ETRI Journal最新文献

This paper presents the basic quantum reinforcement learning theory and its applications to various engineering problems. With the advances in quantum computing and deep learning technologies, various research works have focused on quantum deep learning and quantum machine learning. In this paper, quantum neural network (QNN)-based reinforcement learning (RL) models are discussed and introduced. Moreover, the pros of the QNN-based RL algorithms and models, such as fast training, high scalability, and efficient learning parameter utilization, are presented along with various research results. In addition, one of the well-known multi-agent extensions of QNN-based RL models, the quantum centralized-critic and multiple-actor network, is also discussed and its applications to multi-agent cooperation and coordination are introduced. Finally, the applications and future research directions are introduced and discussed in terms of federated learning, split learning, autonomous control, and quantum deep learning software testing.

In this paper, a novel dual-band wideband bandpass filter (BPF) based on transversal signal-interaction concepts with a wide upper stopband is proposed and investigated. The designed specification of two passbands can be managed and satisfied based on the independent controllable fractional bandwidth of the two passbands and the centered frequencies. The centered frequencies of dual-band BPF are, respectively, 0.79 GHz (ƒ₁) and 1.24 GHz (ƒ₂) with 3 dB fraction bandwidths of 26.54% and 11.3%. Two transmission paths consisting of coupled stub-loaded square ring resonators and anti-coupled shorted lines are used to realize signal cancellation of multiple transmission path signal transmission from Port 1 to Port 2. Eleven transmission zeros (TZs) modify harmonic suppression up to 10 ƒ₁ with stopband rejection higher than 15 dB. Butterworth lumped notch network and step impedance resonator (SIR) are also utilized to improve the selectivity and harmonic suppression. A compact filter with a circuit size of 0.08λg × 0.08λg is implemented and tested. Good agreement between simulation and measured results verifies the reliability of the designing scheme.

As technology advances, smart homes are being increasingly adopted, thus generating massive data that pose new research challenges. We propose a machine learning framework for monitoring energy consumption in smart home devices. The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home. We employ three outlier-based techniques for anomaly detection: (1) local outlier factor, (2) connectivity-based outlier factor, and (3) cluster-based local outlier factor. Furthermore, we apply random forest, linear regression, decision tree, and the ensemble techniques of adaptive, gradient, and extreme gradient boosting to anomaly free data to develop baseline models that predict the energy consumption patterns of smart home devices. The framework is evaluated on three publicly available energy datasets collected from various smart homes. The experimental results reveal that the cluster-based local outlier factor with extreme gradient boosting achieves promising results with high prediction accuracy.

The eye socket is a cavity in the skull that encloses the eyeball and its surrounding muscles. It has unique shapes in individuals. This study proposes a new recognition method that relies on the eye socket shape and region. This method involves the utilization of an inverse histogram fusion image to generate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep-learning models. Four distinct benchmark datasets (Flickr30, BioID, Masked AT & T, and CK+) were used to evaluate the method's performance. These datasets encompass a range of perspectives, including variations in eye shape, covering, and angles. Experimental results and comparative studies indicate that the proposed method achieved a significantly ($�p�<�0.001$) higher accuracy (average value greater than 92.18%) than that of the relevant identity recognition method and state-of-the-art deep networks (average value less than 78%). We conclude that this improved generalization has significant implications for advancing the methodologies employed for identity recognition.

The power relay assembly (PRA) is an essential component to ensure the safety of an electric vehicle. We propose a semiconductor-based precharge switch to overcome the shortcomings of the conventional mechanical relay, which is currently used as the precharge switch in the PRA. The gate-driving circuit uses a photocoupler instead of a gate-driving integrated circuit to reduce complexity and cost. Five devices, namely, two insulated-gate bipolar transistors (IGBTs), two silicon carbide metal–oxide–semiconductor field-effect transistors (SiC MOSFETs), and one metal–oxide–semiconductor-controlled thyristor (MCT) are tested as candidate precharge switches. Each device is analyzed under varying battery voltage and fixed measurement conditions. The IGBT and SiC MOSFET burn below 600 V, while the MCT exhibits normal operation up to 800 V. The MCT precharge switch can solve shortcomings of the conventional mechanical relay and increase the performance with a simple gate-driving circuit. Furthermore, the PRA with an MCT precharge switch can reduce the volume, weight, and cost while improving reliability.

We propose a multicarrier chaotic system that incorporates dual-index and M-ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with $�M$-ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

Image dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets have hindered the transformer effectiveness for image dehazing. Thus, convolutional neural networks (CNNs) remain the mainstream approach for image dehazing, offering robust performance and adaptability. We further explore the potential of CNNs in image dehazing by proposing a multiscale implicit frequency selection network (MIFSN). The proposed MIFSN enhances multiscale representation learning based on U-shaped networks. As hazy and clear images considerably differ in high-frequency components, we introduce an implicit frequency selection module to amplify high-frequency components of features and generate candidate feature maps. Implicit frequency selection attention is then used to emphasize and merge beneficial frequency components. Results from extensive experiments on synthetic and real-world datasets demonstrate the superior performance of MIFSN for image dehazing.

This paper proposes a new companding scheme called three $�\mu$-law companding to reduce the peak-to-average power ratio (PAPR) in a universal filtered multicarrier system. The proposed scheme is more flexible and provides better PAPR reduction and bit error rate (BER) through the selection of the three companding parameters. However, the proposed scheme suffers from high side lobes and increases the power of the companded signal. Hence, an enhanced three $�\mu$-law, which overcomes these limitations, is proposed. Unlike the first scheme, the enhanced scheme does not change the mean power of the companding signal because it incorporates power-level normalization. Simulation results show that, compared with other companding transforms, the proposed enhanced three $�\mu$-law scheme offers better BER characteristics and side-lobe level suppression. It also provides better PAPR reduction of 7.4 dB and net gain of 6.3 dB. The proposed enhanced three $�\mu$-law was evaluated over a multipath fading channel and was more robust than other companding techniques. Finally, the proposed enhanced algorithm was verified in real time using the Wireless Open-Access Research Platform.

Routing in underwater sensor networks (UWSNs) is highly challenging because of harsh underwater conditions, such as deep water, high pressure, and rapid ocean currents. Furthermore, UWSNs are vulnerable to jamming attacks because of their limited bandwidth and battery capacity. Advancements in machine learning enable numerous routing methods to address these problems. Accordingly, we propose a novel max or minimax Q-learning (M-Qubed)-based opportunistic routing method for UWSNs. The method uses an opportunistic routing protocol, in which nodes dynamically select the next relay node by considering the status of their neighbors. Moreover, M-Qubed can maximize the benefits for both players in a two-player repeated game through reinforcement learning. Hence, it can reduce the energy loss caused by jamming attacks during routing, thereby increasing the routing efficiency in UWSNs. Simulation results reveal that the proposed routing scheme is less affected by jamming attacks than existing state-of-the-art routing methods. In addition, it can balance energy consumption across the nodes in a UWSN.

In recent years, the growing demand for massive connectivity has focused researchers' attention on next-generation radio access techniques. Nonorthogonal multiple access (NOMA) is a candidate for supporting massive connectivity. However, NOMA systems generate severe intracluster interference, which affects user groups (clusters) with low channel gain differences because of user clustering and the low power disparity between users. Therefore, multiuser NOMA clustering and power management policies must be adopted. We propose schemes for multiuser clustering and joint power allocation. We analytically formulate power allocation for a given signal-to-interference-plus-noise ratio considering users in the cluster with different and similar locations (channel gains) in the cell. The formulation is then applied to NOMA clusters. Finally, we evaluate the optimal cluster size according to the number of users. The proposed schemes outperform other approaches in terms of total cluster throughput and user throughput with the highest channel gain.