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Introduction of a hydrophobic crystalline carbon support enhances the performance of AEMWE and improves the corrosion resistance of carbon by reducing its interaction with water. This demonstrates the promising potential of utilizing a carbon support.

In recent years, big data has been widely used to understand consumers’ behavior and opinions. With this paper, we consider the use of big data and its effects in the problem of projecting the number of reverse mortgage subscribers in Korea. We analyzed web-news, blog post, and search traffic volumes associated with Korean reverse mortgages and integrated them into a Generalized Bass Model (GBM) as a part of the exogenous variables representing marketing effort. We particularly consider web-news volume as a proxy for marketer-generated content (MGC) and blog post and search traffic volumes as proxies for user-generated content (UGC). Empirical analysis provides some interesting findings: First, the GBM by incorporating big data is helpful for forecasting the sales of Korean reverse mortgages, and second, the UGC as an exogenous variable is more useful for predicting sales volume than the MGC. The UGC can explain consumers’ interest relatively well. Additional sensitivity analysis supports that the UGC is important for increasing sales volume. Finally, prediction performance is different between blog posts and search traffic volumes.

AbstractRepublic of Korea is the seventh largest CO2 emission country in 2010 and the third fastest country in the growth of CO2 emission according to the European Commission's Joint Research Center. To mitigate the effect of CO2 on the climate change and global warming, Korea should reduce the anthropogenic CO2 emissions from sources such as the power plants and iron works. So carbon dioxide capture and storage (CCS) technology is regarded as one of the most promising reduction options. This study established the CO2 transport strategies from the sources to sinks (such as the saline aquifers and gas fields in the Southeast Sea of the Korean Peninsula) for the offshore CCS in Korea. Also the cost estimations were carried out with the CO2 transport strategies. The CO2 transport methods suggested in this study were pipelines for both onshore and offshore, and a complex concept consisting of a pipeline for the source to coast (including the liquefaction facility on a barge) and a CO2 carrier for the coast to sink (including the temporary storage near offshore sink). With respect to the onshore pipelines, it was desirable to construct the CO2 transport pipelines along existing roads and/or LNG (liquefied natural gas) pipelines, as already realized in the United Kingdom (UK) and the Australia CO2 transport chains because of the cost and environmental aspects. The CO2 carrier was considered for the offshore CCS demonstration stage starting in 2016 to meet the timeline set by the Korea National CCS Master Plan. To optimize the CO2 transport systems, the advantages and drawbacks for the CO2 transport using the pipeline and shipping were analysed and the costs for them were also estimated with the CO2 transport strategies. There were several factors to be considered before constructing the CO2 pipelines including the amount of CO2, the terrain, the diameter of pipe, the transport pressure, the CO2 quality, the transport temperature, the CO2 state (i.e. gas, liquid or supercritical phases), etc. Also for the CO2 shipping it should be considered such as the amount of CO2, the shape and capacity of CO2 cargo tanks, the ship capacity, the liquefaction pressure and temperature, the type of the temporary storage, etc. Although the present study is now on-going to optimize the CO2 transport infrastructure for the offshore CCS in Korea, the preliminary results show the CO2 transport cost for the pipeline system is lower than that for the shipping in the present status.

$Q$ -learning-based operation strategies are being recently applied for optimal operation of energy storage systems, where, a $Q$ -table is used to store $Q$ -values for all possible state-action pairs. However, $Q$ -learning faces challenges when it comes to large state space problems, i.e., continuous state space problems or problems with environment uncertainties. In order to address the limitations of $Q$ -learning, this paper proposes a distributed operation strategy using double deep $Q$ -learning method. It is applied to managing the operation of a community battery energy storage system (CBESS) in a microgrid system. In contrast to $Q$ -learning, the proposed operation strategy is capable of dealing with uncertainties in the system in both grid-connected and islanded modes. This is due to the utilization of a deep neural network as a function approximator to estimate the $Q$ -values. Moreover, the proposed method can mitigate the overestimation that is the major drawback of the standard deep $Q$ -learning. The proposed method trains the model faster by decoupling the selection and evaluation processes. Finally, the performance of the proposed double deep $Q$ -learning-based operation method is evaluated by comparing its results with a centralized approach-based operation.

The earth provides a vast resource of groundwater from aquifers a few meters beneath the surface. Thus, buildings that use underground space must be equipped with dewatering wells to drain the permeated groundwater to the sewage pipelines to ensure the structural stability of the building. Although the inflowing groundwater temperatures and flow rates are stable enough for groundwater to be used as an energy source, 79% of the permeated groundwater is discarded through the sewers, generating significant sewerage expenses. This study introduced a novel heat exchanger module to utilize the permeated groundwater as an unused energy source using heat pumps, and the performance of the system was evaluated by TRNSYS simulations. First, the sizing of the unit heat exchanger module was proposed according to the mean inflow rate of the permeated groundwater. Second, the heat pump system was configured using multiple modules in the source-side loop. Finally, the performance of the proposed heat pump system was compared with that of a conventional air source heat pump using realistic load and temperature profiles. This preliminary study demonstrated interesting performance results, with a coefficient of performance for heating that was higher than that of a conventional heat pump system by 0.79. The results show the potential utilization of the systems for a construction project requiring large-scale underground spaces, where abundant groundwater is available.

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

Smart grids have attracted increasing interest in the field of industrial research. These systems allow electricity consumers to communicate with electrical systems using bidirectional communications. However, these bidirectional channels may introduce privacy threats to consumers. Considerable research has been conducted with the aim of resolving these threats. Unfortunately, a practical smart grid design with adequate efficiency and security functionalities has not yet been produced. In this paper, we propose lightweight privacy preserving metering protocols by designing a distributed authentication method to further increase the speed of the message authentication process. We confirmed the efficiency and security of our method by performing a detailed analysis.