• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3152-3171
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• 2022

This work investigates the physical layer secrecy of a multi-antenna hybrid satellite-terrestrial relay networks (HSTRN) with jamming, in which a satellite aims to make communication with a destination user by means of a relay, along with spatially random eavesdroppers. In order to weaken the signals of eavesdroppers, the conventional relay can also generate intentional interference, besides forwarding the received signal. Shadowed-Rician fading is adopted in satellite link, while Rayleigh fading is adopted in terrestrial link, eavesdropper link and jamming link. The analytical and asymptotic formulas for the system secrecy outage probability (SOP) are characterized. Practical insights on the diversity order of the network are revealed according to the asymptotic behavior of SOP at high signal-to-noise ratio (SNR) regime. Then, analysis of the system throughput is examined to assess the secrecy performance. In the end, numerical simulation results are presented to validate the theoretical analysis and point out: (1) The secrecy performance of the considered network is affected by the channel fading scenario, the system configuration; (2) Decrease of the relay coverage airspace can provide better SOP performance; (3) Jamming from the relay can improve secrecy performance without additional network resources.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

• Smart Structures and Systems
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• v.29 no.3
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• pp.395-420
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• 2022

A new hybrid quasi-3D and 2D high-order shear deformation theory is studied in this mathematical formulation, for an investigation of the bending, free vibrations and buckling influences on a functionally graded material plate. The theoretical formulation has been begun by a displacement field of five unknowns, governing the transverse displacement across the thickness of the plate by bending, shearing and stretching. The transverse shear deformation effect has been taken into consideration, satisfying the stress-free boundary conditions, especially on plate free surfaces as parabolic variation through its thickness. Thus, the mechanical properties of the functionally graded plate vary across the plate thickness, following three distributions forms: the power law, exponential form and the Mori-Tanaka scheme. The mechanical properties are used to develop the equations of motion, obtained from the Hamilton principle, and solved by applying the Navier-type solution for simply supported boundary conditions. The results obtained are compared with other solutions of 2D, 3D and quasi-3D plate theories have been found in the literature.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Journal of ILASS-Korea
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• v.28 no.3
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• pp.150-155
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• 2023

Cold-start emissions are given great importance under the Euro-7 emission standard due to their significant impact on overall vehicle emissions. When an engine is started from a cold state, the combustion process is not yet optimized, leading to higher emissions. Hybrid vehicles, in particular, may face additional challenges, as their engine may remain inactive for extended periods, causing their catalysts to cool down and potentially become less effective in reducing emissions. In the present study, the performance of an electric heater was investigated as a means to enhance the catalyst heating during the start-up time. A simulation tool was utilized to develop a model for the gasoline exhaust aftertreatment system. The result indicates that the heater was able to increase the three-way catalyst temperature to 500℃ in 4 s using 20 kW power. In addition, the implementation of a secondary air supply resulted in reduced temperature overshoot and improved conversion efficiencies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.53-58
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• 2023

As smartphones increasingly support the execution of various applications, the function of virtual memory swapping is becoming important. However, unlike traditional computer systems, mobile platforms do not basically support swapping. This is because swapping results in frequent writes to flash memory, which may degrade the performance of smartphone's storage significantly. To cope with this situation, this paper suggests two multi-swap architectures, hierarchical swapping and hybrid swapping, and compares their performance quantitatively. Specifically, this paper shows that hybrid swapping with the consideration of single-access data can reduce swapping traffic to flash memory, and improve the performance compared to traditional swapping.

• ETRI Journal
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• v.45 no.3
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• pp.519-533
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• 2023

This paper implements a simultaneous solar and thermal energy harvesting system, as a hybrid energy harvesting (HEH) system, to convert ambient light into electrical energy through photovoltaic (PV) cells and heat absorbed in the body of PV cells. Indeed, a solar panel equipped with serially connected thermoelectric generators not only converts the incoming light into electricity but also takes advantage of heat emanating from the light. In a conventional HEH system, the diode block is used to provide the path for the input source with the highest value. In this scheme, at each time, only one source can be handled to generate its output, while other sources are blocked. To handle this challenge of combining resources in HEH systems, this paper proposes a method for collecting all incoming energies and conveying its summation to the load via the current mirror cells in an approach similar to the maximum power point tracking. This technique is implemented using off-the-shelf components. The measurement results show that the proposed method is a realistic approach for supplying electrical energy to wireless sensor nodes and low-power electronics.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.191-197
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• 2023

LPG direct injection (LPDi) technology is a method of improving the weaknesses of existing LPG vehicles by directly injection into the combustion chamber. This study was conducted on the comparison of emissions and fuel efficiency performance of the engine and vehicle by applying LPDi technology. The LPDi hybrid engine's maximum output and maximum torque were measured at an equivalent level of less than 1% compared to conventional gasoline fuel. The fuel amount was corrected using the LCU controller, and the THC, CO, and NOx emissions were reduced to 90% in the operating range of the three-way catalyst through air-fuel ratio control. The analysis of THC+NOx and CO emissions in FTP-75 (CVS-75) driving mode satisfied the US LEV III SULEV30 regulation.