In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL. We then propose an NTL detection algorithm to solve linear system of equations (LSE) constructed by energy balance analysis with IMMs and the collector, and describe the hardware architecture of IMMs. Simulation results show that the proposed NTL detection algorithm successively detects the honesty of energy consumers as well as the energy loss from bypassing. Furthermore, it is observed the proposed detection framework is time-efficient and detection accuracy is at least 95% when variability of meter reading value is 80%.

In this letter, to improve data rate over wireless communication channels, we propose a deep Q network (DQN)-based adaptive modulation scheme by using Markov decision process (MDP) model. The proposed algorithm makes the reinforcement learning agent to select rate region boundaries as the states, which divide signal-to-noise ratio (SNR) range into rate regions. The simulation results show that spectral efficiency can be improved on the average by 0.5395 bps/Hz in wide SNR range.

We propose a novel data-driven feature extraction approach based on direct causality and fuzzy temporal windows (FTWs) to improve the precision of human activity recognition and mitigate the problems of easily-confused activities and unlabeled data, which significantly degrade classification performance owing to the correlation of labeled data. In recognizing activities, the proposed approach not only considers the importance of oncoming short-term sensor data but also considers the continuity from past activities of the preceding long-term sensor data. In terms of the oncoming data, the causality feature is extracted using the direct transfer entropy to determine the unique pattern of an activity, which represents the quantified causal relationship between sensor activations. In terms of the preceding data, several hours of historical data are compressed to fuzzy features based on FTWs. Subsequently, the causality and fuzzy features are fused by matrix multiplication to express distinct features of activities. To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used. Leave-one-day-out cross-validation was performed based on the CASAS open datasets, including Aruba, Cairo, and Milan. The results showed that the macro-F1-scores were improved by 16.4, 37.5, and 18.5%, respectively, compared with those of the FTW-only environments. In addition, the proposed approach could improve the precision of activity recognition and mitigate the problems associated with the environments containing unlabeled data.

In this paper, we propose methods for the diagnosis of down conductor cables installed inside blades for the reliable operation of wind turbines. Utilizing the two synchronized drones equipped with X-ray devices to take X-ray images inside the blades, we propose deep learning methods for diagnosis based on X-ray images. The first method is the unsupervised deblurring method for blurry X-ray images that are generated by the drones. The key idea of the proposed method is to apply an image sharpening process to the latent space of the trained model using only blurry X-ray images. Through experiments using the X-ray images of blades, we show that the proposed method makes the outline of the down conductor cable clearer. As the second method, we propose an object detection-based method to provide fast and accurate object detection as the diagnosis. Given an X-ray image, we also propose a method to determine drone flight direction to enable drones to follow the down conductor cable. Through experiments, we show that our method has good performance in object detection (i.e., mAP of 98.21%) and classification (i.e., AUC of 0.9), and drone flight direction determination (i.e., AUC of 0.99).

In this paper, we study an interference avoidance scenario in the presence of a smart interferer which can rapidly observe the transmit power of a backscatter wireless sensor network (WSN) and effectively interrupt backscatter signals. We consider a power control with a subchannel allocation to avoid interference attacks and a time-switching ratio for backscattering and RF energy harvesting in backscatter WSNs. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, time-switching ratio, and sub-channel allocation parameter to maximize a utility function against the smart interference. We propose two algorithms for the utility maximization using Lagrangian dual decomposition for the backscatter WSN and the smart interference to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome smart interference in backscatter communications.

In this paper, we study the Stackelberg game-based evolutionary game with two players, generators and energy users (EUs), for monetary profit maximization in real-time price (RTP) demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit of generators and EUs, respectively, rather than maximizing the conventional unified profit of the generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal electricity price calculated by the generators to update their own energy parameters to achieve profit maximization over time, whenever the generators and the EUs execute their energy strategy in the proposed Stackelberg game structure. In the problem formulation, we newly formulate a generator profit function containing the additional parameter of the electricity usage of EUs to reflect the influence by the parameter. The simulation results show that the proposed energy strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme, and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio (PAR) reduction, which is one of the goals of DR.