Search Results (5,577)

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to $93.56\%$, and it shows robustness across different environments. Full article

This study investigated cow longevity, culling reasons, and risk factors influencing culling in South African Holstein and Jersey dairy herds. Lactation records of 1,150,625 Jersey and 1,534,875 Holstein cows from 1864 herds, recorded through the National Milk Recording Scheme during the period 2000 to 2019, were analyzed. Longevity was calculated as length of productive life and number of completed lactations. Logistic binary regression was conducted to estimate the odds ratios (OR) for culling among different calving seasons, parities, and herd sizes. Holstein cows had mean productive life of 739.33 ± 434.31 days and 2.37 ± 1.08 lactations, while Jersey cows averaged 696.81 ± 415.44 days productive life and 2.47 ± 1.13 lactations. Leading reasons for culling were infertility (37.94 ± 0.48% Holstein; 30.46 ± 0.63% Jersey), mastitis (18.15 ± 0.38% Holstein and 18.16 ± 0.53% Jersey), and low milk yield (11.76 ± 0.32% Holstein and 19.76 ± 0.55% Jersey). Summer calving, third parity, and small herd size had the highest odds of culling. These findings suggest that herd management practices and selection objectives in South Africa should place high emphasis on cow fertility and udder health. Furthermore, cows calving in summer and those in third parity or small herds require particular attention to minimize culling. Such measures may help to reduce involuntary culling rates and thus increase herd profitability as well as dairy industry sustainability. Full article

The influencing factors of wax deposition are numerous and complex, and accurately predicting the wax deposition rate is of great practical significance for the safe operation of pipelines and the formulation of reasonable pigging schemes. On the basis of mastering the prediction steps of the Elman neural network (ENN), the arithmetic optimization algorithm (AOA) was introduced to improve the Elman neural network and an optimization model was established, and the differences in prediction results between improved models (AOA-ENN model, PSO-ENN model, GA-ENN model) and the traditional ENN model were compared and analyzed through examples. The prediction results of three examples showed that the average relative errors of the AOA-ENN model are 2.5470%, 1.4974%, and 2.3819 %, respectively, while the average relative errors of the traditional ENN model are 19.0313%, 9.1568%, and 11.4836%, respectively. Therefore, the arithmetic optimization algorithm used in this paper has good reliability. For the three improved models, the AOA-ENN model has the highest prediction accuracy, followed by the PSO-ENN model and the GA-ENN model. Overall, the Elman neural network improved by an arithmetic optimization algorithm can be used for predicting wax deposition rate, which can provide new ideas for accurate prediction of wax deposition rate. Full article

This paper proposes a novel finite-time adaptive fuzzy control strategy for two-stage continuous stirred tank reactor (CSTR) systems. The method integrates the gradient descent (GD) algorithm with Armijo line search to dynamically adjust the learning rate, thereby optimizing the parameters of fuzzy logic systems (FLSs) for fast and accurate approximation of unknown nonlinear functions. The proposed control scheme, based on finite-time stability theory, ensures convergence of system states to the desired trajectory within finite time. Compared with conventional adaptive fuzzy control methods, the approach effectively addresses the issues of slow convergence and low approximation accuracy, significantly reducing approximation error while enhancing convergence performance. Simulation results on a two-stage CSTR system verify that the proposed controller achieves rapid convergence and high approximation accuracy. Full article

To reduce the renewable energy waste and carbon emissions predicted for the current expansion plan, this study proposes a hierarchical collaborative optimization model for the planning of generation and transmission expansion plan in cross-regional power systems considering energy storage and load transfer. In the upper layer, the upper limit of expansion is determined according to China’s current policy and expansion plan for the power system. This level completes the annual power expansion plan and provides scale data of power generation facilities and supporting infrastructures for the lower level. The lower layer is the operation level, which simulates the operation of the power system throughout the year. To find the defects of the current plan and provide an optimization scheme, the optimization model is used to analyze China’s power system in 2030. The utilization of renewable energy and power facilities is analyzed, along with the carbon emissions. An improved power expansion plan that comprehensively considers energy storage, transmission and load transfer for China’s carbon peak is proposed. The proposed scheme increases the utilization rate of renewable energy to 97.058%, reduces CO₂ emissions by 224 million tons, and reduces the installed capacity of thermal power by about 18.686 million kilowatts, verifying the effectiveness of the scheme. Full article

Automated berthing remains a critical challenge for autonomous surface vessels (ASVs), necessitating precise berthing state estimation as a fundamental prerequisite. In this paper, we present a novel berthing state estimation method tailored for ASVs and based on 3D LiDAR technology. Firstly, a berthing plane acquisition scheme based on point cloud plane fitting is proposed; the feasibility of the scheme was verified by experiments. The point cloud registration algorithm was used to realize the ship pose estimation. Before registration, the preprocessing technology was used to filter out the noise and outliers in the point cloud data to improve the accuracy of pose estimation. A detailed method for calculating the berthing state information is proposed. This method considers the influence of ship roll, pitch, and yaw during berthing, and ensures the accuracy of the obtained state information. Finally, a real-time ship berthing perception framework was constructed using the Robot Operating System (ROS), enabling the continuous output of vital berthing state information, including berthing distance, velocity, approaching angle, and yaw rate, at a frequency of 10 Hz. To validate the effectiveness of our algorithm, extensive real ship experiments were conducted, yielding highly promising results. The average angle error was found to be less than 0.26°, with an average distance error below 0.023 m. Full article

Generative steganography (GS) generates stego-media via secret messages, but existing GS only targets single-type multimedia data with poor universality. The generator and extractor sizes are highly coupled with resolution. Message mapping converts secret messages and noise, yet current GS schemes based on it use gridded data, failing to generate diverse multimedia universally. Inspired by implicit neural representation (INR), we propose generative implicit steganography via message mapping (GIS). We designed single-bit and multi-bit message mapping schemes in function domains. The scheme’s function generator eliminates the coupling between model and gridded data sizes, enabling diverse multimedia generation and breaking resolution limits. A dedicated point cloud extractor is trained for adaptability. Through a literature review, this scheme is the first to perform message mapping in the functional domain. During the experiment, taking images as an example, methods such as PSNR, StegExpose, and neural pruning were used to demonstrate that the generated image quality is almost indistinguishable from the real image. At the same time, the generated image is robust. The accuracy of message extraction can reach 96.88% when the embedding capacity is 1 bpp, 89.84% when the embedding capacity is 2 bpp, and 82.21% when the pruning rate is 0.3. Full article

The article proposes a novel modulation scheme with a low peak-to-average ratio (PAPR), referred to as offset rotation interpolation modulation (ORIM), which is particularly suitable for low-power consumption and enhanced coverage scenarios in the sixth generation (6G) of wireless communication. ORIM comprises three modulation schemes: I-QPSK, I-BPSK, and I-π/2 BPSK. They are derived from cyclic offsetting, phase rotation, and interpolation, and applied to QPSK, BPSK, and π/2 BPSK, respectively. Simulation results in discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) systems demonstrate that ORIM achieves a lower peak-to-average power ratio (PAPR) than the π/2-BPSK scheme specified in the 5G New Radio (NR) protocol, without incurring any performance degradation in terms of block error rate (BLER). Moreover, with the addition of frequency domain spectrum shaping (FDSS), I-π/2 BPSK demonstrates superior performance over π/2-BPSK in both PAPR and BLER metrics under the TDL-A channel conditions. In addition, the complexity of modulation at the transmitting end or demodulation at the receiving end of ORIM is of the same order of magnitude as that of π/2 BPSK, thereby achieving a certain level of overall performance improvement. Full article

Due to the limited and fixed field of view of the onboard camera, the guiding beacons gradually drift out of sight as the AUV approaches the docking station, resulting in unreliable positioning and intermittent data. This paper proposes an underwater autonomous docking visual localization method based on a cage-type dual-layer guiding light array. To address the gradual loss of beacon visibility during AUV approach, a rationally designed localization scheme employing a cage-type, dual-layer guiding light array is presented. A dual-layer light array localization algorithm is introduced to accommodate varying beacon appearances at different docking stages by dynamically distinguishing between front and rear guiding light arrays. Following layer-wise separation of guiding lights, a robust tag-matching framework is constructed for each layer. Particle swarm optimization (PSO) is employed for high-precision initial tag matching, and a filtering strategy based on distance and angular ratio consistency eliminates unreliable matches. Under extreme conditions with three missing lights or two spurious beacons, the method achieves 90.3% and 99.6% matching success rates, respectively. After applying filtering strategy, error correction using backtracking extended Kalman filter (BTEKF) brings matching success rate to 99.9%. Simulations and underwater experiments demonstrate stable and robust tag matching across all docking phases, with average detection time of 0.112 s, even when handling dual-layer arrays. The proposed method achieves continuous visual guidance-based docking for autonomous AUV recovery. Full article

Display-field communication (DFC) is an imperceptible screen-to-camera technology that embeds and recovers data from the frequency domain of an image frame. Conventional DFC requires a reference frame for each data frame to estimate the channel, a method that, while reliable, is not bandwidth-efficient. Similarly, iterative DFC requires the transmission of pilot symbols for channel estimation. In this paper, we propose an implicit DFC (iDFC) scheme that eliminates the need for reference frames by estimating them using the first-order statistics of the received image. The system employs discrete Fourier-transform-based subcarrier mapping and adds data directly to the frequency coefficients of the host image. At the receiver, statistical estimation enables blind channel equalization without sacrificing the data rate. The simulation results show that iDFC achieves an achievable data rate (ADR) of up to $1.52\times {10}^5$ bps, a significant enhancement of approximately 97% and 11% compared to conventional and iterative DFC schemes, respectively. Furthermore, the analysis reveals a critical trade-off between communication robustness and visual imperceptibility; allocating 70% of signal power to the image maintains high visual quality but results in a symbol error rate (SER) floor of $1.5\times {10}^{-1}$, whereas allocating only 10% improves the SER to below ${10}^{-2}$ at the cost of visible artifacts. The findings also identify QPSK as the optimal modulation order that maximizes the data rate, showing that higher-order schemes can be detrimental due to system impairments such as signal clipping. The proposed iDFC scheme presents a more efficient and robust solution for high-capacity DFC applications by balancing the competing demands of data throughput and visual fidelity. Full article

Transformer reliability is crucial to grid security, with core loosening a common fault. This paper proposes a transformer core loosening fault diagnosis method based on a fusion feature extraction approach and Categorical Boosting (CatBoost) optimized by the Crested Porcupine Optimizer (CPO) algorithm. Firstly, the audio signal is decomposed into six Intrinsic Mode Functions (IMF) components through Variational Mode Decomposition (VMD). This paper utilizes Gaussian membership functions to quantify the energy proportion, central frequency, and kurtosis of IMF and constructs a fuzzy entropy discrimination function. Then, the IMF noise components are removed through an adaptive threshold. Subsequently, the denoised signal undergoes a wavelet packet transform instead of a short-time Fourier transform to optimize Mel-frequency cepstral coefficients (WPT-MFCC), combining time-domain statistical features and frequency-band energy distribution to form a 24-dimensional fusion feature. Finally, the CatBoost algorithm is employed to validate the effects of different feature schemes. The CPO is introduced to optimize its iteration number, learning rate, tree depth, and random strength parameters, thereby enhancing overall performance. The CPO-optimized CatBoost model had 99.0196% fault recognition accuracy in experimental testing, 15% better than the standard CatBoost. Accuracy exceeded 90% even under extreme 0 dB noise. This method makes fault diagnosis more accurate and reliable. Full article

This study introduces an innovative self-regulating intelligent optimal balancing control framework for inverted pendulum-type mechatronic platforms, designed to enhance reference tracking accuracy and improve disturbance rejection capability. The control procedure is synthesized by synergistically integrating a baseline Linear Quadratic Regulator (LQR) with a fuzzy controller via a customized linear decomposition function (LDF). The LDF dissociates and transforms the LQR control law into compounded state tracking error and tracking error derivative variables that are eventually used to drive the fuzzy controller. The principal contribution of this study lies in the adaptive modulation of these compounded variables using reconfigurable tangent hyperbolic functions driven by the cubic power of the error signals. This nonlinear preprocessing of the input variables selectively amplifies large errors while attenuating small ones, thereby improving robustness and reducing oscillations. Moreover, a model-free online self-tuning law dynamically adjusts the variation rates of the hyperbolic functions through dissipative and anti-dissipative terms of the state errors, enabling autonomous reconfiguration of the nonlinear preprocessing layer. This dual-level adaptation enhances the flexibility and resilience of the controller under perturbations. The robustness of the designed controller is substantiated via tailored experimental trials conducted on the Quanser rotary pendulum platform. Comparative results show that the prescribed scheme reduces pendulum angle variance by 41.8%, arm position variance by 34.6%, and average control energy by 28.3% relative to the baseline LQR, while outperforming conventional fuzzy-LQR by similar margins. These results show that the prescribed controller significantly enhances disturbance rejection and tracking accuracy, thereby offering a numerically superior control of inverted pendulum systems. Full article

An efficient cooling configuration is critical for ensuring the safe operation of electrical machines and is key for optimizing the iterative design of motors. To improve the heat dissipation performance of high-power, explosion-proof, integrated variable-frequency permanent magnet motors used in mining and reduce the risk of permanent magnet demagnetization, this study considers a 1600 kW mining explosion-proof variable-frequency permanent magnet motor as its research object. Based on the zigzag-type water channel structure of the frame, a novel rotor-cooling scheme integrating axial–radial ventilation structures and axial flow fans was proposed. The temperature field of the motor was simulated and analyzed using a fluid–thermal coupling method. Under rated operating conditions, the flow characteristics of the frame water channel and the temperature distribution law inside the motor were compared when the water supply flow rates were 5.4, 4.8, 4.2, 3.6, 3, 2.4, and 1.8 m³/h, respectively, and the relationship between the motor temperature rise and the variation in water flow rate was revealed. A production prototype was developed, and temperature rise tests were conducted for verification. The test results were in good agreement with the simulation calculation results, thereby confirming the accuracy of the simulation calculation method. The results provide an important reference for enterprises in the design optimization and upgrading of high-power explosion-proof integrated variable-frequency permanent-magnet motors. Full article

The unimolecular alkene elimination reaction class of fatty acid alkyl esters (FAAEs) is a crucial component in the low-temperature combustion mechanism for biodiesel fuels. However, thermo-kinetic parameters for this reaction class are scarce, particularly for the large-size molecules over four carbon atoms and intricate branched-chain configurations. Thermo-kinetic parameters are essential for constructing a reaction mechanism, which can be used to clarify the chemical nature of combustion for biodiesel fuels. In this paper, the B3LYP method, in conjunction with the 6-311G(d,p) basis set, is used to carry out geometry optimization of the species participating in the reactions. Frequency calculations are further executed at the same level of theory. Additionally, coupled with the 6-311G(d,p) basis set, the B3LYP method acts as the low-level ab initio approach, while the Gaussian-4 (G4) composite method serves as the high-level ab initio approach within the isodesmic reaction correction scheme. The CCSD(T) approach is employed to verify the consistency of the electronic energy ascertained through the G4 method. The isodesmic reaction method (IRM) is used to obtain the energy barriers and reaction enthalpies for unimolecular alkene elimination reaction class of FAAEs. Based on the reaction class transition state theory (RC-TST), high-pressure-limit rate coefficients were computed, with asymmetric Eckart tunneling corrections applied across 500~2000 K temperature range. Rate rules at the high-pressure-limit are obtained through the averaging of rate coefficients from a representative collection of reactions, which incorporate substituent groups and carbon chains with different sizes and lengths. Ultimately, the energy barriers, reaction enthalpies, and rate rules at the high-pressure-limit and kinetic parameters expressed as (A, n, E) are supplied for developing the low-temperature combustion mechanism of biodiesel fuels. Full article

Promoting the green transformation of traditional diesel-powered ships is crucial for achieving carbon peaking and carbon neutrality goals. This study focuses on diesel-engine ships operating in the inland river areas of Anhui Province, China. It proposes two electrification retrofit schemes based mainly on lithium iron phosphate (LIP) batteries: full electrification and diesel-engine redundancy. The economic and environmental impacts of these schemes are analyzed and compared with those of conventional diesel-powered ships. A cost prediction algorithm based on model prediction is proposed, supported by a mathematical model for cost analysis. Results indicate that for electric tankers to become economically viable, battery costs must decrease through yearly improvements in energy density and reduced degradation rates. Additionally, government support is essential, such as raising carbon prices and providing subsidies—either an annual operational subsidy of CNY 80,000 or an initial construction subsidy of CNY 500,000. The study concludes that continued advances in battery technology, together with policy and financial support, will accelerate the large-scale electrification of ships. Full article