Explore the vast range of titles available.

Leading countries are developing clean energy to replace fossil fuels. In this context, Russia is changing its energy policy towards fostering new energy resources, such as hydrogen and helium. Hydrogen will not only contribute to Russia’s financial revenue by replacing natural gas, but will also provide a basis for it to maintain its dominance over the international energy market by pioneering new energy markets. Russia is aiming to produce more than two million tons of hydrogen fuel for export to Europe and Asia by 2035. However, it is facing many challenges, including developing hydrogen fuel storage systems, acquiring the technology required for exporting hydrogen, and building trust in the fuel market. Meanwhile, South Korea has a foundation for developing a hydrogen industry, as it has the highest capacity in the world to produce fuel cells and the ability to manufacture LNG: (liquefied natural gas) carriers. Therefore, South Korea and Russia have sufficient potential to create a new complementary and reciprocal cooperation model in the hydrogen fuel field. This study examines the present and future of Russia’s energy policy in this area as well as discusses South Korea and Russia’s cooperation plans in the hydrogen fuel sector and the related implications.

Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

Passive tracking aims to track targets without assistant devices, that is, device-free targets. Passive tracking based on Radio Frequency (RF) Tomography in wireless sensor networks has recently been addressed as an emerging field. The passive tracking scheme using geometric theorems (GTs) is one of the most popular RF Tomography schemes, because the GT-based method can effectively mitigate the demand for a high density of wireless nodes. In the GT-based tracking scheme, the tracking scenario is considered as a two-dimensional geometric topology and then geometric theorems are applied to estimate crossing points (CPs) of the device-free target on line-of-sight links (LOSLs), which reveal the target’s trajectory information in a discrete form. In this paper, we review existing GT-based tracking schemes, and then propose a novel passive tracking scheme by exploiting the Intercept Theorem (IT). To create an IT-based CP estimation scheme available in the noisy non-parallel LOSL situation, we develop the equal-ratio traverse (ERT) method. Finally, we analyze properties of three GT-based tracking algorithms and the performance of these schemes is evaluated experimentally under various trajectories, node densities, and noisy topologies. Analysis of experimental results shows that tracking schemes exploiting geometric theorems can achieve remarkable positioning accuracy even under rather a low density of wireless nodes. Moreover, the proposed IT scheme can provide generally finer tracking accuracy under even lower node density and noisier topologies, in comparison to other schemes.

Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.

Large-scale (LS) multi-user (MU) multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is considered to be a desirable signal transmission scheme because it can significantly improve the energy efficiency (EE) and spectral efficiency (SE) of the system. However, there are many difficulties in realizing an LS-MU-MIMO-OFDM system, and one of these is its high peak-to-average power ratio (PAPR), which results in serious nonlinear signal distortion and power inefficiency of the power amplifier (PA). LS-MIMO-OFDM systems require a lot of PAs, which are necessary to be connected to each antenna. To compensate for the PA nonlinearity and increase the efficiency, a digital pre-distorter (DPD) is very popular and has been successfully implemented in current base stations (BSs). However, a DPD is very difficult to use in an LS-MU-MIMO-OFDM system because it is expensive, but should be applied to each antenna. Therefore, a considerate scheme of signal processing is necessary to cope with the PA nonliearity issue of the LS-MU-MIMO-OFDM system. In this paper, we propose an interference-aware iterative clipping and filtering peak-to-average power ratio (PAPR) reduction scheme for LS-MU-MIMO-OFDM systems. In the proposed scheme, the clipping level in the clipping process is adaptively adjusted based on any kind of interference level that exists in the general communication environment. In particular, when matched filtering (MF) precoding is used for the LS-MU-MIMO-OFDM, the inter-user interference (IUI) always exists with a practical number of transmitter (TX) antennas, and this inevitable IUI level can be a decision point for the clipping ratio (CR). Choosing a proper CR to make the clipping noise lower than IUI has a very high benefit for the EE improvement of the system. The results of numerical analysis show that the proposed scheme can induce a very effective peak-to-average power ratio (PAPR) performance with little SE loss.

In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m.

In recent years, many peak-to-average power ratio (PAPR) reduction techniques have been proposed for orthogonal frequency division multiplexing (OFDM) signals. Among various techniques, the iterative clipping and filtering (ICAF) technique has been considered as a practical scheme, and widely used owing to its non-expansion of bandwidth, low computational complexity, and simplicity in implementation without receiver-side cooperation. However, the performance of conventional ICAF technique is degraded, because the same signals are iteratively clipped with a fixed clipping threshold (CT) in every clipping operation. In this paper, we analyze the performance of conventional ICAF technique, and then propose an adaptive ICAF scheme, which clips the signal with an adaptively modified CT in every clipping operation to achieve enhanced PAPR reduction of OFDM signals. Simulation results show that the proposed scheme significantly outperforms the conventional scheme, in PAPR reduction of OFDM signals at the same number of iterations.

Due to the large measurement error in the practical non-cooperative scene, the passive localization algorithms based on traditional numerical calculation using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) often have no solution, i.e., the estimated result cannot meet the localization background knowledge. In this context, this paper intends to introduce interval analysis theory into joint FDOA/TDOA-based localization algorithm. The proposed algorithm uses the dichotomy algorithm to fuse the interval measurement of TDOA and FDOA for estimating the velocity and position of a moving target. The estimation results are given in the form of an interval. The estimated interval must contain the true values of the position and velocity of the radiation target, and the size of the interval reflects the confidence of the estimation. The point estimation of the position and the velocity of the target is given by the midpoint of the estimation interval. Simulation analysis shows the efficacy of the algorithm.

Due to the continuous increase in data demanded by end-users, an energy-efficient base station (BS) is a vital topic of interest that would not only result in a substantial economic impact on service providers, but would also reduce the carbon footprint of operating a network. In this regard, we propose the structure and systematic operation of a BS with a large-scale (LS) antenna system that can increase the energy efficiency (EE) of cellular systems. The proposed BS structure includes various power-related units, such as a central management apparatus, power controller, EE calculator, radio site-dependent parameter space (RSD-PS) and determiner. With the information provided from each unit, the decision unit determines how to adjust each component of the BS in order to maximize the EE. Extensive simulations show that the proposed BS improves the EE performance by about 83.05% relative to the reference BS.