Wearable devices have great application potential in the next generation of smart portable electronics, especially in the fields of medical monitoring, soft robotics, artificial intelligence, and human-machine interfaces. Piezoelectric flexible strain sensors are key components of wearable devices. However, existing piezoelectric flexible strain sensors have certain limitations in weak signal monitoring due to their large modulus and low sensitivity. To solve this problem, the concept of Kirigami (paper-cutting) was introduced in this study to design the sensor structure. By comparing the Kirigami structures of different basic structures, the serpentine structure was determined as the basic configuration of the sensor. The serpentine structure not only provides excellent tensile properties, but also significantly improves the sensitivity of the sensor, which performs well in monitoring weak signals. On this basis, the adhesion properties of the flexible sensor were analyzed and tested, and the optimal ratio of the substrate was selected for preparation. In addition, a low-cost and rapid prototyping process for stretchable patches was established in this study. Using this technology, we prepared the sensor device and tested its performance. Finally, we successfully developed a flexible sensor with a sensitivity of 0.128 mV/µɛ and verified its feasibility for wrist joint motion monitoring applications. This result opens up new avenues for the recovery care of tenosynovitis patients after surgery.

To achieve high quality factor and high-sensitivity refractive index sensor, a slot micro-ring resonator(MRR) based on asymmetric Fabry-Perot (FP) cavity was proposed. The structure consisted of a pair of elliptical holes to form an FP cavity and a micro-ring resonator. The two different optical modes generated by the micro-ring resonator were destructively interfered to form a Fano line shape, which improved the system sensitivity while obtaining a higher quality factor and extinction ratio. The transmission principle of the structure was analyzed by the transfer matrix method. The transmission spectrum and mode field distribution of the proposed structure were simulated by the finite difference time domain(FDTD) method, and the key structural parameters affecting the Fano line shape in the device were optimized. The simulation results show that the quality factor of the device reached 22 037.1, and the extinction ratio was 23.9 dB. By analyzing the refractive index sensing characteristics, the sensitivity of the structure was 354 nm·RIU^-1, and the detection limit of the sensitivity was 2×10^-4 RIU. Thus, the proposed compact asymmetric FP cavity slot micro-ring resonator has obvious advantages in sensing applications owing to its excellent performance.

The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost, complexity, and accuracy. Therefore, a novel system was developed for precise broadband impedance spectrum measurement of solar cells, which was composed of an oscilloscope, a signal generator, and a sampling resistor. The results demonstrate concurrent accurate measurement of the impedance spectrum (50 Hz-0.1 MHz) and direct current voltametric characteristics. Comparative analysis with Keithley 2450 data yields a global relative error of approximately 6.70%, affirming the accuracy. Among excitation signals (sine, square, triangle, pulse waves), sine wave input yields the most accurate data, with a root mean square error of approximately 13.301 6 and a global relative error of approximately 4.25% compared to theoretical data. Elevating reference resistance expands the half circle in the impedance spectrum. Proximity of reference resistance to that of the solar cell enhances the accuracy by mitigating line resistance influence. Measurement error is lower in high-frequency regions due to a higher signal-to-noise ratio.

Motor imagery　(MI) based electroencephalogram(EEG) represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation. This study introduces a novel time embedding technique, termed traveling-wave based time embedding, utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures. Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference, our approach captures time-related changes for different participants based on a priori knowledge. Through extensive experimentation with multiple participants, we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture. Significantly, our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy, particularly for participants typically considered “EEG-illiteracy”. As a novel direction in EEG research, the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.

Road extraction based on deep learning is one of hot spots of semantic segmentation in the past decade. In this work, we proposed a framework based on codec network for automatic road extraction from remote sensing images. Firstly, a pre-trained ResNet34 was migrated to U-Net and its encoding structure was replaced to deepen the number of network layers, which reduces the error rate of road segmentation and the loss of details. Secondly, dilated convolution was used to connect the encoder and the decoder of network to expand the receptive field and retain more low-dimensional information of the image. Afterwards, the channel attention mechanism was used to select the information of the feature image obtained by up-sampling of the encoder, the weights of target features were optimized to enhance the features of target region and suppress the features of background and noise regions, and thus the feature extraction effect of the remote sensing image with complex background was optimized. Finally, an adaptive sigmoid loss function was proposed, which optimizes the imbalance between the road and the background, and makes the model reach the optimal solution. Experimental results show that compared with several semantic segmentation networks, the proposed method can greatly reduce the error rate of road segmentation and effectively improve the accuracy of road extraction from remote sensing images.

Instance segmentation plays an important role in image processing. The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end, which can improve the performance of instance segmentation, but has defects such as slow segmentation speed and sub-optimal initial contour. To solve these problems, a real-time instance segmentation algorithm based on contour learning was proposed. Firstly, ShuffleNet V2 was used as backbone network, and the receptive field of the model was expanded by using a 5×5 convolution kernel. Secondly, a lightweight up-sampling module, multi-stage aggregation (MSA), performs residual fusion of multi-layer features, which not only improves segmentation speed, but also extracts effective features more comprehensively. Thirdly, a contour initialization method for network learning was designed, and a global contour feature aggregation mechanism was used to return a coarse contour, which solves the problem of excessive error between manually initialized contour and real contour. Finally, the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour. The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD), Cityscapes and Kins datasets, and the average precision reached 55.8 on the SBD; Compared with Deep Snake, the model parameters were reduced by 87.2%, calculation amount was reduced by 78.3%, and segmentation speed reached 39.8 frame·s^-1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU. The proposed method can reduce resource consumption, realize instance segmentation tasks quickly and accurately, and therefore is more suitable for embedded platforms with limited resources.

A facile encryption way was successfully applied to the holographic optical encryption system with high speed, multi-dimensionality, and high capacity, which provided a better security solution for underwater communication. The reconstructed optical security system for information transmission was based on wavelength λ and focal length f that were keys to encryption and decryption. To finish the secure data transmission (λ, f) between sender and receiver, an extended Rivest-Shamir-Adleman(ERSA) algorithm for the encryption was achieved based on three-dimension quaternion function. Therein, the Pollard’s rho method was used for the evaluation and comparison of RSA and ERSA algorithms. The results demonstrate that the message encrypted by the ERSA algorithm has better security than that by RSA algorithm in the face of unpredictability and complexity of information transmission on the unsecure acoustic channel.

Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid. Therefore, we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM). Firstly,　the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs) using variational modal decomposition(VMD). Secondly, random numbers in population initialization were replaced by Tent chaotic mapping, multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator, and then each component was predicted. Finally, Tent multi-objective AVOA-LSSVM(TMOALSSVM) method was used to sum each component to obtain the final prediction result. The simulation results show that the improved AVOA based on Tent chaotic mapping, the improved non-dominated sorting algorithm with elite strategy, and the improved crowding operator are the optimal models for single-objective and multi-objective prediction. Among them, TMOALSSVM model has the smallest average error of stroke power values in four seasons, which are 0.069 4, 0.054 5 and 0.021 1, respectively. The average value of DS statistics in the four seasons is 0.990 2, and the statistical value is the largest. The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision, and faster and more accurately finds the optimal solution on the current solution space sets, which proves that the method has a certain scientific significance in the development of wind power prediction technology.

Considering the instability of the output power of photovoltaic(PV) generation system, to improve the power regulation ability of PV power during grid-connected operation, based on the quantitative analysis of meteorological conditions, a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed. Firstly, through the fuzzy clustering processing of meteorological conditions, taking the power curves of PV power generation in sunny, rainy or snowy, cloudy, and changeable weather as the reference, the local mean decomposition(LMD) was carried out respectively, and their energy entropy (EE) was taken as the meteorological characteristics. Then, the historical generation power series was decomposed by LMD algorithm, and the hierarchical prediction of the power curve was realized by echo state network(ESN) prediction algorithm combined with meteorological characteristics. Finally, the iterative error theory was applied to the correction of power prediction results. The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power, and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand, customer cancellation service, and change of customer delivery address, based on the ideas of pre-optimization and real-time optimization, a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established. At the pre-optimization stage, an improved genetic algorithm was used to obtain the pre-optimized distribution route, a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm, and a variety of operators were introduced to expand the search space of neighborhood solutions; At the real-time optimization stage, a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems, and four neighborhood search operators were used to quickly adjust the route. Two different scale examples were designed for experiments. It is proved that the algorithm can plan the better route, and adjust the distribution route in time under the real-time constraints. Therefore, the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

With the rapid development of urban rail transit, there have been an urgent problem of excessive stray current. Because the stray current distribution is random and difficult to verify in the field, we designed an improved stray current experimental platform by replacing the simulated aqueous solution with a real soil environment and by calculating the transition resistance by measuring the soil resistivity, which makes up for the defects in the previous references. Firstly, the mathematical models of rail-drainage net and rail-drainage net-ground were established, and the analytical expressions of current and voltage of rail, drainage net and other structures were derived. In addition, the simulation model was built, and the mathematical analysis results were compared with the simulation results. Secondly, the accuracy of the improved stray current experimental platform was verified by comparing the measured and simulation results. Finally, based on the experimental results, the influence factors of stray current were analyzed. The relevant conclusions provide experimental data and theoretical reference for the study of stray current in urban rail transit.

Titanium alloys play an important role in aerospace and other fields. However, after precision forging and cold rolling process, some defects will appear on the subsurface of titanium alloy bars, thus reducing the surface quality and precision of turning process. This study aimed at exploring the effect of crack defects on TC4 cutting. Firstly, the finite element cutting simulation model of TC4 material with crack defects was established in ABAQUS. Then, the cutting parameters such as cutting force, stress concentration, chip morphology, residual stress were obtained by changing the variables such as the size and height of crack defects. Finally, the turning experiment was carried out on centerless lathe. The results show that the cutting force changes abruptly when the defect position is located on the cutting path, the maximal stress occurs at the tip of the defect, and the mutation of stress value is more serious with the increase of defect size; the buckling deformation of chip morphology occurs and becomes less serious with the increase of the distance between the defect position and the workpiece surface; the surface residual stress near the defect is related to the stress when the tool is close to the defect, the larger defect size and the closer to the machined surface, the greater the residual stress. Therefore, under certain processing conditions, the TC4 material should avoid large size defects or increase the distance between defects and the machined surface, so as to obtain better and stable surface quality.

The performance of lithium-ion batteries(LIBs) gradually declines over time, making it critical to predict the battery’s state of health(SOH) in real-time. This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR) to predict the SOH of LIBs. Firstly, the degradation process of an LIB is analyzed through indirect health indicators(HIs) derived from voltage and temperature during discharge. Next, the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA), and the EGPR is employed to estimate the SOH of LIBs. Finally, the proposed model is tested under various experimental scenarios and compared with other machine learning models. The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration (NASA) LIB. The root mean square error(RMSE) is maintained within 0.20%, and the mean absolute error(MAE) is below 0.16%, illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

The production and utilization of high-energetic explosives often pose a range of safety hazards, with sensitivity being a key factor in evaluating these risks. To investigate how temperature, particle size, and air humidity affect the responsiveness of commonly used high-energetic explosives, a series of BAM(Bundesanstalt für Materialforschung und-prüfung) impact and friction sensitivity tests were carried out to determine the critical impact energy and critical load pressure of four representative high-energetic explosives (RDX, HMX, PETN and CL-20) under different temperatures, particle sizes, and air humidity conditions. The experimental findings facilitated an examination of temperature and particle size affecting the sensitivity of high-energetic explosives, along with an assessment of the influence of air humidity on sensitivity testing. The results clearly indicate that high-energetic explosives display a substantial decline in critical reaction energy when subjected to micrometre-sized particles and an air humidity level of 45% at a temperature of 90 ℃. Furthermore, it was noted that the critical reaction energy of high-energetic explosives diminishes with an increase in temperature within 25 ℃-90 ℃. In the same vein, as the particle sizes of high-energetic explosives increase, so does the critical reaction energy for micrometre-sized particles. High air humidity significantly affects the sensitivity testing of high-energetic explosives, emphasizing the importance of refraining from conducting sensitivity tests in such conditions.