Abstract:Integrated Energy System (IES) is of great significance to improve energy efficiency and reduce carbon emissions.Here,a low-carbon optimal scheduling approach is proposed for IES,which considers hydrogen energy utilization and demand response.On the source side,an IES model centering on hydrogen energy utilization is built to optimize the equipment operation flexibility.While on the load side,a demand response model based on Logistic function is built to optimize the load curves thus assist in carbon reduction,which takes into account of the users' energy consumption characteristics.In addition,a tiered carbon trading mechanism is introduced into the optimization model to further explore the carbon reduction potential.Finally,the IES is optimized and scheduled to minimize its total daily operating cost,considering the system's expenditure on energy purchase,operation and maintenance,carbon trading and wind abandonment.Case study shows that the proposed scheduling approach not only achieves peak shaving and valley filling,but also reduces the total operating cost and carbon emission of IES,which verifies its low-carbon and economical characteristics.

Abstract:Integrated energy system (IES) enables the supply of multiple forms of energy,but the large amount of carbon dioxide it emitted affects the surrounding environment.Here,an optimal scheduling approach based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed for low-carbon economic scheduling of IES.First,taking the minimum operation cost as the objective function,an IES model with multiple complementary energies of electricity,heat and cold is established considering carbon capture technology and power-to-gas technology.Second,a carbon trading mechanism is introduced to stimulate the enthusiasm of energy conservation and emission reduction under optimal scheduling.Then,according to the reinforcement learning framework,the state space,action space and reward function of the optimization model are designed,and the agents in the TD3 algorithm are used to interact with the environment to explore strategies and learn the IES operation strategies.Finally,the historical data are used to train the agents of TD3 algorithm,and the linear programming and particle swarm optimization are compared under different scenarios.The results show that the proposed approach can reduce the IES carbon emission and operating cost,thus realizing the low-carbon economic dispatch of the integrated energy system.

Abstract:To guarantee the reliable and economical operation of standalone microgrids,the optimal configuration of power capacity must be determined during the planning phase.The selection of power source types and power capacities of standalone microgrids is affected by internal load levels and the unique natural resource conditions such as wind,photovoltaic (PV),water and storage potential of the respective regions.This paper investigates the comprehensive natural resource conditions of "wind/PV/water/storage" across different regions and establishes a multi-objective optimization model aimed at minimizing the annual generation cost,while considering the reliable power supply and environmentally friendly power generation.Constraints related to electric power and energy balance are incorporated into the optimization model,which is solved via a linearization algorithm.To address the uncertainty in the output of wind,PV and water power,we employ Generative Adversarial Networks (GANs) to simulate multiple scenarios,which are then reduced via an improved K-Medoids clustering algorithm,thereby enhancing the computational efficiency.Furthermore,an index evaluation system is constructed to analyze the characteristics of wind,PV and water resources,and the natural resource levels of 31 provincial-level administrative regions in China's mainland are obtained using a fuzzy evaluation method.By comparing the power capacity configurations of standalone microgrids in 5 representative regions with varying natural resources,this study validates the feasibility and rationality of the proposed approach,and provides valuable insights for the planning of standalone microgrid system.

Abstract:Reliable and effective medium- to long-term power demand forecasting serves as a crucial foundation for power generation and transmission.With the rapid development of China's renewable energy sector,the impact of wind and solar power volatility cannot be overlooked.Consequently,ensuring that future power system planning can economically and efficiently adapt to varying demand scenarios has become a topic of high concern.Here,we propose an integrated evaluation model for predictive dispatch based on the Extreme Learning Machine (ELM) optimized by the Bat Algorithm (BA),alongside the introduction of fuzzy parameters in the cooperative source-load-storage operation algorithm.Moreover,an analysis and research study has been conducted in northwest China as an example.The results show that this model can accurately forecast power demand under diverse development scenarios and provides scientific guidance for optimizing the planning of source-load-storage resources.

Abstract:To address issues perplexing classic image dehazing methods,including halo effect in edge regions,color distortion in bright areas like sky,and hue shifts,we propose a novel image dehazing approach based on improved dark channel prior (SSPDCP:Dark Channel Prior based on Sky Detection and Super Pixel).This approach first applies HSV color transformation to hazy images to extract the brightness component for adaptive-threshold segmentation.Then it utilizes image connectivity analysis to identify the sky regions,from which the atmospheric light value is estimated,and separate transmittance maps of sky and non-sky areas are computed with a luminance model and a superpixel segmentation-based dark channel prior model,respectively.Subsequently,a superpixel-based fusion model is proposed to obtain a comprehensive transmittance map,ensuring smooth transition in boundary areas,which is further refined by multi-scale guided filtering.Finally,the dehazed image is naturally restored via the atmospheric scattering model and brightness enhancement processing.Experimental results show that the proposed approach identifies sky regions more continuously and completely,moreover,by employing superpixels instead of square windows,it effectively mitigates halo effects in acquiring transmittance maps.The estimation of atmospheric light values and transmittance maps is more objective and accurate.Both subjective qualitative and objective quantitative evaluations reveal advantages such as low overall error,excellent signal-to-noise ratio,and high structural similarity in dehazed images.Compared to the state-of-the-art methods,the proposed approach restores skies more naturally,weakens halo effect in edge regions,and achieves qualitative and quantitative improvements in dehazing performance.

Abstract:EEG,as a direct response to brain activity,can objectively reflect a person's emotional state.However,the non-smoothness and complexity of EEG signals make it difficult to collect a large number of labelled EEG samples,thus limiting the effectiveness and generalization performance of EEG emotion recognition methods.Here,a Semi-Supervised Low-Rank Representation (SSLRR) approach for EEG emotion recognition is proposed to address the above issues.First,an objective function in regression form is designed using the estimated labels of a small number of labelled EEG samples to effectively estimate the labels of unlabelled samples.Second,an ε-drag-and-drop technique is used to ensure label-to-label separability,and in addition,low-rank constraints are imposed on the slack labels to improve their intra-class tightness and similarity.Then,a class neighborhood graph is incorporated into the proposed approach to capture the local neighborhood information of all EEG sample data.Comparative experiments are conducted on two public datasets of SEED-Ⅳ and SEED-Ⅴ,and the results show that the proposed approach performs well in EEG emotion recognition.

Abstract:The development mode of transportation infrastructure construction has shifted from pursuing speed and scale to prioritizing quality and efficiency.As a result,there has been an increasing demand for fine-tuning and adapting the tunnel lighting environment,which should prioritize the safety and comfort of drivers.Based on tunnel lighting theory and driving behavior researches,eight indicators that affect driver safety and comfort were selected to establish a driving safety and comfort evaluation system.Indoor simulation was conducted through tunnel driving simulation environment and hardware facilities.A virtual simulation model of the tunnel was established using UC-win／Road software,and data corresponding to the eight indicators were obtained through driving simulators and physiological instruments.Then the entropy method was used to determine the weights of the indicators,and the driving safety and comfort in the tunnel under different lighting brightness and color temperature environments were evaluated.The target value of brightness and color temperature in the tunnel were obtained with the goal of achieving the optimal value of driver safety and comfort.The experimental results confirm the effectiveness of the driver safety and comfort evaluation system,propose an improved tunnel lighting scheme,and provide reference for tunnel operators.

Abstract:Fault location is crucial for the long-distance HVDC transmission systems.Here,a fault location model using the Improved Pelican Optimization Algorithm (IPOA) to optimize the Least Squares Support Vector Machine (LSSVM) is provided to address the issues of imprecise attenuation coefficient computation and challenging secondary wave head capture.First,in accordance with the traveling wave attenuation concept,the formulas of the fault distance and the modulus maximum ratio of the line mode components at both ends of the line are derived,revealing a nonlinear relationship between them,which is then generalized by LSSVM.Second,the IPOA is employed to optimize the key parameters of LSSVM,thereby constructing the IPOA-LSSVM fault location model.After performing wavelet transform on the fault signals collected at both ends,the amplitude ratio of the first wave head is obtained and then input into the proposed model to output the fault distance as simulation verification.Simulation results show that the proposed model can locate fault reliably and accurately regardless of transition resistance and fault type.

Abstract:Here,an Active Noise Control (ANC) approach is proposed which replaces Filtered-x Least Mean Square (FxLMS) algorithm with Dual-decoder Convolutional Recurrent Network (DCRN).Due to the importance of phase information in ANC,the input feature of DCRN is the complex spectrogram of the noise signal (including real and imaginary spectrograms).In the network structure,a coding module is used to extract features from the noise complex spectrograms,and a dual-decoder module is used to estimate the real and imaginary spectrograms of the network output.Parameter sharing mechanism and group strategy are adopted to reduce the number of training parameters and improve the learning ability and generalization performance.Especially for wind noise,a new loss function is adopted and the training data are regularized to improve the performance of DCRN.Experiments in both simulation and ANC headphone environments show that the DCRN approach exhibits good noise reduction performance and robustness for both general noise and wind noise.

Abstract:Physical layer security techniques utilize the wireless channel environment to dynamically generate keys,however,in quasi-static environment,slow channel transformation leads to insufficient key randomness and security.Here,a Backtracking Scrambled Key Generation (BSKG) algorithm is proposed.First,the real and imaginary parts of the channel coefficients are split and quantized to generate a longer key,which is reconciled,then the sum of the inconsistent indexes between the current key and previous key is used to generate a scrambling code to scramble the current key.Simulation shows that,compared with the existing multi-dimensional information and artificial randomness key generation method,the proposed algorithm has higher key generation rate and security,and the key leakage rate is close to 0.5 with the increase of the one-time pad key generation times,even if more relevant channel coefficients have been eavesdropped.The upper bounds on the probability of successful eavesdropping and their variations with the number of key generation N for general and bad channel conditions are estimated using semantic security and information-theoretic inequalities,respectively,when giving certain parameters,the upper bounds for these two cases turn out to be 2^-77N and 2^-23N.

Abstract:Two-dimensional (2D) materials have the characteristics of low loss,ultrafast carrier response,and wideband nonlinear saturable absorption,which have originated a range of innovative applications in photonics and photoelectric device owing to their advantages of layered structures.Graphene-like materials have recently been utilized for short and ultrashort pulsed laser generation in visible,near-infrared,and mid-infrared wavelength ranges.This article reviews the recent progress of 2D materials as saturable absorbers for Q-switched and mode-locked solid-state lasers.First,the preparation methods of 2D materials as saturable absorbers,the saturable absorption principle,and methods for measuring nonlinear absorption properties are introduced and explained theoretically.Second,the solid-state pulsed laser is summarized based on performance of 2D saturable absorbers in operating wavelength,output power,and pulse width.Finally,the development trends of two-dimensional saturable absorbers in solid-state lasers are prospected.

Abstract:To address the challenges of accurately estimating rotor speed and position using the Sliding Mode Observer (SMO) due to high-frequency chattering,and the poor robustness and speed tracking performance of the speed loop PI controller,we propose an improved SMO-based speed sensorless control scheme for Permanent Magnet Synchronous Motor (PMSM).First,an Infinite Impulse Response (IIR) filter is incorporated into the SMO within the Direct Torque Control (DTC) system to mitigate the high frequency noise in the SMO output.Then a continuous hyperbolic tangent function is adopted to replace the discontinuous sign function to attenuate the system chattering.Moreover,an Active Disturbance Rejection Control (ADRC)-based speed loop controller is designed to optimize the trade-off between the overshoot and settling time of the system.Finally,a hardware-in-the-loop experimental platform is established in Matlab/Simulink environment.Both simulation and experimental results show that,under identical load and interference conditions,the proposed ADRC-based speed sensorless controller outperforms traditional speed sensorless control systems,effectively suppressing the chattering and exhibiting superior robustness and speed tracking performance.

Abstract:The photon-counting laser altimeter on the Ice,Cloud and Land Elevation 2 (ICESat-2) satellite offers a solution for tracking the dynamic water level variations in medium and large inland lakes.We utilize the monthly ATL13 global inland water data of ICESat-2 satellite from 2018 to 2020 to estimate and analyze the water level change in Poyang Lake.The measured data from Hukou,Xingzi and Kangshan hydrological stations are used for verification and error correction,and the water level and rainfall data of each station are combined to analyze the dynamic variation of Poyang Lake water level and reveal the underlying drivers.The results show that,the annual water level of Poyang Lake varied sharply with obvious seasonal variations and an overall upward trend;the high water level period was from June to October,which peaked from July to September.The linear correlation coefficient of water levels between ICESat-2 and measured data is above 0.846,rising to 0.974 after error correction.The Root Mean Square Error (RMSE) is 1.660 m,1.073 m,and 0.836 m for Hukou,Xingzi,and Kangshan stations,respectively;error correction and recalculation can decrease the RMSE to 0.663 m,0.659 m,and 0.440 m for Hukou,Xingzi,and Kangshan stations,respectively,enhancing the measurement accuracy by nearly one meter.The variation of water level in Poyang Lake is highly correlated with the change of rainfall,the reduced precipitation during periods from January to February and October to December corresponds to the declining water level in dry season,while the increased rainfall from March to October corresponds to the water level rise in wet season,and the precipitation concentration period from July to September aligns with the peak of water level in Poyang Lake.

Abstract:To address the accurate extraction of sparse and low mangroves perplexed by the periodic change of tide level,we take the Beibu Gulf of Guangxi as the research area to construct a decision tree model for mangrove identification using Landsat8 OLI images at low and high tidal levels and DEM (Digital Elevation Model) data,which is then evaluated by comparing with SVM (Support Vector Machine).The research results show that difference exists in the spectra of mangroves with different heights and canopy densities or under different tide levels,while the sparse and low mangroves share the same spectrum with shady slope forest and water-terrestrial vegetation mixed pixel.The SVM approach classifies the mangroves as high-dense type and low-sparse type,and improves the overall accuracy by 4.65,4.41 and 7.22 percentage points for low-tide,high-tide and multi-tide images,respectively.The proposed approach reaches 98.80％ of overall accuracy and 0.973 of Kappa coefficient,which are 1.62 percentage points and 0.035 higher than the best values of SVM approach.It can be concluded that considering the mangrove height,density,tide level and DEM can significantly improve the identification accuracy of mangroves from remote sensing images.