The rapid development of global manufacturing has made distributed flexible workshop scheduling problems increasingly complex, involving multiple dimensions such as factory task allocation, internal machine scheduling, and job sequence sorting, requiring efficient collaboration. However, in the actual production environment, the arrival times of jobs are usually dynamic. To solve the distributed flexible job shop scheduling problem with dynamic arrival of jobs, a mixed-integer programming model is developed to minimize both the maximum completion time and total energy consumption. Furthermore, a dual objective optimization algorithm based on an improved dual deep Q-network is proposed. To enhance learning efficiency and model generalization, 9 normalized state representation functions are extracted. In addition, considering the characteristics of the problem, 9 composite scheduling rules and multi-objective adaptive reward functions are specifically designed. The results showed that the proposed algorithm can more effectively optimize the maximum completion time and total energy consumption targets, demonstrating significant advantages in solving the dynamic scheduling problem under study.
Accurate species identification of marine microalgae is crucial for water-quality monitoring and harmful algal bloom (HAB) early warning. However, existing microscopic image detection methods struggle with dense, small targets and low-contrast backgrounds. This study presents PHYLA-Det11, an enhanced model based on YOLOv11n, specifically optimized for microalgae detection. Our architecture integrates a four-scale detection head for hierarchical cell modeling, an Attention-guided Feature Fusion and Recalibration Module (AFFRM) to enhance feature extraction and suppress background noise, and a CARAFE upsampling operator to improve reconstruction in low-contrast regions. Benchmarked on the algae-gtnnf and VisAlgae 2023 datasets, PHYLA-Det11 achieved mAP@0.5 improvements of 3.4% and 3.0%, respectively, over the baseline. Notably, recall on the algae-gtnnf dataset increased from 84.8% to 90.7%. With its lightweight design and superior sensitivity to small taxa, PHYLA-Det11 is highly suitable for deployment on shipborne or buoy-based embedded monitoring platforms.
This paper introduces a novel image encryption algorithm that integrates the one-dimensional Logistic chaotic system, the three-dimensional Lorenz chaotic system, compressed sensing (CS), and a dynamic S-box. Initially, the Arnold transform is applied to scramble the pixel positions of the color image, thereby increasing its complexity. Subsequently, compressed sensing technology is utilized for the first-stage encryption, achieving simultaneous measurement and compression. A hybrid chaotic sequence, generated by combining the one-dimensional Logistic chaotic map and the three-dimensional Lorenz chaotic system, is then used to construct a dynamic S-box, which performs secondary encryption on the image data. This approach not only enhances encryption security but also significantly reduces storage and transmission requirements. Experimental results show that the proposed method achieves a high entropy value of 7.9991 bits per pixel, a peak signal-to-noise ratio (PSNR) of up to 40.743 dB, and a structural similarity index (SSIM) close to 1, demonstrating its effectiveness in preserving image quality while providing robust encryption. This work presents a promising solution for securing image data in various applications, including those in the military and medical industries.
This paper presents a comprehensive performance analysis of single, double, and triple material gate (SMGJL, DMGJL, TMGJL) Junctionless SOI FinFETs for Analog and RF applications using Technology Computer-Aided Design (TCAD) simulations at a 30 nm gate length. This study investigates key performance metrics, including Drain Induced Barrier Lowering (DIBL), Subthreshold Swing (SS), ON current (I-on), OFF current (I-off), I-on/I-off ratio, transconductance (g(m)), output conductance (g(d)), cut-off frequency (f(T)), and maximum oscillation frequency (f(max)) for three different gate structures. The junctionless structure, along with SOI technology, enables simplified fabrication, improved electrostatic control, and reduced leakage currents. The simulation results indicate that the TMGJL FinFET exhibits superior performance, achieving the lowest SS (59.99mV/dec), highest I-on/I-off ratio (6.11 & times;10), and enhanced RF figures of merit (f(T) = 121.98GHz, f(max) = 471.16GHz), making it highly suitable for next-generation low-power and high-frequency integrated circuits. Furthermore, the inverter circuit designed using the TMGJL FinFET exhibits high noise margin (N-MH) of 0.500 V and low noise margin (N-ML) of 0.287 V, from the voltage transfer characteristics, indicating robust noise immunity of the simulated TMGJL FinFET. The comparative analysis demonstrates the advantages of multi-material gate engineering, with the TMGJL structure providing the best trade-off among switching behaviour, current drive capability, and RF/Analog performance.
Asymmetric high-voltage pulse track circuits are widely applied in track sections with poor shunting conditions. To address the lack of mature mathematical or circuit models for pulse signals, this study develops a wideband simulation model of the asymmetric high-voltage pulse track circuit. This model allows for exhaustive simulation of various operational scenarios, thereby providing foundational support for simulation-based analysis of pulse track circuits. Firstly, the construct a simulation circuit for the pulse signal source. Next, theoretical calculations determine the impedance parameters of the frequency-dependent components like rails within the track circuit system. A wideband model is then established using vector fitting method combined with circuit synthesis theory. Subsequently, a wideband model for asymmetric high-voltage pulse track circuits is constructed based on the system architecture for simulation analysis. The model's accuracy and robustness are validated through field measurement data and rail surface voltage tests. Finally, transmission characteristics analysis is performed based on this simulation model, including the effects of ballast resistance and transmission distance on the receiving-end voltage and track surface voltage along the line.
