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Remote sensing semantic change detection (SCD) involves extracting information about changes in land cover/land use (LCLU) within the same area at different times. This issue is of crucial significance in many Earth observation tasks, such as precise urban planning and natural resource management. However, the current methods primarily focus on spatial feature extraction, lacking awareness of temporal features. Consequently, there are challenges in extracting change features, making distinguishing intraclass and interclass differences difficult. This also contributes to pseudochange, posing challenges for SCD tasks. To overcome the limitations of existing methods, we present a dual-dimension feature interaction network (DFINet) for SCD. First, to enhance the assessment and perceptual abilities related to intraclass and interclass differences, we introduce a temporal difference feature enhancement (TDFE) module. This module comprehensively captures features from the temporal dimension. Then, to address the interrelation between multitemporal and multilevel features, we investigate the feature selection interaction (FSIA) and interaction attention modules (IAM), which enable multidimensional deep fusion and interaction of change features. This enhances the capacity for information transfer and integration among the features within multitemporal remote sensing images (RSIs). The experimental results demonstrate that, compared to existing methods, the proposed architecture achieves a significant improvement in accuracy. Additionally, the design enhancements added to DFINet boost the practicality of remote sensing SCD, underscoring its substantial research value.

The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the scheduling potential of EVs, this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

In response to the modeling difficulties and low search efficiency of traditional weapon-target assignment algorithms, this paper proposes a deep reinforcement learning-based intelligent weapon-target assignment method. A weapon-target intelligent assignment model with strong decision-making capabilities (RL4WTA) is obtained by training. Firstly, a multi-constraint weapon-target assignment optimization model is established to discretize the dynamic weapon-target assignment problem into a static weapon-target assignment problem. Furthermore, a planning and solving environment for the weapon-target assignment (WTA) problem is designed, and a Markov Decision Process (MDP) for WTA tasks is constructed based on the planning and solving model. This provides a foundation for solving the WTA problem using reinforcement learning algorithms. Additionally, a reinforcement learning-based WTA-solving model is proposed in this paper. By utilizing a multi-head Q-value network, the complex joint decision space is decoupled, thereby improving the efficiency of the WTA model. The use of a masking mechanism allows for inferring valid actions that satisfy the constraint conditions under the current situation, reducing uncertainty during the reinforcement learning training process. Experimental results show that the proposed model, RL4WTA, can generate satisfactory solutions adaptively in both small-scale and large-scale scenarios. Compared with traditional optimization algorithms, the model is superior in adaptability and computational efficiency, meeting the requirements of making optimal decisions for weapon-target assignment problems.

An ultracompact and broadband silicon wavelength demultiplexer based on SWG-assisted directional coupler is designed and experimentally demonstrated for wavelength splitting of 1310 and 1550 nm. The embedded SWG enables significantly reduced footprint, due to the precise phase matching at 1550 nm and large phase mismatch at 1310 nm. As a result, the fabricated device has a length of only 9 μm, and provides high extinction ratios (ERs) of 23 dB and 19 dB at 1310 and 1550 nm, respectively. Its 3-dB bandwidths are 85 nm for O-band (ER > 15 dB) and 140 nm for C-band (ER > 10 dB).

Advancements in smart grid technology and the extensive applications of electric power big data have made in-depth exploration of the behavioral characteristics of power consumers highly necessary for further development of the electricity market. This paper proposes an effective and interpretable load curve classification method that based on sequential trajectory feature learning and a random forest algorithm. Firstly, the unsupervised K-medoid clustering algorithm is used to obtain and filter precise category labels. Next, the fused lasso generalized eigenvector (FLAG) technique is used to search for interpretable sub-sequences from the labeled data in order to properly account for the sequential trajectory feature of load curves and increase the speed of the computation process. Following that, shapelet transformation is used to extract the sequential trajectory features from original data. Finally, in order to inherit the interpretability of shapelet, the random forest is trained on the sequential trajectory features. The simulated examples based on the real load curves of the specific city in China were investigated in order to assess the performance of the proposed load curve categorization approach. The results of the simulation demonstrate that the proposed approach has considerable advantages in terms of effectiveness, accuracy, and interpretability of load classification.

This study proposes a deep reinforcement learning (DRL) based approach to analyze the optimal power flow (OPF) of distribution networks (DNs) embedded with renewable energy and storage devices. First, the OPF of the DN is formulated as a stochastic nonlinear programming problem. Then, the multi-period nonlinear programming decision problem is formulated as a Markov decision process (MDP), which is composed of multiple single-time-step sub-problems. Subsequently, the state-of-the-art DRL algorithm, i.e., proximal policy optimization (PPO), is used to solve the MDP sequentially considering the impact on the future. Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN. The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results. The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones. Comparative results demonstrate the effectiveness of the proposed approach.

Novel successive cancellation list (SCL) decoding schemes of Polar codes by exploiting the error correction (EC) capability of the cyclic redundancy check (CRC) codes are proposed in this paper. Different from the schemes in the literature that only considered the error detection capability of CRC, the proposed schemes aim to improve the performance of CRC-aided Polar decoding to the most by benefiting from the potential EC capability of CRC. Specifically, we first propose a basic decoding scheme, named CRC error-correction-aided successive cancellation list (CRC-EC-SCL) decoding, by introducing the look-up-table-based CRC EC method into the Polar SCL decoding process. Then, based on the basic scheme, we further propose the segmented-CRC-EC-SCL decoding scheme. By segmented-CRC, the information block is divided into multiple segments and each segment is attached with a shorter CRC sequence. By conducting the CRC-EC-aided coding for each of the segments, multiple erroneous bits can be corrected early and error propagation can, thus, be efficiently suppressed. The simulation results show that both of the proposed decoding schemes have a significant performance gain over the classic CRC-EC-SCL decoding scheme. Especially for the segmented-CRC-EC-SCL, the performance gain at a frame error rate of 10−4 is about 0.4 dB. Moreover, the average computation complexity of the segmented-CRC-EC-SCL is close to the classic scheme.

Renewable energy is widely used as a clean energy in the world, yet the intermittent of the power generation from renewable energy power plants, resulting in poor quality of power supply. However, the hybrid energy system and some energy storage devices can be installed to mitigate the power fluctuation, to achieve power smoothing and maximize the profit, a double-objective optimization model of hybrid system integrated with micro-hydro power (MHP), photovoltaic (PV), and pumped hydro storage (PHS) is established in this study. The day-ahead optimized operation strategy of the PHS based-on hour-level data of seven days is obtained, which aims to maximize the similarity value (SIM) between the power generation curve and load profile, and the economic revenue (ER) of the PHS. Optimized schedule is obtained by means of chance-constrained programming (CCP) and modified multi-objective particle swarm optimization (MOPSO). The performance of the model is evaluated by using the meteorological, the historical MHP power generation and the load data obtained in Xiaojin County, Sichuan province, China. The simulation results reveal that the best trade-off value between SIM and ER can be obtained on the Pareto front with comprehensive consideration of the system operation strategy and target. Furthermore, the generation/load similarity can be increased 7.89% under the participating of the PHS.

We demonstrate the design and fabrication of a mode (de)multiplexer based on the tapered mode-selective coupler. The proposed device is directly inscribed in the substrate material by a needle-type liquid micro-dispenser and a three-axis robot stage using commercially available UV-curable resins. The fabricated mode (de)multiplexer has an excess loss less than 0.3 dB, a coupling ratio higher than 0.93, and an extinction ratio higher than 23 dB within a broadband of 100 nm around 1550 nm. The near field patterns of LP$_{11}$ mode at different wavelengths were successfully observed using an infrared charge-coupled-device camera. The fabrication tolerance on the spacing, core diameter deviation, and core circularity were investigated and the results imply that our fabrication process can satisfy the requirements on it. The proposed method is capable of fabricating functional devices for high-speed on-board optical interconnects.

An innovative scheme for concatenating the polar codes with the recently proposed spinal codes is presented in this paper. The primary objective is to improve the performance of the polar codes in the finite-length regime. One effective used way is to concatenate the polar codes with short outer codes. Essentially, the short outer codes that have as high performance as possible is required for concatenation. To this end, the newly invented spinal codes are used as the outer codes, which are characterized by its great error correcting capability even when the message length is short. First, the proposed codes, named spinal-polar, is implemented through an interleaved concatenation scheme. A joint iterative decoding algorithm is proposed accordingly, and the decoding complexity of the algorithm is also analyzed. Second, in order to reduce the decoding complexity of the full concatenation while maintaining the BER performance, the partial concatenation scheme is presented. Finally, we propose a rate allocation algorithm to further improve the BER performance of the designed concatenated codes. Extensive simulations results indicate that the proposed spinal-polar codes have significant performance improvement over the stand-alone polar codes, and the other improvement strategies mentioned earlier have also been proved to be effective.

Bilateral teleoperation systems with haptic feedback allow human users to interact with objects or perform complex tasks in remote or inaccessible environments. Communication delays in teleoperation systems jeopardize system stability and transparency, leading to degraded system performance and poor user experience. In this paper, we provide a survey of the model-mediated teleoperation (MMT) approach, which has been developed to guarantee both system stability and transparency in the presence of arbitrary communication delays. This survey focuses on two major parts: 1) the historical development of the MMT approach from the late 1980s to the present and 2) the main challenges facing the design of a reliable MMT system. Along with the discussion of the MMT challenges and the proposed solutions, a series of experiments has been conducted to compare the performance between the existing techniques and to supply data that were missing in the previous studies on the MMT approach.

In semiconductor spin quantum bits (qubits), the radio-frequency (RF) gate-based readout is a promising solution for future large-scale integration, as it allows for a fast, frequency-multiplexed readout architecture, enabling multiple qubits to be read out simultaneously. This article introduces a theoretical framework to evaluate the effect of various parameters, such as the readout probe power, readout chain's noise performance, and integration time on the intrinsic readout signal-to-noise ratio, and thus readout fidelity of RF gate-based readout systems. By analyzing the underlying physics of spin qubits during readout, this work proposes a qubit readout model that takes into account the qubit's quantum mechanical properties, providing a way to evaluate the tradeoffs among the aforementioned parameters. The validity of the proposed model is evaluated by comparing the simulation and experimental results. The proposed analytical approach, the developed model, and the experimental results enable designers to optimize the entire readout chain effectively, thus leading to a faster, lower power readout system with integrated cryogenic electronics.