating defects generated by web unevenness caused by convection and conduction heat should be considered in the high-volume production of brittle electrolytes using the R2R process. Full article

Assessment of lymph node (LN) status in patients with papillary thyroid carcinoma (PTC) is often troublesome because of cervical LNs with indeterminate US (ultrasound) features. We aimed to explore whether Superb Microvascular Imaging (SMI) could be helpful for distinguishing metastasis from indeterminate LNs when combined with power Doppler US (PDUS). From 353 consecutive patients with PTC, LNs characterized as indeterminate by PDUS were evaluated by SMI to distinguish them from metastasis. Indeterminate LNs were reclassified according to the SMI, the malignancy risk of each category was assessed, and the diagnostic performance of suspicious findings on SMI was calculated. The incidence of US-indeterminate LNs was 26.9%. Eighty PDUS-indeterminate LNs (39 proven as benign, 41 proven as malignant) were reclassified into probably benign (n = 26), indeterminate (n = 20), and suspicious (n = 34) categories according to SMI, with malignancy risks of 19.2%, 20.0%, and 94.1%, respectively. After combining SMI with PDUS, 80.8% (21/26) of probably benign LNs and 94.1% (32/34) of suspicious LNs could be correctly diagnosed as benign and metastatic, respectively. The diagnostic sensitivity, specificity, and accuracy of categorizing LNs as suspicious based on SMI were 78.1%, 94.9%, and 86.3%, respectively. In conclusion, the combination of SMI with PDUS was helpful for the accurate stratification of indeterminate LNs based on US in patients with PTC. Full article

Building air-tightness has been increased to make energy efficient buildings. However, various indoor air quality issues can be caused by high building air-tightness because it allows low air and moisture transmission through building envelop. In order to solve and prevent these issues, mechanical ventilation systems can be used to control the indoor humidity level. The purpose of this paper is to evaluate the performances of the Relative Humidity (RH)-sensor based auto-controlled centralized exhaust ventilation systems to manage indoor air quality and thermal comfort of multi-family residential buildings in South Korea. A series of field tests were performed for different target zones and for various moisture source scenarios. As a result, it was found that the auto-controlled centralized exhaust ventilation systems were able to control indoor air quality and to maintain the zones thermal comfort faster than the baseline cases that did not operate exhaust vents. The results presented in this paper can show the potential and the feasibility of the auto-controlled centralized exhaust ventilation systems for multi-family residential buildings in South Korea. It is expected that the results presented in this paper would be useful for building owners, engineers, and architects when designing building systems. Full article

Microfluidic technologies have shown powerful abilities for reducing cost, time, and labor, and at the same time, for increasing accuracy, throughput, and performance in the analysis of biological and biochemical samples compared with the conventional, macroscale instruments. Synthetic biology is an emerging field of biology and has drawn much attraction due to its potential to create novel, functional biological parts and systems for special purposes. Since it is believed that the development of synthetic biology can be accelerated through the use of microfluidic technology, in this review work we focus our discussion on the latest microfluidic technologies that can provide unprecedented means in synthetic biology for dynamic profiling of gene expression/regulation with high resolution, highly sensitive on-chip and off-chip detection of metabolites, and whole-cell analysis. Full article